AI能力框架-学生-英文

侖领 Unesco 侖领联合国教科文组织

Al competency framework 人工智能能力框架

for students 针对学生

UNESCO - a global leader in education
联合国教科文组织 - 教育领域的全球领导者

Education is UNESCO’s top priority because it is a basic human right and the foundation for peace and sustainable development. UNESCO is the United Nations’ specialized agency for education, providing global and regional leadership to drive progress, strengthening the resilience and capacity of national systems to serve all learners. UNESCO also leads efforts to respond to contemporary global challenges through transformative learning, with special focus on gender equality and Africa across all actions.
教育是联合国教科文组织的首要任务，因为它是基本人权和和平与可持续发展的基础。联合国教科文组织是联合国的教育专门机构，提供全球和区域领导，以推动进展，加强国家系统的韧性和能力，以服务所有学习者。联合国教科文组织还领导应对当代全球挑战的努力，通过变革性学习，特别关注性别平等和非洲的所有行动。

The Global Education 2030 Agenda
全球教育 2030 议程

UNESCO, as the United Nations’ specialized agency for education, is entrusted to lead and coordinate the Education 2030 Agenda, which is part of a global movement to eradicate poverty through 17 Sustainable Development Goals by 2030. Education, essential to achieve all of these goals, has its own dedicated Goal 4, which aims to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all.” The Education 2030 Framework for Action provides guidance for the implementation of this ambitious goal and commitments.
联合国教科文组织作为联合国的教育专门机构，负责领导和协调教育 2030 议程，这是一个通过 2030 年实现 17 个可持续发展目标来消除贫困的全球运动。教育是实现所有这些目标的关键，拥有自己的专门目标 4，旨在“确保包容和公平的优质教育，并为所有人提供终身学习机会。”教育 2030 行动框架为实施这一雄心勃勃的目标和承诺提供指导。

unesco 教科文组织

Education
2030 教育 2030

United Nations 联合国
Educational, Scientific and Cultural Organization
教育、科学及文化组织

Published in 2024 by the United Nations Educational, Scientific and Cultural Organization
由联合国教育、科学及文化组织于 2024 年出版
7, place de Fontenoy, 75352 Paris 07 SP, France
法国巴黎 07 SP，方特诺伊广场 7 号，邮政编码 75352
© UNESCO 2024 © 联合国教科文组织 2024

ISBN: 978-92-3-100709-5 国际标准书号：978-92-3-100709-5
https://doi.org/10.54675/JKJB9835

This publication is available in Open Access under the Attribution-ShareAlike 3.0 IGO (CC-BY-SA 3.0 IGO) license (http://creativecom-mons.org/licenses/by-sa/3.0/igo/). By using the content of this publication, the users accept to be bound by the terms of use of the UNESCO Open Access Repository (https://www.unesco.org/en/open-access/cc-sa).
本出版物在知识共享署名-相同方式共享 3.0 国际组织（CC-BY-SA 3.0 IGO）许可下以开放获取方式提供（http://creativecom-mons.org/licenses/by-sa/3.0/igo/）。使用本出版物内容的用户接受遵守联合国教科文组织开放获取库的使用条款（https://www.unesco.org/en/open-access/cc-sa）。

Images marked with an asterisk (*) do not fall under the CC-BY-SA license and may not be used or reproduced without the prior permission of the copyright holders.
带有星号（*）的图像不受 CC-BY-SA 许可证的保护，未经版权持有者的事先许可，不得使用或复制。

The designations employed and the presentation of material throughout this publication do not imply the expression of any opinion whatsoever on the part of UNESCO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.
本出版物中所使用的名称和材料的呈现并不意味着联合国教科文组织对任何国家、地区、城市或地区及其当局的法律地位，或其边界或界限的划定表达任何意见。

The ideas and opinions expressed in this publication are those of the authors; they are not necessarily those of UNESCO and do not commit the Organization.
本出版物中表达的观点和意见仅代表作者的观点；它们不一定代表联合国教科文组织的观点，也不对该组织承担任何责任。

Cover credit: Heena Rajput/Shutterstock.com*
封面致谢：Heena Rajput/Shutterstock.com*
Designed and printed by UNESCO
由联合国教科文组织设计和印刷
Printed in France 在法国印刷

SHORTSUMMARY 简短摘要

Preparing students to be responsible and creative citizens in the era of AI
准备学生在人工智能时代成为负责任和富有创造力的公民

Artificial intelligence (AI) is increasingly integral to our lives, necessitating proactive education systems to prepare students to be responsible users and co-creators of AI. Integrating Al learning objectives into official school curricula is crucial for students globally to engage safely and meaningfully with AI.
人工智能（AI）在我们的生活中越来越不可或缺，这需要积极的教育系统来准备学生成为负责任的用户和 AI 的共同创造者。将 AI 学习目标纳入官方学校课程对于全球学生安全且有意义地与 AI 互动至关重要。

The UNESCO AI competency framework for students aims to help educators in this integration, outlining 12 competencies across four dimensions: Human-centred mindset, Ethics of AI, Al techniques and applications, and AI system design. These competencies span three progression levels: Understand, Apply, and Create. The framework details curricular goals and domain-specific pedagogical methodologies.
联合国教科文组织的学生 AI 能力框架旨在帮助教育工作者进行这种整合，概述了四个维度下的 12 项能力：以人为本的思维方式、AI 伦理、AI 技术与应用，以及 AI 系统设计。这些能力跨越三个进阶水平：理解、应用和创造。该框架详细说明了课程目标和特定领域的教学方法。

Grounded in a vision of students as AI co-creators and responsible citizens, the framework emphasizes critical judgement of Al solutions, awareness of citizenship responsibilities in the era of Al, foundational AI knowledge for lifelong learning, and inclusive, sustainable AI design.
该框架以学生作为 AI 共同创造者和负责任公民的愿景为基础，强调对 AI 解决方案的批判性判断、在 AI 时代对公民责任的意识、终身学习的基础 AI 知识，以及包容性和可持续的 AI 设计。

unesco 联合国教科文组织
“Since wars begin in the minds of men and women it is in the minds of men and women that the defences of peace must be constructed”
“战争始于人们的思想，因此和平的防御必须在男女的思想中构建”

IIIII Unesco 联合国教科文组织

Al competency framework 人工智能能力框架

for students 针对学生

The past decade has seen widespread adoption of artificial intelligence (AI) in all areas of human development, with the public release of generative AI tools in November 2022 only accelerating its permeation within social life. The education sector, which is at the heart of the transformation of human societies, has been no exception.
在过去十年中，人工智能（AI）在所有人类发展领域得到了广泛应用，2022 年 11 月生成性 AI 工具的公开发布进一步加速了其在社会生活中的渗透。教育部门作为人类社会转型的核心，也不例外。

This process of rapid technological change brings multiple opportunities but also risks and challenges for students, teachers and society at large. In the era of AI, school students need to be prepared to become active co-creators of Al , as well as future leaders who will shape novel iterations of the technology and define its relationship with society.
这一快速技术变革的过程为学生、教师和整个社会带来了多重机遇，但也带来了风险和挑战。在 AI 时代，学校学生需要准备好成为 AI 的积极共同创造者，以及未来将塑造技术新迭代并定义其与社会关系的领导者。

This is exactly the ambition of UNESCO’s AI competency framework for students - the first ever global framework of its kind. It aims to support the development of core competencies for students to become responsible and creative citizens, equipped to thrive in the Al era. This will help students acquire the values, knowledge and skills necessary to examine and understand AI critically from a holistic perspective, including its ethical, social and technical dimensions.
这正是联合国教科文组织为学生制定的 AI 能力框架的目标——这是首个全球范围内的此类框架。它旨在支持学生核心能力的发展，使他们成为负责任和富有创造力的公民，具备在 AI 时代蓬勃发展的能力。这将帮助学生从整体的角度获取必要的价值观、知识和技能，以批判性地审视和理解 AI，包括其伦理、社会和技术维度。

The new framework embodies UNESCO’s mandate by anchoring its vision of AI and education in principles of human rights, inclusion and equity. This approach seeks to ensure that AI supports the development of human capabilities, protects human dignity and agency, and promotes justice and sustainability.
新框架体现了联合国教科文组织的使命，通过将其对人工智能和教育的愿景建立在尊重人权、包容性和公平的原则上。这种方法旨在确保人工智能支持人类能力的发展，保护人类尊严和自主权，并促进正义和可持续性。

The publication builds on UNESCO’s previous work in the field, such as the ICT competency framework for teachers, Al and education: Guidance for policy-makers, and the more recent Guidance for generative Al in education and research. It reflects the contributions of a wide range of stakeholders, drawing on UNESCO Member States’insights on developing and implementing Al school curricula, the expertise of an international working group, three international consultation meetings, and multiple rounds of online consultations.
该出版物建立在联合国教科文组织在该领域的先前工作基础上，例如教师的信息通信技术能力框架、人工智能与教育：政策制定者指南，以及最近的教育和研究中的生成性人工智能指南。它反映了广泛利益相关者的贡献，借鉴了联合国教科文组织成员国在开发和实施人工智能学校课程方面的见解、一个国际工作组的专业知识、三次国际咨询会议以及多轮在线咨询。

The AI competency framework for students has been developed hand in hand with a competency framework for teachers. It is my hope that these two frameworks will empower students and teachers to shape the digital futures we want.
学生的人工智能能力框架是与教师的能力框架共同开发的。我希望这两个框架能够赋予学生和教师塑造我们想要的数字未来的能力。

In a world characterized by rising complexity and uncertainty, it is our collective responsibility to ensure that education remains the central space for transformation of our shared futures.
在一个复杂性和不确定性日益增加的世界中，我们有共同的责任确保教育仍然是我们共同未来转型的核心空间。

Stefania Giannini 斯特法尼亚·贾尼尼

UNESCO Assistant Director-General for Education
联合国教科文组织教育助理总干事

Acknowledgements 致谢

Under the leadership of Stefania Giannini, Assistant Director-General for Education, and the guidance of Sobhi Tawil, Director of the Future of Learning and Innovation Division at UNESCO, the drafting of the publication was led by Fengchun Miao, Chief of Unit for Technology and AI in Education.
在斯特凡尼亚·贾尼尼（Stefania Giannini）担任助理总干事的领导下，在联合国教科文组织未来学习与创新部主任索比·塔维尔（Sobhi Tawil）的指导下，出版物的起草工作由教育技术与人工智能单位负责人冯春苗（Fengchun Miao）主导。

The framework was drafted by Fengchun Miao, Chief of Unit for Technology and AI in Education at UNESCO and Kelly Shiohira, Director of the Global Science of Learning Education Network. The development of the framework also benefited from the contributions of a group of international experts, including Natalie Lao, Executive Director of the App Inventor Foundation, and Lidija Kralj, Education Analyst at EduConLK. UNESCO is grateful to them for contributing their expertise.
该框架由联合国教科文组织教育技术与人工智能单位负责人冯春苗（Fengchun Miao）和全球学习科学教育网络主任凯莉·希奥希拉（Kelly Shiohira）共同起草。该框架的开发还得益于一组国际专家的贡献，包括应用程序发明基金会执行董事娜塔莉·劳（Natalie Lao）和 EduConLK 教育分析师莉迪亚·克拉尔（Lidija Kralj）。联合国教科文组织对他们的专业贡献表示感谢。

Special appreciation is also extended to the following experts for peer-reviewing the publication: Kate Arthur, Co-founder and partner at Comz; Ke Gong, President of the World Federation of Engineering Organizations (WFEO); Kaśka Porayska-Pomsta, Professor of AI in Education at University College London; Nisha Talagala, Co-Founder and CEO of AIClub and AIClubPro; Monique Brodeur, Hugo Couture, Sophie Gosselin, Yves Munn and Benoit Petit from the Conseil supérieur de l’éducation du Québec; and Luc Bégin, Nicolas Bernier and Guillaume Pelletier from the Commission de léthique en science et en technologie.
特别感谢以下专家对本出版物的同行评审：Kate Arthur，Comz 的联合创始人和合伙人；Ke Gong，世界工程组织联合会（WFEO）主席；Kaśka Porayska-Pomsta，伦敦大学学院教育领域的人工智能教授；Nisha Talagala，AIClub 和 AIClubPro 的联合创始人兼首席执行官；来自魁北克教育委员会的 Monique Brodeur、Hugo Couture、Sophie Gosselin、Yves Munn 和 Benoit Petit；以及来自科学与技术伦理委员会的 Luc Bégin、Nicolas Bernier 和 Guillaume Pelletier。

Thanks also go to the following UNESCO colleagues for contributing to the peer-review process: Andrea Detmer at the Executive Office of the Culture Sector; Amal Kasry, Chief of the Basic Sciences, Research, Innovation and Engineering Section; Karalyn Monteil, Head of the Programmes and Stakeholder Outreach Unit at the Culture Sector; Renato Opertti, Senior Education Expert at the International Bureau of Education; Arianna Valentini at the International Institute for Higher Education in Latin America and the Caribbean; Soichiro Yasukawa, Chief of the Disaster Risk Reduction Unit in the Science Sector; Martiale Kana Zebaze, Senior Programme Specialist for Science, Technology and Innovation at the UNESCO Harare Office; as well as Jaco Du Toit, Chief, and Zeynep Varoglu, Programme Specialist, at the Section for Universal Access to Information and Digital Inclusion in the Communication and Information Sector.
还要感谢以下联合国教科文组织的同事们为同行评审过程做出的贡献：文化部门执行办公室的安德烈亚·德特默；基础科学、研究、创新与工程部门的首席阿玛尔·卡斯里；文化部门项目和利益相关者外联单位的负责人卡拉琳·蒙泰尔；国际教育局的高级教育专家雷纳托·奥佩尔蒂；拉丁美洲和加勒比地区高等教育国际研究所的阿里安娜·瓦伦蒂尼；科学部门灾害风险减少单位的首席八木宗一郎；联合国教科文组织哈拉雷办事处科学、技术与创新高级项目专家马尔蒂尔·卡纳·泽巴泽；以及信息与数字包容性部门普遍获取信息和数字包容性部分的首席雅各·杜托和项目专家泽伊内普·瓦罗格鲁。

Special thanks go to Luisa Ferrara at the Unit for Technology and AI in Education within the Future of Learning and Innovation Division, for managing expert inputs, and to Glen Hertelendy from the same Unit for coordinating the production of the publication.
特别感谢未来学习与创新部技术与教育人工智能单位的路易莎·费拉拉，感谢她管理专家意见，以及同一单位的格伦·赫特伦迪，感谢他协调出版物的制作。

Additionally, UNESCO is grateful to Jenny Webster for copy-editing and proofreading the text.
此外，联合国教科文组织感谢珍妮·韦伯斯特对文本的编辑和校对。
Finally, UNESCO would like to thank the Tomorrow Advancing Life (TAL) Education Group of China for generously supporting this publication project and, more broadly, for promoting the potential of artificial intelligence for the future of education.
最后，联合国教科文组织感谢中国的明日教育集团（TAL）慷慨支持本出版项目，并更广泛地促进人工智能在教育未来中的潜力。

Table of contents 目录

Foreword … 6  前言 … 6
Acknowledgements … 7  致谢 … 7
List of tables and boxes … 10
表格和框的列表 … 10
List of acronyms and abbreviations … 11
缩略语和简称列表 … 11
Chapter 1: Introduction … 12
第 1 章：引言 … 12
1.1 Why an AI competency framework for students? … 12
1.1 为什么需要学生的 AI 能力框架？ … 12
1.2 Purpose and target audience … 13
1.2 目的和目标受众 … 13
Chapter 2: Key principles … 14
第 2 章：关键原则 … 14
2.1 Fostering a critical approach to AI … 14
2.1 培养对 AI 的批判性思维 … 14
2.2 Prioritizing human-centred interaction with AI … 15
2.2 优先考虑以人为本的人工智能互动 … 15
2.3 Encouraging environmentally sustainable AI … 15
2.3 鼓励环境可持续的人工智能 … 15
2.4 Promoting inclusivity in AI competency development … 16
2.4 促进人工智能能力发展的包容性 … 16
2.5 Building core AI competencies for lifelong learning … 17
2.5 为终身学习构建核心人工智能能力 … 17
Chapter 3: Structure of the AI competency framework for students … 18
第三章：学生 AI 能力框架的结构 … 18
3.1 The framework … 18
3.1 框架 … 18
3.2 Progression levels … 20
3.2 进阶水平 … 20
Level 1: Understand … 20
一级：理解 … 20
Level 2: Apply … 20
第二级：应用 … 20
Level 3: Create … 21
第三级：创造 … 21
3.3 Aspects … 21
3.3 方面 … 21
Human-centred mindset … 22
以人为本的思维方式 … 22
Ethics of AI … 23
人工智能的伦理 … 23
Al techniques and applications … 24
人工智能技术与应用 … 24
Al system design. … 25
人工智能系统设计 … 25
Chapter 4: Specifications of AI competencies for students … 27
第四章：学生人工智能能力规范 … 27
4.1 Level 1: Understand … 27
4.1 级别 1：理解 … 27
4.2 Level 2: Apply … 37
4.2 级别 2：应用 … 37
4.3 Level 3: Create … 45
4.3 级别 3：创造 … 45
Chapter 5: Applying the framework … 53
第五章：应用框架 … 53
5.1 Aligning Al competencies as the foundation for national Al strategies … 53
5.1 将人工智能能力作为国家人工智能战略的基础 … 53
5.2 Building interdisciplinary core and cluster AI curricula for Al competency … 56
5.2 为人工智能能力构建跨学科的核心和集群课程 … 56
5.3 Framing future-proofing and locally feasible Al domains as carriers of the curriculum … 58
5.3 将未来可持续和本地可行的人工智能领域框架作为课程的载体 … 58
5.4 Tailoring age-appropriate spiral curricular sequences … 59
5.4 定制适合年龄的螺旋课程序列 … 59
5.5 Building enabling learning environments for AI curricula … 61
5.5 为人工智能课程构建支持性学习环境……61
5.6 Promoting the professionalization of AI teachers and streamlining their support … 62
5.6 促进人工智能教师的专业化及其支持的简化……62
5.7 Guiding the cohort-based design and organization of pedagogical activities … 64
5.7 指导基于 cohort 的教学活动设计和组织……64
5.8 Constructing competency-based assessments on the progression of key Al aspects … 69
5.8 构建基于能力的评估，评估关键人工智能方面的进展……69
Conclusion … 78  结论 … 78
References … 79  参考文献 … 79
Endnotes … 80  尾注 … 80

List of tables 表格列表

Table 1. Al competency framework for students … 19
表 1. 学生的人工智能能力框架 … 19
Table 2. Competency blocks for level 1: Understand … 29
表 2. 级别 1 的能力模块：理解 … 29
Table 3. Competency blocks for level 2: Apply … 37
表 3. 级别 2 的能力模块：应用 … 37
Table 4. Competency blocks for level 3: Create … 45
表 4. 级别 3 的能力模块：创造 … 45
Table 5. Examples of assessment tasks … 73
表 5. 评估任务示例 … 73
List of boxes 框列表
Box 1: Recommendation on the Ethics of Artificial Intelligence … 53
框 1: 关于人工智能伦理的建议 … 53
Box 2: Supporting human resource development: The Republic of Korea’s National Strategy for Artificial Intelligence … 55
框 2: 支持人力资源发展的国家战略: 韩国的人工智能国家战略 … 55
Box 3: The United Arab Emirates’ interdisciplinary approach to K-12 AI curricula … 57
案例 3：阿拉伯联合酋长国对 K-12 人工智能课程的跨学科方法……57
Box 4: The spiral curricular sequence of ‘Day of AI’ courses … 60
案例 4：“人工智能日”课程的螺旋课程序列……60
Box 5: Typical enabling learning environment set up by governments’ AI curricula … 61
案例 5：政府人工智能课程设立的典型支持学习环境……61
Box 6: An Al competency framework for Al subject teachers in China … 63
案例 6：中国人工智能学科教师的人工智能能力框架……63
Box 7: Pedagogical methodologies in the MIT curriculum on the ethics of AI for middle school students … 66
框 7：麻省理工学院课程中关于中学生人工智能伦理的教学方法……66

List of acronyms and abbreviations
缩略语和缩写列表

AGI	Artificial general intelligence 人工通用智能
AI	Artificial intelligence 人工智能
AI CFS 人工智能能力框架	Al competency framework for students 学生的人工智能能力框架
CCDI	Computing, Creative Design and Innovation 计算、创意设计与创新
CG	Curricular goal 课程目标
GAN	Generative adversarial networks 生成对抗网络
K-12	Kindergarten through 12th grade 幼儿园至 12 年级
ICT	Information and communication technology 信息和通信技术
IEA	International Energy Agency 国际能源署
IGO	Intergovernmental organization 政府间组织
ITU	International Telecommunication Union 国际电信联盟
MIT	Massachusetts Institute of Technology 麻省理工学院
NGO	Non-governmental organization 非政府组织
STEAM	Science, technology, engineering, arts and mathematics 科学、技术、工程、艺术和数学
STEM	Science, technology, engineering and mathematics 科学、技术、工程和数学
TVET	Technical and vocational education and training 技术和职业教育与培训
UNESCO	United Nations Educational, Scientific and Cultural Organization 联合国教育、科学及文化组织

Chapter 1: Introduction 第 1 章：引言

1.1 Why an Al competency framework for students?
1.1 为什么需要学生的人工智能能力框架？

The rapid iterations and proliferation of artificial intelligence (AI) across all aspects of life and all sectors are posing new challenges regarding the nature of machine intelligence, the collection and use of personal data, the role of humans and machines in decisionmaking, and the impact of AI on social and environmental sustainability. It is essential that education systems prepare students not only with the knowledge and skills to use AI, but also with insight into the potential impact of technology on societies and the environment at large. Given the transformative potential of AI for human societies, it is crucial to equip students with the values, knowledge and skills needed for the effective use and active co-creation of AI.
人工智能（AI）在生活和各个行业的快速迭代和普及带来了关于机器智能的本质、个人数据的收集和使用、人类与机器在决策中的角色，以及人工智能对社会和环境可持续性的影响等新挑战。教育系统必须不仅为学生提供使用人工智能的知识和技能，还要让他们了解技术对社会和环境的潜在影响。鉴于人工智能对人类社会的变革潜力，培养学生具备有效使用和积极共同创造人工智能所需的价值观、知识和技能至关重要。

Education, as a public sector, cannot be reduced to a testing ground for the passive adoption of AI. The role of the education sector is not only to prepare students to adapt to a society that is increasingly being transformed by Al technologies; it also has a key role to play in empowering young people to help co-create sustainable futures by rebalancing our relationships, not only with others, but also with technology and the environment. By defining the core competencies that students are likely to require as we move deeper into the Al era, the ultimate aim of this AI competency framework for students (AI CFS) is to help shape responsible and creative citizens that can co-create these desirable futures.
教育作为公共部门，不能仅仅被视为被动接受人工智能的试验场。教育部门的角色不仅是为学生准备适应一个日益被人工智能技术转型的社会；它还在赋权年轻人共同创造可持续未来方面发挥着关键作用，通过重新平衡我们与他人、技术和环境的关系。通过定义学生在深入进入人工智能时代时可能需要的核心能力，这个学生人工智能能力框架（AI CFS）的最终目标是帮助培养能够共同创造这些理想未来的负责任和富有创造力的公民。

Governments acknowledged the urgent need to develop Al literacy and more advanced AI competencies as early as 2019, when they adopted the UNESCO Beijing Consensus on AI and Education. Indeed, the Beijing Consensus underlined the need to equip people with AI literacy across all layers of society. However, according to a recent survey conducted across 190 countries, only some 15 countries were found to be developing or implementing AI curricula in school education (UNESCO, 2022b). The survey also found that there was wide variation in how countries defined AI literacy, skills and competency. The results of the survey therefore underscored the urgency of developing a harmonized approach to integrating Al-related teaching and learning content in school curricula.
各国政府早在 2019 年就承认了发展人工智能素养和更高级人工智能能力的紧迫需求，当时他们通过了《联合国教科文组织北京共识：人工智能与教育》。实际上，北京共识强调了在社会各个层面为人们提供人工智能素养的必要性。然而，根据最近在 190 个国家进行的一项调查，仅有约 15 个国家被发现正在开发或实施学校教育中的人工智能课程（联合国教科文组织，2022b）。调查还发现，各国对人工智能素养、技能和能力的定义存在很大差异。因此，调查结果强调了在学校课程中整合与人工智能相关的教学和学习内容的统一方法的紧迫性。

Far too often, the definition of Al competencies for students is influenced by training designed and/or provided by private companies, which tends to focus on technical skills to operate profit-driven Al platforms. Such approaches seldom engage with the broader critical issues of the implications of AI for learning and citizenship, more broadly. There is currently a void in too many education systems when it comes to public-approved frameworks for introducing AI-related content and methods to educational curricula. One of the challenges that public education systems are facing in filling this void is the lack of an international reference framework on AI competencies for students. Such an international reference framework can inform the design of national/local AI competency frameworks for students that
学生的人工智能能力定义往往受到私人公司设计和/或提供的培训的影响，这些培训往往侧重于操作以利润为驱动的人工智能平台的技术技能。这种方法很少涉及人工智能对学习和公民身份的更广泛影响的关键问题。目前，许多教育系统在引入与人工智能相关的内容和方法到教育课程方面缺乏公众认可的框架。公共教育系统在填补这一空白时面临的挑战之一是缺乏国际参考框架来定义学生的人工智能能力。这样的国际参考框架可以为国家/地方学生人工智能能力框架的设计提供信息。
promote a critical and ethical approach to Al tools, as well as develop the foundational knowledge required for their effective and meaningful use in education. The aim of this AI CFS is to fill this void.
促进对人工智能工具的批判性和伦理性的方法，并发展在教育中有效和有意义使用这些工具所需的基础知识。这个人工智能能力框架的目标是填补这一空白。

Al technology is a rapidly moving target. It is therefore critical to ensure that all students have a core set of knowledge, skills and values for interacting ethically and effectively with Al in the present. This foundation can enable students to utilize future iterations of Al technology in an appropriate and humancentred manner.
人工智能技术是一个快速变化的目标。因此，确保所有学生具备一套核心知识、技能和价值观，以便在当前与人工智能进行伦理和有效的互动至关重要。这一基础可以使学生能够以适当和以人为本的方式利用未来的人工智能技术迭代。

The AI CFS supports educational authorities to respond to these needs by defining a core set of competencies for students that fall under four aspects: Human-centred mindset; Ethics of AI; AI techniques and applications; and AI system design. These four aspects are articulated at three levels of progression or mastery (understanding, application and creation), resulting in a total of twelve competency blocks. For each of these competency blocks, the AI CFS proposes detailed specifications on relevant pedagogical methodologies and strategies for the planning and provision of AI-related curricular content.
人工智能能力框架支持教育当局应对这些需求，通过定义一套核心能力，涵盖四个方面：以人为本的思维方式；人工智能的伦理；人工智能技术和应用；以及人工智能系统设计。这四个方面在三个进阶或掌握水平（理解、应用和创造）中进行了阐述，形成了总共十二个能力模块。对于每个能力模块，人工智能能力框架提出了关于相关教学方法和策略的详细规范，以便规划和提供与人工智能相关的课程内容。

1.2 Purpose and target audience
1.2 目的和目标受众

The AI CFS aims to serve as a guide for public education systems to build the competencies required of all students and citizens for the effective implementation of national AI strategies and the building of inclusive, just and sustainable futures in this new technological era.
AI 能力框架旨在为公共教育系统提供指导，以建立所有学生和公民在有效实施国家人工智能战略以及在这个新技术时代构建包容、公正和可持续未来所需的能力。

More specifically, the AI CFS: (1) provides a global reference framework on the core set of AI competencies for students to inform the design of national or institutional AI competency frameworks; (2) specifies typical attitudinal and behavioural performance relating to the key aspects of AI competencies at different levels of mastery to help design Al-related curricular content for school students; and (3) recommends an open-ended roadmap to help plan the learning sequence of Al curricula across grade levels.
更具体地说，AI 能力框架：(1) 提供了一个全球参考框架，涵盖学生所需的核心人工智能能力，以指导国家或机构的人工智能能力框架设计；(2) 规定了与不同掌握水平的人工智能能力关键方面相关的典型态度和行为表现，以帮助设计与人工智能相关的学校课程内容；(3) 推荐了一条开放式路线图，以帮助规划各年级人工智能课程的学习顺序。

As a global reference framework, the AI CFS is to be tailored to the diverse readiness levels of local education systems in terms of curricula, the enabling learning environment for teaching AI, preparedness of teachers, and the prior knowledge and capacities of specific groups of students.
作为一个全球参考框架，AI 能力框架需要根据地方教育系统在课程、教学人工智能的支持性学习环境、教师的准备情况以及特定学生群体的先前知识和能力的多样化准备水平进行调整。

The AI CFS is aimed principally at policymakers, curriculum developers, providers of education programmes on Al for students, school leaders, teachers and educational experts.
AI 能力框架主要面向政策制定者、课程开发者、为学生提供人工智能教育项目的机构、学校领导、教师和教育专家。

Chapter 2: Key principles
第 2 章：关键原则

2.1 Fostering a critical approach to AI
2.1 培养对人工智能的批判性思维

Critical thinking is a fundamental skill that students need to meaningfully engage with Al as learners, users and creators. Students also have the responsibility to determine what types of AI should be developed and how they should be used to drive human societies towards inclusive, environmentally sound, shared futures. School students need to be supported to become active co-creators of AI, as well as potential leaders who will define further iterations of AI and its interactions with human society for present and future generations. To support this vision, the AI CFS is designed to foster a critical approach to Al by engaging students with fundamental questions, such as: is Al poised to help solve real-world challenges faced by humans, or does it pose insurmountable threats to humans? Are adverse impacts on climate of training and using AI disproportionate to its anticipated benefits? What social, economic, political and demographic impacts of the use of AI should be carefully reviewed?
批判性思维是学生作为学习者、用户和创造者与人工智能进行有意义互动所需的基本技能。学生还有责任确定应该开发哪些类型的人工智能，以及如何使用它们来推动人类社会朝着包容、环境友好和共享的未来发展。学校学生需要得到支持，成为人工智能的积极共同创造者，以及潜在的领导者，他们将为现在和未来的世代定义人工智能的进一步迭代及其与人类社会的互动。为了支持这一愿景，人工智能能力框架旨在通过让学生参与基本问题来培养对人工智能的批判性思维，例如：人工智能是否准备好帮助解决人类面临的现实挑战，还是对人类构成不可逾越的威胁？训练和使用人工智能对气候的负面影响是否与其预期的好处不成比例？使用人工智能的社会、经济、政治和人口影响应该仔细审查哪些方面？

The Al-driven transformation across development sectors has profound implications for human agency, human interactions, social equity, economic inclusiveness, and environmental sustainability. Thus, in the first place, school students are expected to be conscious and knowledgeable of the advantages and limitations of existing affordances of AI. The pre-condition for responsible use consists in students’ abilities to detect the trustworthiness and proportionality of AI tools. The AI CFS aims to prepare
AI 驱动的各个发展领域的转型对人类自主性、人际互动、社会公平、经济包容性和环境可持续性具有深远的影响。因此，首先，学校学生应当意识到并了解现有 AI 工具的优势和局限性。负责任使用的前提是学生能够识别 AI 工具的可信度和适度性。AI CFS 旨在为学生做好准备。
students with the values, knowledge and skills necessary to critically examine the proportionality of Al from an ethical perspective. This includes examining and understanding its impact on human agency, social inclusion and equity, institutional and individual security, cultural and linguistic diversity, the construction and expression of plural opinions, as well as on the environment and on ecosystems. Students are expected to move beyond the misconception that Al is a solution to everything. Rather, they are to become conscious decision-makers on when AI systems and applications should, or should not, be used; what problems they may or may not solve; and when and how Al should be designed and used as one part of a wider solution. The AI CFS aims to nurture students’ aspirations to apply and design Al tools to serve meaningful specific purposes or to address real-world challenges and promote sustainable development.
学生具备必要的价值观、知识和技能，以从伦理角度批判性地审视人工智能的比例性。这包括审视和理解其对人类自主性、社会包容性和公平性、机构和个人安全、文化和语言多样性、不同意见的构建和表达，以及对环境和生态系统的影响。学生应超越将人工智能视为万灵药的误解。相反，他们应成为有意识的决策者，判断何时应使用或不应使用人工智能系统和应用；它们可能解决或无法解决哪些问题；以及何时和如何将人工智能设计和使用作为更广泛解决方案的一部分。人工智能能力框架旨在培养学生应用和设计人工智能工具以服务于有意义的特定目的或应对现实世界挑战并促进可持续发展的愿望。

Societies are moving into the era of Al at different paces, but students everywhere are, or will be, citizens in contexts characterized by widespread Al integration. They will not only have to comply with legal regulations and ethical principles, but, as citizens, they will also have to contribute to the adaptation of AI standards and regulations. The framework therefore highlights the importance of supporting students to become responsible and ethical users of, as well as contributors to, AI. It engages students to reflect on key controversies surrounding AI, internalize ethical principles, and become familiar with related regulations.
社会正在以不同的速度进入人工智能时代，但世界各地的学生都将是或将成为在广泛的人工智能整合背景下的公民。他们不仅需要遵守法律法规和伦理原则，作为公民，他们还需要为人工智能标准和法规的适应做出贡献。因此，该框架强调支持学生成为负责任和道德的人工智能用户以及贡献者的重要性。它鼓励学生反思围绕人工智能的关键争议，内化伦理原则，并熟悉相关法规。

The AI CFS sets out a forward-looking vision of the type of citizenship required by societies increasingly shaped by Al. It proposes that students be challenged and enabled to make meaningful use of AI for self-actualization; to evaluate its social, economic and environmental impacts; and to contribute, at a level appropriate for their age or grade, to the development of AI regulations, helping to shape our relationship with technology in society at large.
人工智能能力框架提出了一个前瞻性的愿景，描绘了在日益受到人工智能影响的社会中所需的公民身份。它建议学生应被挑战并能够有意义地利用人工智能实现自我价值；评估其社会、经济和环境影响；并在适合他们年龄或年级的水平上，为人工智能法规的发展做出贡献，帮助塑造我们与社会中技术的关系。

2.2 Prioritizing human-centred interaction with AI
2.2 优先考虑以人为本的人工智能交互

In the era of Al, interaction between humans and AI systems and applications will become an essential constituent element of public service, production and commerce, social practice, learning, and daily life. Establishing the competencies needed to understand and ensure human-centred interaction with Al in these domains is a priority for the AI CFS.
在人工智能时代，人类与人工智能系统和应用之间的互动将成为公共服务、生产和商业、社会实践、学习和日常生活的重要组成部分。建立理解和确保人本互动所需的能力在这些领域是人工智能能力框架的优先事项。

UNESCO’s human-centric approach advocates that the design and use of Al should serve the development of human capabilities, protect human dignity and agency, and promote justice and sustainability throughout the entire Al life cycle and all possible human-Al interaction loops. Such an approach must be guided by human rights principles and respect for the linguistic and cultural diversity that defines the knowledge commons. A humancentred approach also requires that AI be used in ways that ensure transparency and explainability, as well as human control and accountability.
联合国教科文组织的人本中心方法主张，人工智能的设计和使用应服务于人类能力的发展，保护人类尊严和自主权，并在整个人工智能生命周期及所有可能的人机互动循环中促进正义和可持续性。这种方法必须以人权原则为指导，并尊重定义知识共享的语言和文化多样性。人本中心的方法还要求人工智能的使用方式确保透明性和可解释性，以及人类的控制和问责。

As AI becomes increasingly sophisticated and more widely used, a key danger is its potential to undermine human agency and
随着人工智能变得越来越复杂和广泛使用，一个关键的危险是它可能会削弱人类的自主权和
compromise the development of human intellectual skills. While AI can be used to challenge and extend human thought, it should not be allowed to usurp or replace critical thinking. The protection and enhancement of human agency should, therefore, always be a core principle in the design of AI curricula and education programmes. The AI CFS aims to support students to understand the types of data that AI may collect from them, the methods with which the data may be used to train Al models, and the impact that the data cycle may have on their privacy and wider lives. It seeks to stimulate students’ intrinsic motivation to grow and learn as individuals and to reinforce their autonomy in contexts in which sophisticated AI systems are increasingly being integrated. Critical Al competencies, as proposed in this framework, can also guide students to understand the unique value of social interaction and of the creative works produced by humans that should not be replaced by AI outputs. By developing competencies for human-centred engagement with AI, the framework aims to prevent students from becoming addicted to or dependent on Al , and to foster behaviours that maintain human accountability for highstakes decisions.
妨碍人类智力技能的发展。虽然人工智能可以用来挑战和扩展人类思维，但不应允许其篡夺或取代批判性思维。因此，保护和增强人类自主性应始终是人工智能课程和教育项目设计中的核心原则。人工智能能力框架旨在支持学生理解人工智能可能从他们那里收集的数据类型、这些数据可能用于训练人工智能模型的方法，以及数据循环可能对他们的隐私和更广泛生活产生的影响。它旨在激发学生作为个体成长和学习的内在动机，并在复杂的人工智能系统日益被整合的背景下增强他们的自主性。该框架中提出的关键人工智能能力也可以指导学生理解社会互动的独特价值以及人类创作的作品，这些作品不应被人工智能输出所取代。通过发展以人为本的人工智能互动能力，该框架旨在防止学生对人工智能上瘾或依赖，并促进保持人类对重大决策的责任感的行为。

2.3 Encouraging environmentally sustainable AI
2.3 鼓励环境可持续的人工智能

As co-creators and potential leaders of the next generations of AI technology, students need to have a critical understanding of the adverse environmental impact of profitdriven approaches to the design, training and deployment of AI models. Education systems bear the responsibility of ensuring that students understand carbon emissions,
作为下一代人工智能技术的共同创造者和潜在领导者，学生需要对以利润驱动的人工智能模型设计、训练和部署方式对环境造成的不利影响有批判性的理解。教育系统有责任确保学生理解碳排放，
analyse the root causes of climate change, and act judiciously to protect the climate and the environment.
分析气候变化的根本原因，并明智地采取行动以保护气候和环境。

In the race to produce increasingly powerful Al models, environmental sustainability is often considered to be of secondary importance. In some instances, it has even been intentionally obscured by claims that AI holds the promise of solving climate change. As global leaders and policy-makers work to consider regulations around the consumption of energy and the protection of the environment, it is imperative that students understand how the training of AI models is contributing to the destruction of the natural environment. Learning about AI should empower them to urgently explore more climate-friendly approaches to the design, training and use of Al models. The Al CFS attends to this by guiding students to design and implement project-based learning activities on the environmental impacts of Al use and training, prompting students to investigate potential solutions to mitigate these impacts.
在生产越来越强大的人工智能模型的竞赛中，环境可持续性常常被视为次要重要性。在某些情况下，它甚至被故意掩盖，声称人工智能有望解决气候变化。随着全球领导者和政策制定者努力考虑关于能源消费和环境保护的法规，学生们必须理解人工智能模型的训练如何导致自然环境的破坏。学习人工智能应该使他们能够紧急探索更具气候友好的方法来设计、训练和使用人工智能模型。人工智能能力框架通过指导学生设计和实施关于人工智能使用和训练对环境影响的项目式学习活动，促使学生调查减轻这些影响的潜在解决方案。

2.4 Promoting inclusivity in AI competency development
2.4 促进人工智能能力发展的包容性

Access to Al and Al competencies represent the two sides of citizens’ basic rights in today’s world. All students should have inclusive access to the environments required for learning about Al at the basic level, and they should be supported to learn how to embed the principle of inclusivity into the design of Al and be prepared to contribute to an inclusive AI society.
访问人工智能和人工智能能力代表了当今世界公民基本权利的两个方面。所有学生都应该能够获得学习人工智能所需的环境，并得到支持，以学习如何将包容性原则融入人工智能的设计中，并为贡献于一个包容的人工智能社会做好准备。

When defining AI competencies, school students should be provided with opportunities to understand and apply the principle of inclusivity across the AI
在定义人工智能能力时，学校学生应获得机会理解和应用包容性原则贯穿于人工智能的整个生命周期。
life cycle. This covers the selection of representative data, the choice of biasagnostic algorithms and anti-discrimination training methods, the design of accessible functionalities, testing for the inclusiveness of AI outputs, and impact assessment of the use of AI on social inclusion. With regard to AI system design, students can deepen their understanding and application skills to assess the needs of users with different abilities as well as those from diverse linguistic and cultural backgrounds.
这包括选择具有代表性的数据、选择无偏算法和反歧视培训方法、设计可访问的功能、测试人工智能输出的包容性以及评估人工智能对社会包容性的影响。关于人工智能系统设计，学生可以加深他们的理解和应用技能，以评估不同能力用户以及来自不同语言和文化背景的用户的需求。

In selecting the models and categories of technologies as vectors of Al-related teaching and learning, care is needed to avoid favouring certain demographics over others. When recommending specific Al tools for educational purposes, rigorous public validation mechanisms must be applied to avoid algorithms with bias(es) related to gender, ability, socio-economic status, language, ethnicity and/or culture. Al tools that are designed to support individuals with disabilities and promote linguistic and cultural diversity should be given priority. Where such validation mechanisms are unavailable, the recommendation of specific Al tools for use at scale should be avoided.
在选择作为人工智能相关教学和学习载体的模型和技术类别时，需要谨慎，以避免偏向某些人口统计群体。当推荐特定的人工智能工具用于教育目的时，必须应用严格的公共验证机制，以避免与性别、能力、社会经济地位、语言、种族和/或文化相关的偏见算法。旨在支持残疾人士并促进语言和文化多样性的人工智能工具应优先考虑。在缺乏此类验证机制的情况下，应避免推荐特定的人工智能工具进行大规模使用。

Turning to delivery of the curriculum, specific measures can be outlined to provide basic enabling conditions for the implementation of the AI CFS-based curriculum. While AI frameworks or educational programmes should be designed to be applicable to all students, including those who live in low-tech settings, engagement with AI without access to the internet and AI tools will limit the scope and mastery level of Al competencies. Governments should commit to promoting inclusive access to basic internet connectivity, updated digital devices, open-source or affordable AI
在课程交付方面，可以概述具体措施，以提供实施基于 AI CFS 的课程的基本支持条件。虽然 AI 框架或教育项目应设计为适用于所有学生，包括那些生活在低技术环境中的学生，但在没有互联网和 AI 工具的情况下参与 AI 将限制 AI 能力的范围和掌握水平。各国政府应致力于促进对基本互联网连接、更新的数字设备、开源或可负担得起的 AI 的包容性访问。
programmes and software, and essential AI devices, with the support of academia or the private sector, where appropriate. Once again, these efforts must pay particular attention to students who have disabilities and/or are from linguistic or cultural minority groups.
程序和软件，以及必要的人工智能设备，在学术界或私营部门的支持下（如适用）。再次强调，这些努力必须特别关注有残疾的学生和/或来自语言或文化少数群体的学生。

2.5 Building core AI competencies for lifelong learning
2.5 建立终身学习的核心人工智能能力

Al-related teaching and learning should serve to build core AI competencies that allow students to accommodate new knowledge, as well as adapt to solving problems in new contexts with novel AI technologies. First and foremost, these core competencies must include values associated with an ethical and humancentred mindset. Students need guidance to progressively deepen their understanding of particular human rights - such as rights to equality, non-discrimination, privacy and plural expression - as well as their implications for varying forms of human-AI interaction. The competencies also reflect
与人工智能相关的教学和学习应致力于建立核心人工智能能力，使学生能够适应新知识，并能够在新环境中利用新颖的人工智能技术解决问题。首先，这些核心能力必须包括与伦理和以人为本的思维方式相关的价值观。学生需要指导，以逐步加深对特定人权的理解，例如平等权、非歧视权、隐私权和多元表达权，以及这些权利对人类与人工智能互动的不同形式的影响。这些能力还反映了
the need to understand controversies surrounding Al and the key ethical principles that guide regulation, as well as foster practical skills to combat bias, protect privacy, promote transparency and accountability, and adopt an ethics-bydesign approach to the co-creation of AI.
理解围绕人工智能的争议和指导监管的关键伦理原则的必要性，同时培养实用技能以对抗偏见、保护隐私、促进透明度和问责制，并采用设计伦理的方法共同创造人工智能。

The core competencies are brand-agnostic and product-agnostic, ensuring that students can appropriately engage with a range of tools, as well as with future iterations of Al technologies. It enables them to develop an age-appropriate and progressively deeper understanding of AI data, algorithms, models and system design. Students must be supported to construct this understanding by connecting AI concepts with real-world challenges to develop critical problemsolving skills. Students should be further encouraged to exploit their creativity in an effort to optimize existing Al models or co-create more meaningful Al. These core competencies constitute the foundation for further learning and more specialized use of Al in further education, work and life.
核心能力是品牌无关和产品无关的，确保学生能够适当地使用各种工具，以及未来的人工智能技术的迭代。这使他们能够发展出适合年龄的、逐步深入的对人工智能数据、算法、模型和系统设计的理解。必须支持学生通过将人工智能概念与现实世界挑战相连接来构建这种理解，以培养批判性的问题解决能力。还应进一步鼓励学生发挥他们的创造力，以优化现有的人工智能模型或共同创造更有意义的人工智能。这些核心能力构成了进一步学习和在进一步教育、工作和生活中更专业化使用人工智能的基础。

Chapter 3: Structure of the Al competency
第三章：人工智能能力的结构

framework for students 学生的框架

3.1 The framework 3.1 框架

The AI CFS specifies twelve competency blocks based on a matrix of two dimensions. The first dimension comprises four interlinked aspects of AI competencies, while the second dimension includes three levels of progression or mastery that students are expected to engage with iteratively.
AI 能力框架指定了基于两个维度矩阵的十二个能力模块。第一个维度包括四个相互关联的 AI 能力方面，而第二个维度则包含学生预期以迭代方式参与的三个进阶或掌握水平。

While the AI CFS anchors the definition of Al competency on three pillars that frame wider core competencies for students - namely, knowledge, skills and values - it also aims to encourage an ethical understanding of human-led methods underlying AI systems. Based on this conceptualization, the framework defines four essential constituent elements of students’ AI competency: a human-centred mindset, ethics of AI, AI techniques and applications, and AI system design. These elements focus on fundamental values, social responsibilities to uphold ethical principles, foundational knowledge and skills, and higher-order thinking skills for system design. While different elements can be developed through domain-specific learning and pedagogical methodologies, Al competencies are ultimately a set of interdisciplinary, general abilities and value orientations that extend beyond particular AI domains or tools.
虽然 AI 能力框架将 AI 能力的定义锚定在三个支柱上，这些支柱构成了学生更广泛的核心能力——即知识、技能和价值观——但它也旨在鼓励对人类主导的 AI 系统方法的伦理理解。基于这一概念框架，定义了学生 AI 能力的四个基本组成要素：以人为本的思维方式、AI 伦理、AI 技术与应用，以及 AI 系统设计。这些要素关注基本价值观、维护伦理原则的社会责任、基础知识和技能，以及系统设计的高阶思维技能。虽然不同的要素可以通过特定领域的学习和教学方法来发展，但 AI 能力最终是一组跨学科的通用能力和价值取向，超越了特定的 AI 领域或工具。

The first aspect positions students’ competencies within a human-centred attitude towards the benefits and risks of AI.
第一个方面将学生的能力定位于以人为本的态度，关注人工智能的益处和风险。

It also aims to foster a critical understanding of the proportionality 1 of specific AI tools for our human needs and for the sustainable development of the environment and ecosystems. Ethics of AI, the second aspect, encompasses the social and ethical components of students’ Al competencies, including the social skills to navigate, understand, practise and contribute to the adaptation of a growing set of principles that regulate human behaviour throughout the entire life cycle of AI.
它还旨在促进对特定人工智能工具在满足人类需求和可持续发展环境与生态系统方面的比例性的批判性理解。人工智能的伦理，作为第二个方面，涵盖了学生人工智能能力的社会和伦理组成部分，包括在整个人工智能生命周期中，导航、理解、实践和贡献于一套不断增长的规范人类行为的原则所需的社会技能。

The third aspect, Al techniques and applications, represents an integrated view of the intrinsically linked conceptual knowledge on Al and associated operational skills, using selected AI tools and authentic tasks. The last aspect is Al system design, which covers comprehensive engineering skills that determine the problem scoping, architecture building, training, testing and optimization of Al systems. This aspect aims to challenge and enable students to gain a deeper understanding of Al systems and scaffold their exploratory learning for the pursuit of further study in the field of AI.
第三个方面，人工智能技术和应用，代表了对人工智能及其相关操作技能的内在联系的概念知识的综合视角，使用选定的人工智能工具和真实任务。最后一个方面是人工智能系统设计，涵盖了全面的工程技能，这些技能决定了问题范围、架构构建、训练、测试和优化人工智能系统。这个方面旨在挑战并使学生深入理解人工智能系统，并为他们在人工智能领域进一步学习的探索性学习提供支撑。

The second dimension of the framework outlines three levels of progression: Understand, Apply and Create, which are designed to reflect levels of mastery across all four aspects outlined above. They can be used to furnish AI curricula or programmes of study with a spiral learning sequence
框架的第二个维度概述了三个进阶水平：理解、应用和创造，旨在反映上述四个方面的掌握水平。它们可以用于为人工智能课程或学习计划提供螺旋学习序列。
across grade levels, to assist students in progressively building a systematic and transferable schema of competencies.
跨年级水平，帮助学生逐步建立系统的、可转移的能力框架。

The framework matrix cuts across the four aspects for the three levels of progression or mastery (see Table 1). At the intersection of these levels and aspects are twelve constituent blocks of AI competencies whose characteristics underpin the critical thinking, ethical examination, practical use and iterative co-creation of AI. These competency blocks should be understood as interlinked units for the framing of key components. Rather than considering them as fragmented and disparate topics to be learned in isolation, they can be connected and woven together as the operational organs of AI competency.
框架矩阵涵盖了三个进阶或掌握水平的四个方面（见表 1）。在这些水平和方面的交汇处，有十二个构成 AI 能力的模块，其特征支撑着对 AI 的批判性思维、伦理审查、实际应用和迭代共创。这些能力模块应被理解为相互关联的单元，用于框定关键组成部分。与其将它们视为孤立学习的碎片化和离散主题，不如将它们连接和编织在一起，作为 AI 能力的操作器官。

The matrix provides a blueprint for learning outcomes at a minimum level of mastery within a certain competency block. More specifically, the matrix is designed to guide:
矩阵提供了在某个能力模块内达到最低掌握水平的学习成果蓝图。更具体地说，矩阵旨在指导：
(1) the scoping of main Al-related focus areas and expected mastery levels, tailored to local Al readiness and available instructional time; (2) the identification of AI-related learning content that can be integrated across existing curricula, subject areas, and grade levels; (3) the definition of proficiency levels and the development of assessment criteria to assess students’ general AI competencies and progression; and (4) the design and exploration of age-appropriate and domain-specific agile teaching and learning methodologies. Many of these factors will be vital to consider when a country, district or school localizes this framework; the selection of focus aspects and specification of the desired mastery levels, for instance, will depend on students’ existing AI competencies, the training and skills of teachers, the availability of learning hours, and local AI readiness, including affordability and infrastructure.
(1) 主要人工智能相关重点领域的范围界定和预期掌握水平，针对当地人工智能准备情况和可用的教学时间量身定制；(2) 确定可以融入现有课程、学科领域和年级的人工智能相关学习内容；(3) 定义熟练程度和制定评估标准，以评估学生的整体人工智能能力和进展；(4) 设计和探索适合年龄和特定领域的灵活教学和学习方法。许多这些因素在一个国家、地区或学校本地化此框架时将至关重要；例如，重点方面的选择和所需掌握水平的具体化将取决于学生现有的人工智能能力、教师的培训和技能、学习时间的可用性以及当地的人工智能准备情况，包括可负担性和基础设施。

Table 1. Al competency framework for students
表 1. 学生人工智能能力框架

Competency aspects 能力方面

Progression levels 进展水平

Understand 理解

Apply 应用

Create 创造

- 以人为本的思维方式

- Human-centred

mindset

- Human agency - 人类能动性

- 人类问责制

- Human

accountability

- 人工智能时代的公民身份

- Citizenship in the era

of AI

- Ethics of AI
- 人工智能的伦理学

- Embodied ethics - 具身伦理

- 安全和负责任的使用

- Safe and responsible

use

- Ethics by design
- 设计中的伦理

- 人工智能技术与应用

- Al techniques and

applications

- Al foundations - 人工智能基础

- Application skills - 应用技能

- Creating Al tools
- 创建人工智能工具

- Al system design
- 人工智能系统设计

- Problem scoping - 问题范围

- Architecture design - 架构设计

- 迭代和反馈循环

- Iteration and

feedback loops

3.2 Progression levels 3.2 进阶水平

The three levels reflect increasing sophistication, proficiency and ethical consciousness in using and co-creating Al technology. Students are expected to progress through them reciprocally. These levels, and the specifications of each competency block, can guide both the formative and summative evaluations of students’ Al competencies, as well as inform the design of contextually relevant and agile pedagogical methodologies.
这三个层次反映了在使用和共同创造人工智能技术方面日益复杂的能力、熟练程度和伦理意识。学生们预计会在这三个层次之间相互进步。这些层次以及每个能力模块的具体要求，可以指导学生人工智能能力的形成性和总结性评估，并为设计具有情境相关性和灵活性的教学方法提供信息。

Level 1: Understand 第一层次：理解

This first level is designed for all students. All individuals are, or will be, interacting with some form of AI over the course of their lives. It is also true that AI providers have been mining and manipulating data from almost all internet users. All students must therefore develop the human-centred values, knowledge and skills needed to engage in a safe, informed and meaningful manner in their daily interaction with Al in various spheres of life.
第一个层次是为所有学生设计的。所有人都将在其生活中与某种形式的人工智能互动。AI 提供者几乎一直在挖掘和操纵几乎所有互联网用户的数据，这也是事实。因此，所有学生必须发展以人为本的价值观、知识和技能，以便在日常与人工智能的互动中以安全、知情和有意义的方式参与各个生活领域。

At the ‘Understand’ level, students are expected to foster an understanding of what Al is and construct age-appropriate interpretations of the values, ethical issues, concepts, processes and technical methods underlying Al tools and their uses. They should be able to explain or exemplify their knowledge with connections to real-life or social practices and assimilate novel knowledge by integrating them into their own knowledge schemas.
在“理解”层面，学生需要培养对人工智能的理解，并构建适合年龄的对人工智能工具及其使用所涉及的价值观、伦理问题、概念、过程和技术方法的解释。他们应该能够通过与现实生活或社会实践的联系来解释或举例说明他们的知识，并通过将新知识整合到自己的知识结构中来吸收新知识。

This level of mastery provides the essential attitudinal, cognitive and practical foundations for the further study of AI. It does not define the exit-level competencies for specific areas or domains of AI overall.
这一掌握水平为进一步学习人工智能提供了基本的态度、认知和实践基础。它并不定义人工智能特定领域或整体的退出级别能力。

Level 2: Apply 第二级：应用

Given that the use of AI has permeated all sectors, as well as all aspects of life, including education and work, students at school should be prepared to become responsible, active and effective users of AI, both for the sake of their own individual interests, as well as to address shared sustainability challenges. The outcomes at the second level, ‘Apply’, are therefore relevant for all school students and can be used to tailor the scope, breadth and level of difficulty of thematic modules of a formal AI curriculum. Studying at this level requires students to have acquired a basic understanding of the human-centred approach and essential ethical principles for Al , as well as basic Al knowledge and application skills.
鉴于人工智能的使用已经渗透到各个领域，以及生活的各个方面，包括教育和工作，学校的学生应该准备好成为负责任、积极和有效的人工智能用户，这不仅是为了他们个人的利益，也是为了应对共同的可持续发展挑战。因此，第二级别的结果“应用”对所有学校学生都是相关的，可以用来定制正式人工智能课程的主题模块的范围、广度和难度水平。在这个级别的学习要求学生具备对以人为本的方法和人工智能的基本伦理原则的基本理解，以及基本的人工智能知识和应用技能。

At the 'Apply’level, students are expected to enhance, transfer and adapt their learned values, knowledge and skills to new learning processes. They do so by addressing theoretical questions and/or practical tasks in more complex contexts, and by critically examining advanced technical methods behind AI tools. Upon achieving this level, students will have constructed a sound and transferable foundation of conceptual knowledge and associated AI skill-sets. They should also be able to apply the humancentred mindset and ethical perspective to the assessment, study and practical uses of Al tools.
在“应用”层面，学生需要增强、转移和调整他们所学的价值观、知识和技能，以适应新的学习过程。他们通过在更复杂的背景下解决理论问题和/或实践任务，并批判性地审视 AI 工具背后的先进技术方法来实现这一点。达到这一层次后，学生将构建起坚实且可转移的概念知识基础和相关的 AI 技能组合。他们还应该能够将以人为本的思维方式和伦理视角应用于对 AI 工具的评估、研究和实际使用。

Students at this level may progress to the third, more specialized level, Create. However, it is possible that some students will not have a strong interest in Al, or will lack sufficient time or opportunities to finetune their AI competencies within the formal learning environment at school. For many, ‘Apply’ at Level 2 will be the point of exit for their Al-related competency development, at least at school.
处于这一层次的学生可能会进展到第三个更专业的层次，即“创造”。然而，也有可能一些学生对 AI 没有强烈的兴趣，或者在学校的正式学习环境中缺乏足够的时间或机会来细化他们的 AI 能力。对于许多人来说，“应用”在第二层次将是他们在学校的 AI 相关能力发展的退出点。

Level 3: Create 第三层次：创造

The exponential pace of innovation within the Al sector means that technology providers are defining the terms of the transformation of our societies. Developing critical AI competencies is critical to ensuring that the design, deployment and use of Al responds to the needs of users and benefits the public. School students should be prepared to create trustable AI tools and to take a leading role in the definition and design of the next generation of Al technologies. At the ‘Create’ level, students are expected to become conscientious AI co-creators, developing human-centred solutions to positively impact the design and use of AI. Study at this level requires the integrated application of the acquired values, knowledge and skills on Al to design, implement and test AI solutions that can help address real-world challenges.
人工智能领域创新的指数级发展意味着技术提供者正在定义我们社会转型的条款。发展关键的人工智能能力对于确保人工智能的设计、部署和使用能够响应用户的需求并惠及公众至关重要。学校学生应准备好创建可信赖的人工智能工具，并在下一代人工智能技术的定义和设计中发挥主导作用。在“创造”层面，学生被期望成为有责任感的人工智能共同创造者，开发以人为本的解决方案，以积极影响人工智能的设计和使用。在这一层次的学习需要将所获得的价值观、知识和技能综合应用于人工智能，以设计、实施和测试能够帮助解决现实世界挑战的人工智能解决方案。

Students will critically leverage their knowledge and skills on data, algorithms and ethical design; actively craft Al applications; and deliberate on the adaptation of AI regulations.
学生将批判性地利用他们在数据、算法和伦理设计方面的知识和技能；积极构建人工智能应用；并对人工智能法规的适应进行深入讨论。

At the ‘Create’ level, students are expected to reinforce their interest in Al innovation and develop new Al tools based on open-source and/or customizable datasets, programming
在“创造”层面，学生被期望增强对人工智能创新的兴趣，并基于开源和/或可定制的数据集、编程开发新的人工智能工具。
tools or Al models. Throughout the iterative process of customizing and testing AI technologies, students are expected to reinforce the sense of being an Al co-creator and belonging within a broader community, helping to lead the human-centred design and use of AI. At this level, students are also expected to enhance their capacity to critically assess the social implications of AI and to personalize the responsibilities of being a citizen in Al-driven societies.
工具或 AI 模型。在定制和测试 AI 技术的迭代过程中，学生们被期望增强作为 AI 共同创造者的意识，并在更广泛的社区中感受到归属感，帮助引领以人为本的 AI 设计和使用。在这个层面上，学生们还被期望提高批判性评估 AI 社会影响的能力，并个性化在 AI 驱动社会中作为公民的责任。

Learning at the ‘Create’ level also aims to foster students’ creative problemsolving skills and a proactive attitude to advocating for ethical Al practices. Meeting the requirements of this level in full will require sufficient allocation of learning time and space within the curriculum (e.g. an entire semester or multiple semesters). The learning programme must also provide the necessary AI resources and facilitate age-appropriate innovative pedagogical methodologies. For students who do not have a strong interest in pursuing deeper study in the field, the learning outcomes at this level, in particular under the ‘Al system design’ aspect, should be offered as elective programmes rather than as compulsory requirements for all students.
在“创造”层次的学习还旨在培养学生的创造性解决问题的能力和积极倡导伦理人工智能实践的态度。要完全满足这一层次的要求，需要在课程中充分分配学习时间和空间（例如，整个学期或多个学期）。学习计划还必须提供必要的人工智能资源，并促进适合年龄的创新教学方法。对于那些对深入研究该领域没有强烈兴趣的学生，特别是在“人工智能系统设计”方面的学习成果，应作为选修课程而非所有学生的必修要求提供。

3.3 Aspects 3.3 方面

The four aspects specify the essential constituent elements of AI competencies that students need to build and continuously update in order to become responsible users and active co-creators of AI, and potential leaders in defining and developing next generations of AI.
四个方面指定了学生需要构建和不断更新的人工智能能力的基本组成要素，以便成为负责任的用户和积极的人工智能共同创造者，并在定义和发展下一代人工智能方面成为潜在的领导者。

Human-centred mindset 以人为本的思维方式

Competency aspects 能力方面

Progression levels 进阶水平

Understand 理解

Apply 应用

Create 创建

- 以人为本的思维方式

- Human-centred

mindset

• Human agency • 人类自主性

- 人类问责制

- Human

accountability

- 人工智能时代的公民身份

- Citizenship in the era

of AI

The ‘Human-centred mindset’ aspect focuses on students’ values, beliefs and critical thinking skills, applied to the examination of whether Al is fit for purpose, whether its use is justified, how humans should interact with it, and what responsibilities individuals and institutions should take on to contribute to the building of safe, inclusive and just AI societies. A human-centred mindset lays the foundation for further engagement with all aspects of AI. The full expression of this aspect also encompasses human identities in relation with Al , assuming social and civic responsibilities, and the pursuit or deepening of personal interests in the AI era. The values and skills that this aspect is intended to nurture can be characterized by the following three competency blocks:
“以人为本的思维方式”侧重于学生的价值观、信念和批判性思维技能，应用于审视人工智能是否适合其目的、其使用是否合理、人类应如何与之互动，以及个人和机构应承担什么责任，以促进安全、包容和公正的人工智能社会的建设。以人为本的思维方式为进一步参与人工智能的各个方面奠定了基础。该方面的全面表达还包括人类身份与人工智能的关系，承担社会和公民责任，以及在人工智能时代追求或深化个人兴趣。该方面旨在培养的价值观和技能可以通过以下三个能力模块来表征：

Human agency: Students are expected to be able to recognize that Al is humanled and that the decisions of AI creators influence the way in which AI systems impact human rights, human-Al interaction, as well as their own lives and societies. They are expected to understand the implications of protecting human agency throughout the design, provision and use of Al. Students will understand what it means for Al to be human-controlled, and what the consequences might be when this is not the case.
人类代理：学生应能够认识到人工智能是由人类主导的，人工智能创造者的决策影响着人工智能系统对人权、人类与人工智能互动以及他们自身生活和社会的影响。他们应理解在人工智能的设计、提供和使用过程中保护人类代理的意义。学生将理解人工智能受人类控制的含义，以及当这种情况不再成立时可能产生的后果。

Human accountability: Students are expected to recognize that human accountabilities are the legal obligations of Al creators and AI service providers, and to understand what human accountabilities they should assume during the design and use of AI. They should also develop an awareness that human accountability is a legal and social responsibility when using AI to assist in decision-making, and that human choice should not be ceded to Al when making high-stakes decisions.
人类问责：学生应认识到人类问责是人工智能创造者和人工智能服务提供者的法律义务，并理解在人工智能的设计和使用过程中他们应承担哪些人类问责。他们还应意识到，在使用人工智能辅助决策时，人类问责是一种法律和社会责任，并且在做出高风险决策时，人类选择不应被让渡给人工智能。

Citizenship in the Al era: Students are expected to critically understand the impact of Al on human societies and to promote responsible and inclusive design and use of Al for sustainable development. They should have an awareness of their civic and social responsibility as citizens in the era of AI. Students are also expected to develop a desire to continue learning about, and using, Al throughout their lives to support selfactualization.
人工智能时代的公民身份：学生应批判性地理解人工智能对人类社会的影响，并促进负责任和包容性的人工智能设计与使用，以实现可持续发展。他们应意识到作为人工智能时代公民的公民和社会责任。学生还应培养终身学习和使用人工智能以支持自我实现的愿望。

Ethics of AI 人工智能的伦理

Competency aspects 能力方面

Progression levels 进阶水平

Understand 理解

Apply 应用

Create 创造

• Ethics of AI
• 人工智能伦理

•Embodied ethics • 具身伦理

• 安全和负责任的使用

• Safe and responsible

use

• Ethics by design
• 设计中的伦理

The ‘Ethics of Al’ aspect represents the ethical value judgements, embodied reflections, and social and emotional skills students require to navigate, understand, practise and contribute to the adaptation of a growing set of principles and regulatory rules relative to the entire life cycle of AI systems. Students are expected to understand and apply knowledge on the governance of ethics at the intersection of global implications and local contexts. As the rapid iterations of AI are triggering more profound controversies, the scope of the ethics of Al is expanding, and new regulations, laws and rules are being adopted. The three competency blocks for this aspect outline key steps for students to gradually internalize ethical principles as well as habituate compliance with AI regulations.
“人工智能伦理”方面代表了学生在适应、理解、实践和贡献于一套不断增长的原则和监管规则的整个生命周期中所需的伦理价值判断、具身反思以及社会和情感技能。学生需要理解并应用关于伦理治理的知识，特别是在全球影响与地方背景的交汇处。随着人工智能的快速迭代引发了更深刻的争议，人工智能伦理的范围正在扩大，新的法规、法律和规则也在不断被采纳。该方面的三个能力模块概述了学生逐步内化伦理原则以及习惯遵守人工智能法规的关键步骤。

Embodied ethics: Students are expected to develop a basic understanding of the issues underlying key ethical debates around Al , including the impact of Al on human rights, social justice, inclusion, equity and climate change within their local context and personal lives. They will have understood, internalized, and adopted the following principles in their reflective practices and uses of Al tools in their learning and beyond:
具身伦理：学生应当发展对人工智能关键伦理辩论背后问题的基本理解，包括人工智能对人权、社会正义、包容性、公平和气候变化的影响，尤其是在他们的本地环境和个人生活中。他们将理解、内化并采纳以下原则，以指导他们在学习及其他方面使用人工智能工具的反思实践：

Do no harm: Students demonstrate an understanding that Al systems should not be used for purposes that might be harmful for humans (such as facial recognition for surveillance or
不造成伤害：学生展示出理解人工智能系统不应被用于可能对人类造成伤害的目的（例如用于监视的面部识别或
assigning social status, or predictive algorithms for grading examinations). This includes the ability to assess whether a certain AI solution infringes upon human values and rights, particularly data privacy, and to decide on whether a particular AI method complies with global or local regulations.
社会地位分配，或用于考试评分的预测算法）。这包括评估某个人工智能解决方案是否侵犯人类价值和权利，特别是数据隐私，并决定某种人工智能方法是否符合全球或地方的法规。
Proportionality: Students develop the capacity - as appropriate for their age and ability level - to examine whether or not the use of a specific AI system is advantageous in achieving a justified aim, and whether or not a given AI method is appropriate to the context.
比例原则：学生发展能力——根据他们的年龄和能力水平——审查特定人工智能系统的使用是否有助于实现合理的目标，以及某种人工智能方法是否适合该情境。
Non-discrimination: Students are aware of and are able to detect gender, ethnic, cultural and other biases embedded in Al tools or their outputs. Further, students are aware of AI divides within and between countries, and understand the need to make efforts to address these and ensure greater accessibility and inclusivity.
非歧视：学生能够意识到并检测出嵌入在人工智能工具或其输出中的性别、种族、文化和其他偏见。此外，学生还意识到国家内部和之间的人工智能差距，并理解需要努力解决这些问题，以确保更大的可及性和包容性。
Sustainability: Students are able to explain and illustrate the implications of AI systems for environmental sustainability.
可持续性：学生能够解释和说明人工智能系统对环境可持续性的影响。
Human determination in human-AI collaboration: Students are able to demonstrate why humans should bear ethical and legal responsibilities for the use of Al ; they are able to exemplify how humans can remain accountable in Al-assisted decisionmaking loops, rather than cede determination to machines.
人类在人工智能协作中的决策：学生能够展示为什么人类应对人工智能的使用承担伦理和法律责任；他们能够举例说明人类如何在人工智能辅助的决策循环中保持问责，而不是将决策权交给机器。
Transparency and explainability: Students are aware that users are entitled to request explanatory information from designers and providers on how Al tools work, how their outputs are produced based on algorithms and models, and the degree to which the deployment and application of certain Al tools are appropriate for users of a certain age or ability level.
透明性和可解释性：学生意识到用户有权要求设计者和提供者提供关于人工智能工具如何工作、其输出是如何基于算法和模型生成的，以及某些人工智能工具的部署和应用在特定年龄或能力水平的用户中是否适当的解释性信息。
Safe and responsible use: Students are expected to be able to use AI in a responsible manner in compliance with ethical principles and locally applicable regulations. They are aware
安全和负责任的使用：学生应能够以负责任的方式使用人工智能，遵守伦理原则和当地适用的法规。他们意识到
of the risks of disclosing data privacy and they take measures to ensure that their data are collected, used, shared, archived and deleted only with their deliberate and informed consent. They are also aware of the specific risks of certain Al systems, and are able to protect their own safety, as well as that of their peers, when using AI.
透露数据隐私的风险，并采取措施确保他们的数据仅在经过深思熟虑和知情同意的情况下被收集、使用、共享、存档和删除。他们还意识到某些人工智能系统的特定风险，并能够在使用人工智能时保护自己和同伴的安全。
Ethics by design: Students are expected to adopt an ethics-bydesign approach to the design, assessment and use of Al tools, as well as to the review and adaptation of AI regulations. Students are aware that assessing the intent behind AI design involves examining all steps of the AI life cycle, starting with the stage of conceptualization. Students should be able to assess the compliance of an AI tool with ethical regulations, as well as review Al regulations and inform adaptation.
设计中的伦理：学生应采用设计中的伦理方法来设计、评估和使用人工智能工具，以及审查和调整人工智能法规。学生应意识到，评估人工智能设计背后的意图涉及审查人工智能生命周期的所有步骤，从概念化阶段开始。学生应能够评估人工智能工具与伦理法规的合规性，并审查人工智能法规并告知调整。

Al techniques and applications
人工智能技术和应用

Competency aspects 能力方面

Progression levels 进阶水平

Understand 理解

Apply 应用

Create 创建

- 人工智能技术与应用

- Al techniques and

applications

•Al foundations • 人工智能基础

• Application skills • 应用技能

• Creating Al tools
• 创建人工智能工具

The ‘Al techniques and applications’ aspect represents the intrinsically linked conceptual knowledge on Al and associated operational skills, in connection with concrete Al tools or authentic tasks. This aspect serves as the most important and transferable technical foundation for a concrete understanding and application of
“人工智能技术与应用”方面代表了与人工智能及相关操作技能密切相关的概念知识，涉及具体的人工智能工具或真实任务。这个方面是对人工智能的具体理解和应用最重要且可转移的技术基础。
a human-centred mindset and its associated ethical principles. The basic knowledge structure and practical skills on data and Al programming is the foundation for the capacity to design and build AI systems, especially for students who have strong interests and abilities in the field. The ‘AI techniques and applications’ aspect implies
以人为本的思维方式及其相关的伦理原则。关于数据和人工智能编程的基本知识结构和实践技能是设计和构建人工智能系统的基础，尤其是对于那些在该领域具有强烈兴趣和能力的学生。“人工智能技术与应用”方面意味着
that students are expected to look into exemplar AI tools to gain insight on how AI is developed, based on data and algorithms. Students will synchronically acquire skills in Al programming and reinforce the transferability of their knowledge and skills by applying them to the crafting of Al tools. In the stream of the three progression levels, students are also expected to integrate ethical, cultural and social parameters, and solidify the interdisciplinary foundational knowledge and skills in science, technology, engineering, mathematics, arts, languages and social studies.
学生们被期望研究示范性的人工智能工具，以深入了解人工智能是如何基于数据和算法开发的。学生们将同步掌握人工智能编程技能，并通过将这些知识和技能应用于人工智能工具的制作来增强其可转移性。在三个进阶层次的过程中，学生们还被期望整合伦理、文化和社会参数，并巩固科学、技术、工程、数学、艺术、语言和社会研究的跨学科基础知识和技能。

Al foundations: Students are expected to be able to build basic knowledge and skills on AI, particularly with respect to data and algorithms, understanding the importance of the interdisciplinary foundational knowledge required to gradually deepen understanding of data and algorithms. Students should also be able to connect conceptual knowledge on Al with their activities in society and daily
人工智能基础：学生们被期望能够建立关于人工智能的基本知识和技能，特别是在数据和算法方面，理解逐步加深对数据和算法理解所需的跨学科基础知识的重要性。学生们还应该能够将关于人工智能的概念知识与他们在社会和日常生活中的活动联系起来。
life, concretizing a human-centred mindset and ethical principles by understanding how AI works and how Al interacts with humans.
生活，具体化以人为本的思维方式和伦理原则，理解人工智能是如何工作的，以及人工智能如何与人类互动。

Application skills: Students are expected to be able to construct an age-appropriate understanding of data, Al algorithms and programming, as well as acquire transferable application skills. Students are expected to be able to critically evaluate and leverage free and/or open-source AI tools, programming libraries and datasets.
应用技能：学生应能够构建适合其年龄的数据、人工智能算法和编程的理解，并获得可转移的应用技能。学生应能够批判性地评估和利用免费和/或开源的人工智能工具、编程库和数据集。

Creating AI tools: Students are expected to be able to deepen and apply knowledge and skills on data and algorithms to customize existing AI toolkits to create task-based AI tools. Students are expected to integrate their human-centred mindset and ethical considerations into the assessment of existing AI resources. They are also expected to develop the social and emotional skills needed to engage in creating with AI, including through adaptivity, complex communication and teamwork skills.
创建人工智能工具：学生应能够深化和应用数据和算法的知识与技能，以定制现有的人工智能工具包，创建基于任务的人工智能工具。学生应将以人为本的思维方式和伦理考虑融入对现有人工智能资源的评估中。他们还应发展参与与人工智能创造相关的社会和情感技能，包括适应性、复杂沟通和团队合作技能。

Al system design 人工智能系统设计

Competency aspects 能力方面

Progression levels 进展水平

Understand 理解

Apply 应用

Create 创造

• Al system design
• 人工智能系统设计

• Problem scoping • 问题范围界定

• Architecture design • 架构设计

• 迭代和反馈循环

• Iteration and

feedback loops

The aspect of ‘Al system design’ focuses on the systemic design thinking and comprehensive engineering skills required for problem scoping, design, architecture building, training, testing and optimization of AI systems. This aspect aims to challenge the explainability of AI systems and to enable exploratory learning for students who will pursue further programmes of study in the
“人工智能系统设计”的方面侧重于系统设计思维和全面的工程技能，这些技能对于问题范围界定、设计、架构构建、训练、测试和优化人工智能系统是必需的。这个方面旨在挑战人工智能系统的可解释性，并为那些将继续深造的学生提供探索性学习的机会。
field. Students are also expected to deepen and practise ‘ethics by design’. Although the systemic design thinking methodology, associated human-centred values and ethical principles, and required knowledge and skills on Al may be embedded in all other aspects of students’ Al competencies, this aspect mainly targets students who have a
学生还需加深并实践“设计中的伦理”。尽管系统设计思维方法论、相关的人本价值观和伦理原则，以及人工智能所需的知识和技能可能嵌入到学生人工智能能力的其他所有方面中，但这个方面主要针对那些对深化自己在该领域的知识和技能有特别兴趣和承诺的学生。
particular interest in, and commitment to, deepening their knowledge and skills in this field.
特别关注并致力于在该领域深化知识和技能的学生。

Problem scoping: Students are expected to be able to understand the importance of ‘Al problem scoping’ as the starting point for Al innovation. They are expected to be able to examine whether AI should be used in particular situations, from a legal, ethical and logical perspective; and to define the boundaries, goals and constraints of a problem before attempting to train an AI model to solve it. Students are also expected to acquire the knowledge and project-planning skills needed in order to conceptualize and construct an Al system, including the ability to assess the appropriateness of different AI techniques, define the need for data, and devise test and feedback metrics.
问题范围界定：学生应能够理解“人工智能问题范围界定”作为人工智能创新起点的重要性。他们应能够从法律、伦理和逻辑的角度审视在特定情况下是否应使用人工智能；并在尝试训练人工智能模型解决问题之前，定义问题的边界、目标和约束。学生还应掌握构思和构建人工智能系统所需的知识和项目规划技能，包括评估不同人工智能技术的适用性、定义数据需求以及制定测试和反馈指标的能力。

Architecture design: Students are expected to be able to cultivate basic methodological knowledge and technical skills to configure a scalable, maintainable and reusable architecture for an Al system covering layers of data, algorithms, models and application interfaces. Students are expected to develop
架构设计：学生应能够培养基本的方法论知识和技术技能，以配置一个可扩展、可维护和可重用的人工智能系统架构，涵盖数据、算法、模型和应用接口的各个层面。学生应能够发展
the interdisciplinary skills necessary to leverage datasets, programming tools and computational resources to construct a prototype AI system. This includes the expectation that they apply deepened human-centred values and ethical principles in their configuration, construction and optimization.
利用数据集、编程工具和计算资源构建原型 AI 系统所需的跨学科技能。这包括期望他们在配置、构建和优化过程中应用深化的人本价值观和伦理原则。

Iteration and feedback: Students are expected to enhance and apply their interdisciplinary knowledge and practical methods to evaluate the appropriateness and methodological robustness of an AI model and its impact on individual users, societies and the environment. They should be able to acquire age-appropriate technical skills to improve the quality of datasets, reconfigure algorithms and enhance architectures in response to results of tests and feedback. They should be able to apply a human-centred mindset and ethical principles in simulating decision-making on when an Al system should be shut down and how its negative impact can be mitigated. They are also be expected to cultivate their identities as co-creators within the wider AI community.
迭代和反馈：学生应增强并应用他们的跨学科知识和实践方法，以评估 AI 模型的适用性和方法论的稳健性，以及其对个体用户、社会和环境的影响。他们应能够获得适合年龄的技术技能，以提高数据集的质量，重新配置算法，并根据测试和反馈的结果改进架构。他们应能够在模拟决策时应用人本思维和伦理原则，判断何时应关闭 AI 系统以及如何减轻其负面影响。他们还应被期望在更广泛的 AI 社区中培养作为共同创造者的身份。

Chapter 4: Specifications of AI competencies
第四章：AI 能力规范

for students 针对学生

The following specifications of the AI CFS clarify what each competency block entails in terms of curricular goals, desirable pedagogical methods and required learning environments, with consideration given to inclusivity as well as variation in levels of AI readiness.
AI 能力框架的以下规范阐明了每个能力模块在课程目标、期望的教学方法和所需学习环境方面的内容，同时考虑到包容性以及 AI 准备水平的差异。

The specifications outlined below are based on the assumption that students’ Al competencies are the result of the integrated interventions of national AI curricula; extracurricular programmes; informal learning through various media, including the internet; and engagement with families and local communities. To guide the development of an AI curriculum, the AI CFS specifies the expected learning and behavioural outcomes of a formal AI curriculum while considering the impact of informal learning in social contexts. AIrelated learning - introduced into curricula as a specific subject, or as modules within related disciplines, such as computer science or information and communication technology (ICT) - should be allocated adequate instructional time within a semester, or preferably, across multiple semesters.
以下列出的规范基于这样的假设：学生的 AI 能力是国家 AI 课程、课外项目、通过各种媒体（包括互联网）进行的非正式学习以及与家庭和地方社区的互动的综合干预结果。为了指导 AI 课程的开发，AI 能力框架规定了正式 AI 课程的预期学习和行为结果，同时考虑到社会背景中非正式学习的影响。与 AI 相关的学习——作为特定学科引入课程，或作为计算机科学或信息与通信技术（ICT）等相关学科中的模块——应在一个学期内分配足够的教学时间，或更好地，在多个学期内进行分配。

The specified curricular goals outline domain-specific values, knowledge and skills that can be applied to students at a range of ages and ability levels, who are exposed to AI-related learning for the first time. It is up to national or institutional curriculum agencies to define concrete learning objectives for specific student
指定的课程目标概述了特定领域的价值观、知识和技能，这些可以应用于不同年龄和能力水平的学生，他们首次接触与人工智能相关的学习。由国家或机构的课程机构来定义具体的学习目标，以适应特定学生的需求。
cohorts, based on their Al readiness and that of their teachers, available instructional time and local learning environments. The specifications include recommendations for configuring these environments in line with the curricular goals, with regard to inclusivity, the potential of open-source options, and the sharing of AI resources with academic institutes and the private sector.
基于他们的人工智能准备程度以及教师的准备程度、可用的教学时间和当地学习环境，针对特定学生群体的学习目标。规范包括关于如何根据课程目标配置这些环境的建议，涉及包容性、开源选项的潜力以及与学术机构和私营部门共享人工智能资源。

Finally, the specifications also propose pedagogical methodologies for specific domains of Al at a certain progression level. These may inspire teachers and students to explore agile methods of delivery that are relevant for specific contexts and needs.
最后，规范还提出了针对特定人工智能领域在某一进阶水平上的教学方法。这些方法可能会激励教师和学生探索与特定背景和需求相关的灵活教学方式。

4.1 Level 1: Understand
4.1 级别 1：理解

The overall goal of this level is to support all students to acquire an understanding of what Al is and to construct age-appropriate interpretations of the values, ethical issues, concepts, processes and technical methods underlying AI tools and their uses. Students should also be supported to make connections between their knowledge of Al and real-life experiences, and between domain-specific knowledge of Al and knowledge of related learning areas.
该级别的总体目标是支持所有学生理解人工智能（AI）的概念，并构建适合年龄的对 AI 工具及其使用所涉及的价值观、伦理问题、概念、过程和技术方法的解释。还应支持学生将他们对 AI 的知识与现实生活经验之间建立联系，以及将 AI 的领域特定知识与相关学习领域的知识之间建立联系。

The curricular goals outlined in Table 2 help to map the set of foundational values, ethical principles, knowledge and understanding that can ensure the proper and effective use of AI by students - an ability sometimes referred to as ‘Al literacy’. The suggested pedagogical methods are designed to
表 2 中概述的课程目标有助于绘制出一套基础价值观、伦理原则、知识和理解，这些可以确保学生正确有效地使用 AI——这种能力有时被称为“AI 素养”。建议的教学方法旨在
facilitate age- and domain-appropriate teaching and learning practices that can potentially stimulate students’ interests and support their learning trajectory on the basis of concrete tools, personal experiences, and
促进适合年龄和领域的教学和学习实践，这些实践可以潜在地激发学生的兴趣，并支持他们基于具体工具、个人经验和
real-world use scenarios. The specifications also recommend basic learning settings, which include practising with unplugged and low-tech options.
现实世界使用场景的学习轨迹。规范还建议基本的学习环境，包括使用无电和低技术选项进行实践。

Table 2. Competency blocks for level 1: Understand
表 2. 1 级能力模块：理解

STUDENT COMPETENCY 学生能力

课程目标（人工智能课程或学习计划应...）

CURRICULAR GOALS

(AI curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习设置。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Humancentred mindset 以人为本的思维方式

4.1.1 人类代理 - 学生应能够认识到人工智能是由人类主导的，人工智能创造者的决策影响着人工智能系统对人权、人机互动以及他们自身生活和社会的影响。他们应理解在设计、提供和使用人工智能的过程中保护人类代理的意义。学生将理解人工智能受人类控制的含义，以及当情况不是这样时可能产生的后果。

4.1.1 Human agency

- Students are expected to be able to recognize that Al is humanled and that the decisions of the AI creators influence how AI systems impact human rights, human-AI interaction, and their own lives and societies. They are expected to understand the implications of protecting human agency throughout the design, provision and use of AI. Students will understand what it means for Al to be human-controlled, and what the consequences could be when that is not the case.

- CG4.1.1.1 Foster an understanding that AI is human-led: Based on selected AI tools, explain to students that AI is humanled; facilitate students to develop a stepwise and integral comprehension of human agency which may cover principles on data ownership and data privacy, protection of human rights in collecting and processing data, explainability of AI methods, human control in deployment, and human determination in using AI for decision-making. Guide students to understand that Al cannot replace human thinking or intellectual development.
- CG4.1.1.1 培养对人工智能是人类主导的理解：基于所选的人工智能工具，向学生解释人工智能是由人类主导的；促进学生逐步和全面理解人类代理，这可能涵盖数据所有权和数据隐私的原则、在收集和处理数据时保护人权、人工智能方法的可解释性、部署中的人类控制，以及在决策中使用人工智能的人类决策。引导学生理解人工智能无法替代人类思维或智力发展。

- CG4.1.1.2 Facilitate an understanding on the necessity of exercising sufficient human control over AI: Expose students to real-world scenarios and guide students to experience the consequences of human oversight in controlling AI (e.g. weak regulations failing to prevent the design and production of harmful AI tools, the institutional use of Al to substitute for humans when making high-stakes decisions, and the absence of human validation of the accuracy of AI outputs). Help students to grasp the necessity of exercising human control over AI
- CG4.1.1.2 促进对在人工智能上行使足够人类控制必要性的理解：让学生接触真实世界的场景，并引导学生体验人类监督在控制人工智能中的后果（例如，薄弱的监管未能防止有害人工智能工具的设计和生产，机构使用人工智能替代人类进行高风险决策，以及缺乏对人工智能输出准确性的人工验证）。帮助学生理解在人工智能上行使人类控制的必要性

Visualizing the abstract concept of human agency throughout the Al life cycle: Ask students to draw concept maps of human agency in key steps of the life cycle of selected AI tools, including data ownership, respecting data privacy when collecting and processing data, explainability of AI algorithms and AI models, human-controlled evaluation of AI outputs, and human determination in Al-assisted decision-making. The concept maps should also reflect on the potential consequences of a loss of human agency at each step, for the individual and for society.
可视化人工智能生命周期中人类代理的抽象概念：要求学生绘制人类代理在选定人工智能工具生命周期关键步骤中的概念图，包括数据所有权、在收集和处理数据时尊重数据隐私、人工智能算法和模型的可解释性、人工控制的人工智能输出评估，以及在人工智能辅助决策中的人类决策。概念图还应反映在每个步骤中失去人类代理的潜在后果，既包括对个人的影响，也包括对社会的影响。

- Simulating an AI Act courtroom debate to evaluate creators' intents underlying prohibited AI systems: Based on an ageappropriate interpretation of the definition of Al systems prohibited under the European Union's AI Act, organize students to act as jury members to evaluate selected examples of AI systems that are due to be prohibited under the AI Act, deliberating on what their creators' intents and motivations may have been. Help students understand how these systems can do harm to humans, especially by undermining human agency: for example, an
- 模拟 AI 法案法庭辩论，以评估创作者在被禁止的 AI 系统背后的意图：根据对欧盟 AI 法案下被禁止的 AI 系统定义的适龄解释，组织学生作为陪审团成员，评估即将被 AI 法案禁止的选定 AI 系统的例子，讨论其创作者的意图和动机可能是什么。帮助学生理解这些系统如何对人类造成伤害，特别是通过削弱人类的自主权：例如，一个

- 无需电源的学习环境，如纸质文章、印刷阅读材料和工作表。 - 本地可用的 AI 工具，包括带有 AI 应用程序的手机。 - 预下载或录制的视频和其他与特定案例研究或呈现困境的场景相关的资源。 - 搜索引擎、在线视频和补充在线学习课程。

- Unplugged learning settings like paper-based articles, printed reading materials and worksheets.

- Locally available Al tools including mobile phones with Al applications.

- Predownloaded or recorded videos and other resources related to specific case studies, or scenarios that present a dilemma.

- Search engines, online videos and supplemental online learning courses.

STUDENT COMPETENCY "CURRICULAR GOALS (AI curricula or programmes of study should...)" "SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.)" "LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)" Humancentred mindset "4.1.1 Human agency - Students are expected to be able to recognize that Al is humanled and that the decisions of the AI creators influence how AI systems impact human rights, human-AI interaction, and their own lives and societies. They are expected to understand the implications of protecting human agency throughout the design, provision and use of AI. Students will understand what it means for Al to be human-controlled, and what the consequences could be when that is not the case." "- CG4.1.1.1 Foster an understanding that AI is human-led: Based on selected AI tools, explain to students that AI is humanled; facilitate students to develop a stepwise and integral comprehension of human agency which may cover principles on data ownership and data privacy, protection of human rights in collecting and processing data, explainability of AI methods, human control in deployment, and human determination in using AI for decision-making. Guide students to understand that Al cannot replace human thinking or intellectual development. - CG4.1.1.2 Facilitate an understanding on the necessity of exercising sufficient human control over AI: Expose students to real-world scenarios and guide students to experience the consequences of human oversight in controlling AI (e.g. weak regulations failing to prevent the design and production of harmful AI tools, the institutional use of Al to substitute for humans when making high-stakes decisions, and the absence of human validation of the accuracy of AI outputs). Help students to grasp the necessity of exercising human control over AI" "Visualizing the abstract concept of human agency throughout the Al life cycle: Ask students to draw concept maps of human agency in key steps of the life cycle of selected AI tools, including data ownership, respecting data privacy when collecting and processing data, explainability of AI algorithms and AI models, human-controlled evaluation of AI outputs, and human determination in Al-assisted decision-making. The concept maps should also reflect on the potential consequences of a loss of human agency at each step, for the individual and for society. - Simulating an AI Act courtroom debate to evaluate creators' intents underlying prohibited AI systems: Based on an ageappropriate interpretation of the definition of Al systems prohibited under the European Union's AI Act, organize students to act as jury members to evaluate selected examples of AI systems that are due to be prohibited under the AI Act, deliberating on what their creators' intents and motivations may have been. Help students understand how these systems can do harm to humans, especially by undermining human agency: for example, an" "- Unplugged learning settings like paper-based articles, printed reading materials and worksheets. - Locally available Al tools including mobile phones with Al applications. - Predownloaded or recorded videos and other resources related to specific case studies, or scenarios that present a dilemma. - Search engines, online videos and supplemental online learning courses."

| | STUDENT COMPETENCY | CURRICULAR GOALS (AI curricula or programmes of study should...) | SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) | LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) | | :---: | :---: | :---: | :---: | :---: | | Humancentred mindset | 4.1.1 Human agency - Students are expected to be able to recognize that Al is humanled and that the decisions of the AI creators influence how AI systems impact human rights, human-AI interaction, and their own lives and societies. They are expected to understand the implications of protecting human agency throughout the design, provision and use of AI. Students will understand what it means for Al to be human-controlled, and what the consequences could be when that is not the case. | - CG4.1.1.1 Foster an understanding that AI is human-led: Based on selected AI tools, explain to students that AI is humanled; facilitate students to develop a stepwise and integral comprehension of human agency which may cover principles on data ownership and data privacy, protection of human rights in collecting and processing data, explainability of AI methods, human control in deployment, and human determination in using AI for decision-making. Guide students to understand that Al cannot replace human thinking or intellectual development. - CG4.1.1.2 Facilitate an understanding on the necessity of exercising sufficient human control over AI: Expose students to real-world scenarios and guide students to experience the consequences of human oversight in controlling AI (e.g. weak regulations failing to prevent the design and production of harmful AI tools, the institutional use of Al to substitute for humans when making high-stakes decisions, and the absence of human validation of the accuracy of AI outputs). Help students to grasp the necessity of exercising human control over AI | Visualizing the abstract concept of human agency throughout the Al life cycle: Ask students to draw concept maps of human agency in key steps of the life cycle of selected AI tools, including data ownership, respecting data privacy when collecting and processing data, explainability of AI algorithms and AI models, human-controlled evaluation of AI outputs, and human determination in Al-assisted decision-making. The concept maps should also reflect on the potential consequences of a loss of human agency at each step, for the individual and for society. - Simulating an AI Act courtroom debate to evaluate creators' intents underlying prohibited AI systems: Based on an ageappropriate interpretation of the definition of Al systems prohibited under the European Union's AI Act, organize students to act as jury members to evaluate selected examples of AI systems that are due to be prohibited under the AI Act, deliberating on what their creators' intents and motivations may have been. Help students understand how these systems can do harm to humans, especially by undermining human agency: for example, an | - Unplugged learning settings like paper-based articles, printed reading materials and worksheets. - Locally available Al tools including mobile phones with Al applications. - Predownloaded or recorded videos and other resources related to specific case studies, or scenarios that present a dilemma. - Search engines, online videos and supplemental online learning courses. |

学生能力

STUDENT

COMPETENCY

课程目标（AI 课程或学习计划应...）

CURRICULAR GOALS

(AI curricula or programmes

of study should...)

建议的教学方法

SUGGESTED

PEDAGOGICAL METHODS

（机构和教师可以考虑并调整以下学习方法。）

(Institutions and teachers

can consider and adapt the

following learning methods.)

学习环境（可以提供和调整以下学习设置。）

LEARNING

ENVIRONMENTS

(The following learning

settings can be

provided and adapted.)

centred 以...为中心

mindset 心态

STUDENT COMPETENCY 学生能力

课程目标（所有课程或学习计划应...）

CURRICULAR GOALS

(Al curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习设置。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Ethics of AI 人工智能的伦理

4.1.2 具身伦理 - 学生应能够对人工智能的伦理问题及其对人权、社会正义、包容性、公平和气候变化的潜在影响有基本的理解，特别是在他们的本地环境和个人生活中。他们将理解并内化以下关键伦理原则，并在他们的反思实践和使用人工智能工具的过程中进行转化：- 不造成伤害：评估人工智能的监管合规性及其对人权的潜在侵犯 - 成比例性：评估人工智能的收益与风险和成本；评估上下文的适当性 - 非歧视：检测偏见，促进包容性和可持续性

4.1.2 Embodied ethics

- Students are expected to be able to develop a basic understanding of the ethical issues around Al , and the potential impact of Al on human rights, social justice, inclusion, equity and climate change within their local context and with regard to their personal lives. They will understand, and internalize the following key ethical principles, and will translate these in their reflective practices and uses of AI tools in their lives and learning:

- Do no harm: Evaluating Al's regulatory compliance and potential to infringe on human rights

- Proportionality: Assessing Al's benefits against risks and costs; evaluating contextappropriateness

- Non-discrimination: Detecting biases and promoting inclusivity and sustainability

- CG4.1.2.1 Illustrate dilemmas around AI and identify the main reasons behind ethical conflicts:
- CG4.1.2.1 说明人工智能相关的困境，并识别伦理冲突背后的主要原因：

Based on concrete Al tools, guide students to surface dilemma decisions that individual or corporate creators need to make in the design and development of Al (e.g. maximizing the scale of data collection versus protecting data ownership, recording users' private data for the training of Al models versus protecting their privacy, promoting machine control to generate profit versus guaranteeing the primacy of human agency, and prioritizing AI safety versus accelerating the iteration of AI). Support students to associate perspectives on these dilemmas with the reasons behind ethical conflicts around AI.
基于具体的人工智能工具，引导学生揭示个人或企业创作者在人工智能设计和开发中需要做出的困境决策（例如，最大化数据收集规模与保护数据所有权之间的权衡、记录用户私人数据以训练人工智能模型与保护用户隐私之间的权衡、促进机器控制以获取利润与保证人类主权之间的权衡，以及优先考虑人工智能安全与加速人工智能迭代之间的权衡）。支持学生将这些困境的不同视角与人工智能伦理冲突背后的原因联系起来。

- CG4.1.2.2 Facilitate scenario-based understandings of ethical principles on Al and their personal implications: Offer students opportunities to discuss age-appropriate real-world cases around the six core Al ethical principles: (1) 'do no harm', 2) proportionality, (3) non-discrimination, (4) sustainability, (5) human determination, and (6) transparency and explainability. Guide students to build a knowledge framework on the ethics of AI and practice
- CG4.1.2.2 促进基于情境的对人工智能伦理原则及其个人影响的理解：为学生提供讨论与六项核心人工智能伦理原则相关的适龄现实案例的机会：（1）“不造成伤害”，（2）比例原则，（3）非歧视，（4）可持续性，（5）人类决策，以及（6）透明性和可解释性。引导学生建立关于人工智能伦理的知识框架并进行实践。

- Case studies on scenarios containing controversies around AI: Present ageappropriate real-world or simulated scenarios, and guide students to surface controversies surrounding the Al tools and their uses. Discuss the main reasons behind such ethical conflicts and facilitate students to draw infographics or concept maps illustrating the core AI ethical principles.
- 关于人工智能争议场景的案例研究：呈现适合年龄的真实世界或模拟场景，引导学生揭示围绕人工智能工具及其使用的争议。讨论这些伦理冲突背后的主要原因，并帮助学生绘制信息图或概念图，说明核心的人工智能伦理原则。

- Individual or group reflection on the personal implications of ethical dilemmas: Engage students in group discussion and opinion taking on ethical dilemmas that may arise from uses of AI in daily life and learning in local contexts (e.g. whether large language models should use the data of local communities in their training or not; to what extent Al has a negative environmental impact or mitigates climate change; how much of their privacy users should forego to exchange benefits of Al services). Guide students to present their opinions through age-appropriate formats such as essays, posters, drawings or storyboards.
- 对伦理困境个人影响的个人或小组反思：让学生参与小组讨论和意见表达，讨论在日常生活和本地学习中使用人工智能可能引发的伦理困境（例如，大型语言模型是否应该使用本地社区的数据进行训练；人工智能在多大程度上对环境产生负面影响或缓解气候变化；用户应该放弃多少隐私以换取人工智能服务的好处）。引导学生通过适合年龄的格式（如论文、海报、绘画或故事板）展示他们的观点。

- Searching for and validating examples of 'Al for the public good': Organize individual or group scoping of examples of Al tools or approaches to the use of AI that
- 搜索和验证“为公众利益的人工智能”示例：组织个人或小组对人工智能工具或人工智能使用方法的示例进行范围界定。

- 断电学习环境和材料，包括印刷故事或案例研究、工作表和海报。 - 本地可用的人工智能工具，包括通过手机应用程序提供的工具。 - 预下载或录制的视频和其他与特定案例或情境相关的资源，这些案例或情境呈现出困境。 - 搜索引擎、在线视频或与案例研究相关的资源。

- Unplugged learning settings and materials including print stories or case studies, worksheets and posters.

- Locally available Al tools including those available through mobile phone apps.

- Predownloaded or recorded videos and other resources related to specific cases or scenarios that present a dilemma.

- Search engines, online videos or resources related to case studies.

STUDENT COMPETENCY "CURRICULAR GOALS (Al curricula or programmes of study should...)" "SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.)" "LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)" Ethics of AI "4.1.2 Embodied ethics - Students are expected to be able to develop a basic understanding of the ethical issues around Al , and the potential impact of Al on human rights, social justice, inclusion, equity and climate change within their local context and with regard to their personal lives. They will understand, and internalize the following key ethical principles, and will translate these in their reflective practices and uses of AI tools in their lives and learning: - Do no harm: Evaluating Al's regulatory compliance and potential to infringe on human rights - Proportionality: Assessing Al's benefits against risks and costs; evaluating contextappropriateness - Non-discrimination: Detecting biases and promoting inclusivity and sustainability" "- CG4.1.2.1 Illustrate dilemmas around AI and identify the main reasons behind ethical conflicts: Based on concrete Al tools, guide students to surface dilemma decisions that individual or corporate creators need to make in the design and development of Al (e.g. maximizing the scale of data collection versus protecting data ownership, recording users' private data for the training of Al models versus protecting their privacy, promoting machine control to generate profit versus guaranteeing the primacy of human agency, and prioritizing AI safety versus accelerating the iteration of AI). Support students to associate perspectives on these dilemmas with the reasons behind ethical conflicts around AI. - CG4.1.2.2 Facilitate scenario-based understandings of ethical principles on Al and their personal implications: Offer students opportunities to discuss age-appropriate real-world cases around the six core Al ethical principles: (1) 'do no harm', 2) proportionality, (3) non-discrimination, (4) sustainability, (5) human determination, and (6) transparency and explainability. Guide students to build a knowledge framework on the ethics of AI and practice" "- Case studies on scenarios containing controversies around AI: Present ageappropriate real-world or simulated scenarios, and guide students to surface controversies surrounding the Al tools and their uses. Discuss the main reasons behind such ethical conflicts and facilitate students to draw infographics or concept maps illustrating the core AI ethical principles. - Individual or group reflection on the personal implications of ethical dilemmas: Engage students in group discussion and opinion taking on ethical dilemmas that may arise from uses of AI in daily life and learning in local contexts (e.g. whether large language models should use the data of local communities in their training or not; to what extent Al has a negative environmental impact or mitigates climate change; how much of their privacy users should forego to exchange benefits of Al services). Guide students to present their opinions through age-appropriate formats such as essays, posters, drawings or storyboards. - Searching for and validating examples of 'Al for the public good': Organize individual or group scoping of examples of Al tools or approaches to the use of AI that" "- Unplugged learning settings and materials including print stories or case studies, worksheets and posters. - Locally available Al tools including those available through mobile phone apps. - Predownloaded or recorded videos and other resources related to specific cases or scenarios that present a dilemma. - Search engines, online videos or resources related to case studies."

| | STUDENT COMPETENCY | CURRICULAR GOALS (Al curricula or programmes of study should...) | SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) | LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) | | :---: | :---: | :---: | :---: | :---: | | Ethics of AI | 4.1.2 Embodied ethics - Students are expected to be able to develop a basic understanding of the ethical issues around Al , and the potential impact of Al on human rights, social justice, inclusion, equity and climate change within their local context and with regard to their personal lives. They will understand, and internalize the following key ethical principles, and will translate these in their reflective practices and uses of AI tools in their lives and learning: - Do no harm: Evaluating Al's regulatory compliance and potential to infringe on human rights - Proportionality: Assessing Al's benefits against risks and costs; evaluating contextappropriateness - Non-discrimination: Detecting biases and promoting inclusivity and sustainability | - CG4.1.2.1 Illustrate dilemmas around AI and identify the main reasons behind ethical conflicts: Based on concrete Al tools, guide students to surface dilemma decisions that individual or corporate creators need to make in the design and development of Al (e.g. maximizing the scale of data collection versus protecting data ownership, recording users' private data for the training of Al models versus protecting their privacy, promoting machine control to generate profit versus guaranteeing the primacy of human agency, and prioritizing AI safety versus accelerating the iteration of AI). Support students to associate perspectives on these dilemmas with the reasons behind ethical conflicts around AI. - CG4.1.2.2 Facilitate scenario-based understandings of ethical principles on Al and their personal implications: Offer students opportunities to discuss age-appropriate real-world cases around the six core Al ethical principles: (1) 'do no harm', 2) proportionality, (3) non-discrimination, (4) sustainability, (5) human determination, and (6) transparency and explainability. Guide students to build a knowledge framework on the ethics of AI and practice | - Case studies on scenarios containing controversies around AI: Present ageappropriate real-world or simulated scenarios, and guide students to surface controversies surrounding the Al tools and their uses. Discuss the main reasons behind such ethical conflicts and facilitate students to draw infographics or concept maps illustrating the core AI ethical principles. - Individual or group reflection on the personal implications of ethical dilemmas: Engage students in group discussion and opinion taking on ethical dilemmas that may arise from uses of AI in daily life and learning in local contexts (e.g. whether large language models should use the data of local communities in their training or not; to what extent Al has a negative environmental impact or mitigates climate change; how much of their privacy users should forego to exchange benefits of Al services). Guide students to present their opinions through age-appropriate formats such as essays, posters, drawings or storyboards. - Searching for and validating examples of 'Al for the public good': Organize individual or group scoping of examples of Al tools or approaches to the use of AI that | - Unplugged learning settings and materials including print stories or case studies, worksheets and posters. - Locally available Al tools including those available through mobile phone apps. - Predownloaded or recorded videos and other resources related to specific cases or scenarios that present a dilemma. - Search engines, online videos or resources related to case studies. |

STUDENT COMPETENCY 学生能力

课程目标（人工智能课程或学习计划应...）

CURRICULAR GOALS

(Al curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习设置。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Ethics of AI 人工智能的伦理

（理解人工智能对环境和社会的影响）- 人类决策：强调在人工智能使用中的人类代理和责任 - 透明度：倡导用户有权了解人工智能的操作和决策

(understanding Al's environmental and societal impacts)

- Human determination: Emphasizing human agency and accountability in Al use

- Transparency: advocating for the rights of users to understand Al operations and decisions

them in evaluating the AI tools being used in their lives and schools.
在评估他们生活和学校中使用的人工智能工具时。

- CG4.1.2.3 Guide the embodied reflection and internalization of ethical principles on Al: Guide students to understand the implications of ethical principles on Al for their human rights, data privacy, safety, human agency, as well as for equity, inclusion, social justice and environmental sustainability. Guide students to develop embodied comprehension of ethical principles; and offer opportunities to reflect on personal attitudes that can help address ethical challenges (e.g. advocating for inclusive interfaces for Al tools, promoting inclusion in Al and reporting discriminatory biases found in Al tools).
- CG4.1.2.3 指导对人工智能伦理原则的体现性反思和内化：引导学生理解人工智能伦理原则对人权、数据隐私、安全、人类代理权的影响，以及对公平、包容、社会正义和环境可持续性的影响。引导学生发展对伦理原则的体现性理解；并提供反思个人态度的机会，以帮助应对伦理挑战（例如，倡导人工智能工具的包容性界面，促进人工智能的包容性，并报告在人工智能工具中发现的歧视性偏见）。

support the public good, including promoting equity and inclusion for people with disabilities, preserving linguistic and cultural diversity, and increasing social justice and environmental sustainability. Guide students to collect evidence on and discuss examples that genuinely serve the public good; validate and categorize these examples.
支持公共利益，包括促进残疾人公平和包容，保护语言和文化多样性，以及增加社会正义和环境可持续性。引导学生收集证据并讨论真正服务于公共利益的例子；验证并分类这些例子。

Al techniques and applications
人工智能技术和应用

4.1.3 人工智能基础 - 学生应当发展对人工智能的基本知识、理解和技能，特别是在数据和算法方面，并理解逐步深入所需的跨学科基础知识的重要性

4.1.3 Al foundations

- Students are expected to develop basic knowledge, understanding and skills on Al, particularly with respect to data and algorithms, and understand the importance of the interdisciplinary foundational knowledge required for gradually deepening

- CG4.1.3.1 Exemplify the definition and scope of AI: Based on examples of Al tools (e.g. for facial recognition, social media recommendations, pattern analyses underlying scientific data, medical diagnoses, self-driving cars and predicting the risk of loan defaults), facilitate students to understand what Al is and is not; guide students to find and share exemplar tools under the main categories of AI technologies and explain
- CG4.1.3.1 例证人工智能的定义和范围：基于人工智能工具的示例（例如面部识别、社交媒体推荐、科学数据的模式分析、医学诊断、自动驾驶汽车和预测贷款违约风险），帮助学生理解什么是人工智能，什么不是；引导学生寻找和分享主要类别的人工智能技术下的示例工具并进行解释

- Example-based definition and scope of AI: Investigate and experiment with examples of AI tools (e.g. in the medical field using supervised learning and image classification for cancer diagnosis, or in business contexts using natural language processing and generative Al for automated minute-taking and composing literature reviews). Based on selected examples, help students to understand what Al is and is not, and the main categories of AI technologies adopted in daily life, as well
- 基于示例的人工智能定义和范围：调查和实验人工智能工具的示例（例如在医学领域使用监督学习和图像分类进行癌症诊断，或在商业环境中使用自然语言处理和生成式人工智能进行自动记录和撰写文献综述）。基于所选示例，帮助学生理解什么是人工智能，什么不是，以及日常生活中采用的主要人工智能技术类别。

- 断开连接的学习环境和资源，包括教科书、论文、工作表和印刷材料。 - 在线或下载的视频和其他媒体，介绍人工智能的创新或工具。 - 本地可用的人工智能工具，包括基本的人工智能辅助工具

- Unplugged learning settings and resources, including textbooks, essays, worksheets and printed materials.

- Online or downloaded videos and other media introducing Al innovations or tools.

- Locally available Al tools including basic Al-assisted

STUDENT COMPETENCY "CURRICULAR GOALS (Al curricula or programmes of study should...)" "SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.)" "LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)" Ethics of AI "(understanding Al's environmental and societal impacts) - Human determination: Emphasizing human agency and accountability in Al use - Transparency: advocating for the rights of users to understand Al operations and decisions" "them in evaluating the AI tools being used in their lives and schools. - CG4.1.2.3 Guide the embodied reflection and internalization of ethical principles on Al: Guide students to understand the implications of ethical principles on Al for their human rights, data privacy, safety, human agency, as well as for equity, inclusion, social justice and environmental sustainability. Guide students to develop embodied comprehension of ethical principles; and offer opportunities to reflect on personal attitudes that can help address ethical challenges (e.g. advocating for inclusive interfaces for Al tools, promoting inclusion in Al and reporting discriminatory biases found in Al tools)." support the public good, including promoting equity and inclusion for people with disabilities, preserving linguistic and cultural diversity, and increasing social justice and environmental sustainability. Guide students to collect evidence on and discuss examples that genuinely serve the public good; validate and categorize these examples. Al techniques and applications "4.1.3 Al foundations - Students are expected to develop basic knowledge, understanding and skills on Al, particularly with respect to data and algorithms, and understand the importance of the interdisciplinary foundational knowledge required for gradually deepening" - CG4.1.3.1 Exemplify the definition and scope of AI: Based on examples of Al tools (e.g. for facial recognition, social media recommendations, pattern analyses underlying scientific data, medical diagnoses, self-driving cars and predicting the risk of loan defaults), facilitate students to understand what Al is and is not; guide students to find and share exemplar tools under the main categories of AI technologies and explain - Example-based definition and scope of AI: Investigate and experiment with examples of AI tools (e.g. in the medical field using supervised learning and image classification for cancer diagnosis, or in business contexts using natural language processing and generative Al for automated minute-taking and composing literature reviews). Based on selected examples, help students to understand what Al is and is not, and the main categories of AI technologies adopted in daily life, as well "- Unplugged learning settings and resources, including textbooks, essays, worksheets and printed materials. - Online or downloaded videos and other media introducing Al innovations or tools. - Locally available Al tools including basic Al-assisted"

| | STUDENT COMPETENCY | CURRICULAR GOALS (Al curricula or programmes of study should...) | SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) | LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) | | :---: | :---: | :---: | :---: | :---: | | Ethics of AI | (understanding Al's environmental and societal impacts) - Human determination: Emphasizing human agency and accountability in Al use - Transparency: advocating for the rights of users to understand Al operations and decisions | them in evaluating the AI tools being used in their lives and schools. - CG4.1.2.3 Guide the embodied reflection and internalization of ethical principles on Al: Guide students to understand the implications of ethical principles on Al for their human rights, data privacy, safety, human agency, as well as for equity, inclusion, social justice and environmental sustainability. Guide students to develop embodied comprehension of ethical principles; and offer opportunities to reflect on personal attitudes that can help address ethical challenges (e.g. advocating for inclusive interfaces for Al tools, promoting inclusion in Al and reporting discriminatory biases found in Al tools). | support the public good, including promoting equity and inclusion for people with disabilities, preserving linguistic and cultural diversity, and increasing social justice and environmental sustainability. Guide students to collect evidence on and discuss examples that genuinely serve the public good; validate and categorize these examples. | | | Al techniques and applications | 4.1.3 Al foundations - Students are expected to develop basic knowledge, understanding and skills on Al, particularly with respect to data and algorithms, and understand the importance of the interdisciplinary foundational knowledge required for gradually deepening | - CG4.1.3.1 Exemplify the definition and scope of AI: Based on examples of Al tools (e.g. for facial recognition, social media recommendations, pattern analyses underlying scientific data, medical diagnoses, self-driving cars and predicting the risk of loan defaults), facilitate students to understand what Al is and is not; guide students to find and share exemplar tools under the main categories of AI technologies and explain | - Example-based definition and scope of AI: Investigate and experiment with examples of AI tools (e.g. in the medical field using supervised learning and image classification for cancer diagnosis, or in business contexts using natural language processing and generative Al for automated minute-taking and composing literature reviews). Based on selected examples, help students to understand what Al is and is not, and the main categories of AI technologies adopted in daily life, as well | - Unplugged learning settings and resources, including textbooks, essays, worksheets and printed materials. - Online or downloaded videos and other media introducing Al innovations or tools. - Locally available Al tools including basic Al-assisted |

STUDENT COMPETENCY 学生能力

课程目标（人工智能课程或学习计划应...）

CURRICULAR GOALS

(Al curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习设置。）

LEARNING

ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Al techniques and applications
人工智能技术和应用

对数据和算法的理解。学生还应该能够将对人工智能的概念知识与他们在社会和日常生活中的活动联系起来，通过理解人工智能的工作原理以及人工智能如何与人类互动，具体化以人为本的思维方式和伦理原则。

understanding of data and algorithms.

Students should also be able to connect conceptual knowledge on AI with their activities in society and daily life, concretizing a human-centred mindset and ethical principles through an understanding of how Al works and how AI interacts with humans.

their main functions and techniques in an ageappropriate manner.
他们的主要功能和技术以适合年龄的方式。

- CG4.1.3.2 Develop conceptual knowledge on how Al is trained based on data and algorithms: Foster students' examplebased abstraction of conceptual knowledge on how machine-learning models are trained using data and algorithms; help students to develop an age-appropriate understanding of the three types of AI algorithms, namely, supervised learning, unsupervised learning and reinforcement learning. This should include how data behind the three types of Al algorithms are acquired and labelled. Debunk the claims that AI will automate the programming of algorithms and that humans do not need to learn about algorithms.
- CG4.1.3.2 基于数据和算法开发对人工智能训练的概念知识：培养学生基于实例的抽象概念知识，了解机器学习模型如何使用数据和算法进行训练；帮助学生发展适合其年龄的对三种类型人工智能算法的理解，即监督学习、无监督学习和强化学习。这应包括三种类型人工智能算法背后的数据是如何获取和标记的。揭穿人工智能将自动化算法编程以及人类不需要学习算法的说法。

- CG4.1.3.3 Foster openminded thinking on AI and an interdisciplinary foundation for AI: Enable students to gain appropriate knowledge on Al methods and research topics such as the uses of artificial neural networks and the difference between strong Al and weak AI. Offer extended learning opportunities on data and algorithms to students who have strong interests and abilities in AI. Guide students to understand the interplay between knowledge
- CG4.1.3.3 培养对人工智能的开放思维和跨学科基础：使学生获得适当的人工智能方法和研究主题的知识，例如人工神经网络的应用以及强人工智能与弱人工智能之间的区别。为对人工智能有强烈兴趣和能力的学生提供扩展学习机会。引导学生理解知识之间的相互作用。

as in economic and social activities. Guide students to explore the key steps of the AI life cycle; where appropriate, draw a diagram of the cycle for particular AI systems and label the key AI techniques used.
在经济和社会活动中。引导学生探索人工智能生命周期的关键步骤；在适当的情况下，为特定的人工智能系统绘制生命周期图，并标注所使用的关键人工智能技术。

- Spiral learning from examples to abstract concepts and from concepts to specific techniques: Use selected examples to guide students to abstract how a machine learning model is trained, including the steps of problem definition, data collection, data processing, training, evaluation, deployment and iteration based on tests and feedback. Support students' development of age-appropriate knowledge about (and where possible, basic operational skills on) the use of Al techniques involving datasets, algorithms, Al architectures, setting up of computing environments, design of functionalities and interfaces, and planning of deployment scenarios.
- 从示例到抽象概念，再从概念到具体技术的螺旋学习：使用选定的示例引导学生抽象出机器学习模型的训练过程，包括问题定义、数据收集、数据处理、训练、评估、部署以及基于测试和反馈的迭代步骤。支持学生在使用涉及数据集、算法、人工智能架构、计算环境设置、功能和界面设计以及部署场景规划的人工智能技术方面发展适合其年龄的知识（在可能的情况下，基本操作技能）。

- Case analysis of innovative Al tools and innovative uses of Al: Organize students to search for potential innovative Al tools and/or innovative uses of AI; guide students to identify the key techniques and main categories of Al used in these applications. Facilitate them to write an argumentative essay or provide an oral defense on the extent to
- 创新人工智能工具和创新人工智能应用的案例分析：组织学生搜索潜在的创新人工智能工具和/或创新人工智能应用；指导学生识别这些应用中使用的关键技术和主要类别的人工智能。帮助他们撰写一篇论证性文章或提供口头辩护，讨论其程度。

安装在智能手机上的应用程序。 - 在线人工智能工具，例如图像和/或视频创作工具、生成性人工智能模型和社交媒体上的视频推荐。

applications installed on smartphones.

- Online AI tools, for example image and/or video creators, generative Al model and video recommendations on social media.

STUDENT COMPETENCY "CURRICULAR GOALS (Al curricula or programmes of study should...)" "SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.)" "LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)" Al techniques and applications "understanding of data and algorithms. Students should also be able to connect conceptual knowledge on AI with their activities in society and daily life, concretizing a human-centred mindset and ethical principles through an understanding of how Al works and how AI interacts with humans." "their main functions and techniques in an ageappropriate manner. - CG4.1.3.2 Develop conceptual knowledge on how Al is trained based on data and algorithms: Foster students' examplebased abstraction of conceptual knowledge on how machine-learning models are trained using data and algorithms; help students to develop an age-appropriate understanding of the three types of AI algorithms, namely, supervised learning, unsupervised learning and reinforcement learning. This should include how data behind the three types of Al algorithms are acquired and labelled. Debunk the claims that AI will automate the programming of algorithms and that humans do not need to learn about algorithms. - CG4.1.3.3 Foster openminded thinking on AI and an interdisciplinary foundation for AI: Enable students to gain appropriate knowledge on Al methods and research topics such as the uses of artificial neural networks and the difference between strong Al and weak AI. Offer extended learning opportunities on data and algorithms to students who have strong interests and abilities in AI. Guide students to understand the interplay between knowledge" "as in economic and social activities. Guide students to explore the key steps of the AI life cycle; where appropriate, draw a diagram of the cycle for particular AI systems and label the key AI techniques used. - Spiral learning from examples to abstract concepts and from concepts to specific techniques: Use selected examples to guide students to abstract how a machine learning model is trained, including the steps of problem definition, data collection, data processing, training, evaluation, deployment and iteration based on tests and feedback. Support students' development of age-appropriate knowledge about (and where possible, basic operational skills on) the use of Al techniques involving datasets, algorithms, Al architectures, setting up of computing environments, design of functionalities and interfaces, and planning of deployment scenarios. - Case analysis of innovative Al tools and innovative uses of Al: Organize students to search for potential innovative Al tools and/or innovative uses of AI; guide students to identify the key techniques and main categories of Al used in these applications. Facilitate them to write an argumentative essay or provide an oral defense on the extent to" "applications installed on smartphones. - Online AI tools, for example image and/or video creators, generative Al model and video recommendations on social media."

| | STUDENT COMPETENCY | CURRICULAR GOALS (Al curricula or programmes of study should...) | SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) | LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) | | :---: | :---: | :---: | :---: | :---: | | Al techniques and applications | understanding of data and algorithms. Students should also be able to connect conceptual knowledge on AI with their activities in society and daily life, concretizing a human-centred mindset and ethical principles through an understanding of how Al works and how AI interacts with humans. | their main functions and techniques in an ageappropriate manner. - CG4.1.3.2 Develop conceptual knowledge on how Al is trained based on data and algorithms: Foster students' examplebased abstraction of conceptual knowledge on how machine-learning models are trained using data and algorithms; help students to develop an age-appropriate understanding of the three types of AI algorithms, namely, supervised learning, unsupervised learning and reinforcement learning. This should include how data behind the three types of Al algorithms are acquired and labelled. Debunk the claims that AI will automate the programming of algorithms and that humans do not need to learn about algorithms. - CG4.1.3.3 Foster openminded thinking on AI and an interdisciplinary foundation for AI: Enable students to gain appropriate knowledge on Al methods and research topics such as the uses of artificial neural networks and the difference between strong Al and weak AI. Offer extended learning opportunities on data and algorithms to students who have strong interests and abilities in AI. Guide students to understand the interplay between knowledge | as in economic and social activities. Guide students to explore the key steps of the AI life cycle; where appropriate, draw a diagram of the cycle for particular AI systems and label the key AI techniques used. - Spiral learning from examples to abstract concepts and from concepts to specific techniques: Use selected examples to guide students to abstract how a machine learning model is trained, including the steps of problem definition, data collection, data processing, training, evaluation, deployment and iteration based on tests and feedback. Support students' development of age-appropriate knowledge about (and where possible, basic operational skills on) the use of Al techniques involving datasets, algorithms, Al architectures, setting up of computing environments, design of functionalities and interfaces, and planning of deployment scenarios. - Case analysis of innovative Al tools and innovative uses of Al: Organize students to search for potential innovative Al tools and/or innovative uses of AI; guide students to identify the key techniques and main categories of Al used in these applications. Facilitate them to write an argumentative essay or provide an oral defense on the extent to | applications installed on smartphones. - Online AI tools, for example image and/or video creators, generative Al model and video recommendations on social media. |

STUDENT COMPETENCY 学生能力

课程目标（人工智能课程或学习计划应...）

CURRICULAR GOALS

(Al curricula or programmes of study should...)

SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.)
建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

学习环境（可以提供和调整以下学习环境。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Al techniques and applications
人工智能技术和应用

on Al and knowledge in STEM, languages and social studies, and invite them to solidify related interdisciplinary knowledge and the reflections on the reciprocal impact of Al on related subjects.
关于人工智能及其在 STEM、语言和社会研究中的知识，并邀请他们巩固相关的跨学科知识以及对人工智能对相关学科的相互影响的反思。

- CG4.1.3.4 Concretize human-centred considerations in the design and use of AI: Organize tool-based reflections on Al to give form to students' understanding of its impact on life, work and societal relationships. Highlight humans' roles in the key steps of the AI life cycle (e.g. researchers, architecture engineers, data engineers, data workers, beta testers, regulators of ethics and safety, specialists in human-Al interfaces and auditors of system compliance). Guide students toward a deep familiarity with the main ethical issues related to the use of data for training AI systems.
- CG4.1.3.4 在 AI 的设计和使用中具体化以人为本的考虑：组织基于工具的反思，以帮助学生理解 AI 对生活、工作和社会关系的影响。强调人类在 AI 生命周期的关键步骤中的角色（例如，研究人员、架构工程师、数据工程师、数据工作者、测试人员、伦理和安全监管者、人机界面专家以及系统合规审计员）。引导学生深入了解与使用数据训练 AI 系统相关的主要伦理问题。

which these Al technologies may help humans engineer innovations in their personal practices, economic or business models, or social services, and/or the risks that specific Al technologies may pose to ethical principles and human agency.
这些 AI 技术可能帮助人类在个人实践、经济或商业模式或社会服务中进行创新，以及/或者特定 AI 技术可能对伦理原则和人类自主性构成的风险。

Solidifying multidisciplinary foundation for Al with a specific focus on mathematics: Based on lectures and problem-based inquiry, help students grasp that modern AI systems are rooted in mathematics, and learning about data and algorithms requires a strong command of mathematics and a multidisciplinary knowledge set. Nurture students' essential mathematical and interdisciplinary skills for AI development, including relevant material on algebra, probability and statistics, data structures and algorithms such as K-nearest neighbours, K-means clustering, linear regression and CART/decision trees. Cultivate students' higherlevel knowledge on linear algebra for complex data representation and matrix mathematics, calculus for back propagation and gradient descent for understanding machine learning and neural networks. Support students to solidify and extend their other multidisciplinary foundational knowledge as well, especially in science, technology and engineering.
夯实人工智能的多学科基础，特别关注数学：通过讲座和基于问题的探究，帮助学生理解现代人工智能系统根植于数学，学习数据和算法需要扎实的数学基础和多学科知识。培养学生在人工智能开发中所需的基本数学和跨学科技能，包括代数、概率与统计、数据结构和算法（如 K 近邻、K 均值聚类、线性回归和 CART/决策树）等相关内容。培养学生在复杂数据表示和矩阵数学方面的线性代数的高级知识，微积分用于反向传播和梯度下降，以理解机器学习和神经网络。支持学生巩固和扩展他们的其他多学科基础知识，特别是在科学、技术和工程方面。

STUDENT COMPETENCY "CURRICULAR GOALS (Al curricula or programmes of study should...)" SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) "LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)" Al techniques and applications "on Al and knowledge in STEM, languages and social studies, and invite them to solidify related interdisciplinary knowledge and the reflections on the reciprocal impact of Al on related subjects. - CG4.1.3.4 Concretize human-centred considerations in the design and use of AI: Organize tool-based reflections on Al to give form to students' understanding of its impact on life, work and societal relationships. Highlight humans' roles in the key steps of the AI life cycle (e.g. researchers, architecture engineers, data engineers, data workers, beta testers, regulators of ethics and safety, specialists in human-Al interfaces and auditors of system compliance). Guide students toward a deep familiarity with the main ethical issues related to the use of data for training AI systems." "which these Al technologies may help humans engineer innovations in their personal practices, economic or business models, or social services, and/or the risks that specific Al technologies may pose to ethical principles and human agency. Solidifying multidisciplinary foundation for Al with a specific focus on mathematics: Based on lectures and problem-based inquiry, help students grasp that modern AI systems are rooted in mathematics, and learning about data and algorithms requires a strong command of mathematics and a multidisciplinary knowledge set. Nurture students' essential mathematical and interdisciplinary skills for AI development, including relevant material on algebra, probability and statistics, data structures and algorithms such as K-nearest neighbours, K-means clustering, linear regression and CART/decision trees. Cultivate students' higherlevel knowledge on linear algebra for complex data representation and matrix mathematics, calculus for back propagation and gradient descent for understanding machine learning and neural networks. Support students to solidify and extend their other multidisciplinary foundational knowledge as well, especially in science, technology and engineering."

| | STUDENT COMPETENCY | CURRICULAR GOALS (Al curricula or programmes of study should...) | SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) | LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) | | :---: | :---: | :---: | :---: | :---: | | Al techniques and applications | | on Al and knowledge in STEM, languages and social studies, and invite them to solidify related interdisciplinary knowledge and the reflections on the reciprocal impact of Al on related subjects. - CG4.1.3.4 Concretize human-centred considerations in the design and use of AI: Organize tool-based reflections on Al to give form to students' understanding of its impact on life, work and societal relationships. Highlight humans' roles in the key steps of the AI life cycle (e.g. researchers, architecture engineers, data engineers, data workers, beta testers, regulators of ethics and safety, specialists in human-Al interfaces and auditors of system compliance). Guide students toward a deep familiarity with the main ethical issues related to the use of data for training AI systems. | which these Al technologies may help humans engineer innovations in their personal practices, economic or business models, or social services, and/or the risks that specific Al technologies may pose to ethical principles and human agency. Solidifying multidisciplinary foundation for Al with a specific focus on mathematics: Based on lectures and problem-based inquiry, help students grasp that modern AI systems are rooted in mathematics, and learning about data and algorithms requires a strong command of mathematics and a multidisciplinary knowledge set. Nurture students' essential mathematical and interdisciplinary skills for AI development, including relevant material on algebra, probability and statistics, data structures and algorithms such as K-nearest neighbours, K-means clustering, linear regression and CART/decision trees. Cultivate students' higherlevel knowledge on linear algebra for complex data representation and matrix mathematics, calculus for back propagation and gradient descent for understanding machine learning and neural networks. Support students to solidify and extend their other multidisciplinary foundational knowledge as well, especially in science, technology and engineering. | |

STUDENT COMPETENCY 学生能力

课程目标（人工智能课程或学习计划应...）

CURRICULAR GOALS

(AI curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习设置。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Al system design 人工智能系统设计

4.1.4 Problem scoping 4.1.4 问题范围界定

- Students are expected to be able to understand the importance of 'Al problem scoping' as the starting point for Al innovation. They are expected to be able to examine whether Al should be used in certain situations from legal, ethical and logical perspectives; students are able to define the boundaries, goals and constraints of a problem before attempting to train an AI model to solve it; students are also expected to acquire the knowledge and project-planning skills needed in order to conceptualize and construct an Al system, including by assessing the appropriateness of different AI techniques, defining the need for data, and devising test and feedback metrics.
- 学生应能够理解“人工智能问题范围界定”作为人工智能创新起点的重要性。他们应能够从法律、伦理和逻辑的角度审视在某些情况下是否应使用人工智能；学生能够在尝试训练人工智能模型解决问题之前，定义问题的边界、目标和约束；学生还应掌握概念化和构建人工智能系统所需的知识和项目规划技能，包括评估不同人工智能技术的适用性、定义数据需求以及制定测试和反馈指标。

- CG4.1.4.1 Scaffold critical thinking skills on when Al should not be used: Drawing from examples, guide students to develop critical analysis skills to examine reasons why AI should or should not be used to address certain real-world challenges (e.g. improving institutional productivity, the sustainable development of communities, or the precision and efficiency of human decision-making) with reference to human and environmental implications. Provide clarity on when, and under what conditions, Al cannot and/ or should not be applied to problems (e.g. where non-Al solutions would offer the same performance with lower ethical risk and environmental impact, or where the use of AI would weaken human consciousness or manipulate human actions).
- CG4.1.4.1 在何时不应使用人工智能的基础上搭建批判性思维技能：通过实例引导学生发展批判性分析技能，审视在解决某些现实世界挑战时（例如，提高机构生产力、社区的可持续发展或人类决策的精确性和效率）使用人工智能的理由，并参考人类和环境的影响。明确何时以及在什么条件下，人工智能不能和/或不应应用于问题（例如，在非人工智能解决方案能够以更低的伦理风险和环境影响提供相同性能的情况下，或在使用人工智能会削弱人类意识或操控人类行为的情况下）。

- CG4.1.4.2 Support the acquisition and reinforcement of skills in scoping a problem to be solved by an AI system: Based on a simulation project, support the learning and practice of skills to identify and scope a problem that should and could possibly be solved by building a new AI model (e.g. training an Al model on a minority language to better serve its community, or building a model for
- CG4.1.4.2 支持在 AI 系统中界定待解决问题的技能的获取和强化：基于一个模拟项目，支持识别和界定一个应当且可能通过构建新的人工智能模型来解决的问题的学习和实践技能（例如，训练一个人工智能模型以更好地服务于其社区的少数语言，或构建一个模型以解决）。

- Simulating the review of project proposals: Organize students to simulate the review of a project proposal and justification process. The proposals could, for example, be on building or selecting an AI system. Conduct a debate on whether AI should or should not be used in the project to solve the problem, considering factors such as the availability of sufficient training data, ethical implications, environmental impact and whether non-AI solutions could achieve similar outcomes with fewer risks. Guide students to outline a checkbox for the review.
- 模拟项目提案的评审：组织学生模拟项目提案及其论证过程的评审。提案可以是关于构建或选择一个 AI 系统。例如，进行一场辩论，讨论在项目中是否应该使用 AI 来解决问题，考虑因素包括是否有足够的训练数据、伦理影响、环境影响，以及非 AI 解决方案是否能够以更少的风险实现类似的结果。指导学生制定评审的检查表。

- Simulating the problemscoping and justification for the design of new AI system: Facilitate students to research problems in their daily lives or communities (e.g. at school or in volunteer work) and identify a problem that could potentially be addressed by AI (e.g. automatic watering the school garden or helping a hard-of-hearing grandparent to detect alarms). Support students to scope and define the problem by anticipating the key features including Al algorithms and datasets, and produce a corresponding problem statement.
- 模拟新 AI 系统设计的问题识别和论证：引导学生研究他们日常生活或社区中的问题（例如在学校或志愿工作中），并识别一个可以通过 AI 潜在解决的问题（例如自动浇灌学校花园或帮助听力障碍的祖父母检测警报）。支持学生界定和定义问题，预见关键特征，包括 AI 算法和数据集，并产生相应的问题陈述。

- Data preprocessing lab: Using a basic dataset and the architecture of an existing AI model, organize experiments on training the model
- 数据预处理实验室：使用基本数据集和现有 AI 模型的架构，组织对模型训练的实验

- 无插电学习环境，包括工作表、基于纸张的案例研究和 AI 系统设计的原型或计划的打印件。 - 具有互联网连接的数字设备。 - 选定的在线 AI 系统。

- Unplugged learning settings including worksheets, paperbased case studies, and printouts of prototypes or plans for Al system design.

- Digital devices with an internet connection.

- Selected online AI systems.

STUDENT COMPETENCY "CURRICULAR GOALS (AI curricula or programmes of study should...)" "SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.)" "LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)" Al system design "4.1.4 Problem scoping - Students are expected to be able to understand the importance of 'Al problem scoping' as the starting point for Al innovation. They are expected to be able to examine whether Al should be used in certain situations from legal, ethical and logical perspectives; students are able to define the boundaries, goals and constraints of a problem before attempting to train an AI model to solve it; students are also expected to acquire the knowledge and project-planning skills needed in order to conceptualize and construct an Al system, including by assessing the appropriateness of different AI techniques, defining the need for data, and devising test and feedback metrics." "- CG4.1.4.1 Scaffold critical thinking skills on when Al should not be used: Drawing from examples, guide students to develop critical analysis skills to examine reasons why AI should or should not be used to address certain real-world challenges (e.g. improving institutional productivity, the sustainable development of communities, or the precision and efficiency of human decision-making) with reference to human and environmental implications. Provide clarity on when, and under what conditions, Al cannot and/ or should not be applied to problems (e.g. where non-Al solutions would offer the same performance with lower ethical risk and environmental impact, or where the use of AI would weaken human consciousness or manipulate human actions). - CG4.1.4.2 Support the acquisition and reinforcement of skills in scoping a problem to be solved by an AI system: Based on a simulation project, support the learning and practice of skills to identify and scope a problem that should and could possibly be solved by building a new AI model (e.g. training an Al model on a minority language to better serve its community, or building a model for" "- Simulating the review of project proposals: Organize students to simulate the review of a project proposal and justification process. The proposals could, for example, be on building or selecting an AI system. Conduct a debate on whether AI should or should not be used in the project to solve the problem, considering factors such as the availability of sufficient training data, ethical implications, environmental impact and whether non-AI solutions could achieve similar outcomes with fewer risks. Guide students to outline a checkbox for the review. - Simulating the problemscoping and justification for the design of new AI system: Facilitate students to research problems in their daily lives or communities (e.g. at school or in volunteer work) and identify a problem that could potentially be addressed by AI (e.g. automatic watering the school garden or helping a hard-of-hearing grandparent to detect alarms). Support students to scope and define the problem by anticipating the key features including Al algorithms and datasets, and produce a corresponding problem statement. - Data preprocessing lab: Using a basic dataset and the architecture of an existing AI model, organize experiments on training the model" "- Unplugged learning settings including worksheets, paperbased case studies, and printouts of prototypes or plans for Al system design. - Digital devices with an internet connection. - Selected online AI systems."

| | STUDENT COMPETENCY | CURRICULAR GOALS (AI curricula or programmes of study should...) | SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) | LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) | | :---: | :---: | :---: | :---: | :---: | | Al system design | 4.1.4 Problem scoping - Students are expected to be able to understand the importance of 'Al problem scoping' as the starting point for Al innovation. They are expected to be able to examine whether Al should be used in certain situations from legal, ethical and logical perspectives; students are able to define the boundaries, goals and constraints of a problem before attempting to train an AI model to solve it; students are also expected to acquire the knowledge and project-planning skills needed in order to conceptualize and construct an Al system, including by assessing the appropriateness of different AI techniques, defining the need for data, and devising test and feedback metrics. | - CG4.1.4.1 Scaffold critical thinking skills on when Al should not be used: Drawing from examples, guide students to develop critical analysis skills to examine reasons why AI should or should not be used to address certain real-world challenges (e.g. improving institutional productivity, the sustainable development of communities, or the precision and efficiency of human decision-making) with reference to human and environmental implications. Provide clarity on when, and under what conditions, Al cannot and/ or should not be applied to problems (e.g. where non-Al solutions would offer the same performance with lower ethical risk and environmental impact, or where the use of AI would weaken human consciousness or manipulate human actions). - CG4.1.4.2 Support the acquisition and reinforcement of skills in scoping a problem to be solved by an AI system: Based on a simulation project, support the learning and practice of skills to identify and scope a problem that should and could possibly be solved by building a new AI model (e.g. training an Al model on a minority language to better serve its community, or building a model for | - Simulating the review of project proposals: Organize students to simulate the review of a project proposal and justification process. The proposals could, for example, be on building or selecting an AI system. Conduct a debate on whether AI should or should not be used in the project to solve the problem, considering factors such as the availability of sufficient training data, ethical implications, environmental impact and whether non-AI solutions could achieve similar outcomes with fewer risks. Guide students to outline a checkbox for the review. - Simulating the problemscoping and justification for the design of new AI system: Facilitate students to research problems in their daily lives or communities (e.g. at school or in volunteer work) and identify a problem that could potentially be addressed by AI (e.g. automatic watering the school garden or helping a hard-of-hearing grandparent to detect alarms). Support students to scope and define the problem by anticipating the key features including Al algorithms and datasets, and produce a corresponding problem statement. - Data preprocessing lab: Using a basic dataset and the architecture of an existing AI model, organize experiments on training the model | - Unplugged learning settings including worksheets, paperbased case studies, and printouts of prototypes or plans for Al system design. - Digital devices with an internet connection. - Selected online AI systems. |

STUDENT COMPETENCY 学生能力

课程目标（AI 课程或学习计划应...）

CURRICULAR GOALS

(AI curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)
学习环境（可以提供和调整以下学习环境。）

Al system design 人工智能系统设计

the automated tracking of migration across target regions). Students can sharpen their analytical skills by formulating problem statements that can help avoid wastage of time and effort on poorly defined problems.
自动跟踪目标区域的迁移）。学生可以通过制定问题陈述来提高他们的分析能力，这可以帮助避免在定义不清的问题上浪费时间和精力。

- CG4.1.4.3 Develop skills on assessing AI systems' need for data, algorithms and computing resources: Offer opportunities for students to develop planning skills by assessing the need for data, algorithms and programming languages, software, computing capabilities and hardware; study the feasibility of an Al project in terms of the data available given the regulatory and ethical restrictions and the total costs of the required processing and engineering of data, computing capabilities and hardware.
- CG4.1.4.3 发展评估人工智能系统对数据、算法和计算资源需求的技能：为学生提供机会，通过评估数据、算法和编程语言、软件、计算能力和硬件的需求来发展规划技能；研究在监管和伦理限制以及所需数据处理和工程的总成本的情况下，人工智能项目的可行性。

based on variations of the dataset (e.g. a challenge of classifying mystery images). Support students to apply various data preprocessing techniques, such as adjusting the coding (e.g. data augmentation, handling outliers and analysing dataset skew/imbalance). Support them to train the model based on the modified datasets and observe how the data preprocessing has affected the model's performance.
基于数据集的变体（例如，分类神秘图像的挑战）。支持学生应用各种数据预处理技术，例如调整编码（例如，数据增强、处理异常值和分析数据集偏斜/不平衡）。支持他们基于修改后的数据集训练模型，并观察数据预处理如何影响模型的性能。

4.2 Level 2: Apply
4.2 第二级：应用

The overall goal of the ‘Apply’ level is for students to construct a solid and transferable conceptual knowledge structure and associated skill sets on Al and to habituate their application of the human-centred mindset and ethical principles to guide the assessment, learning and practice of AI tools. The curricular goals in Table 3 aim to guide the charting of a core set of value orientations, practical ethical principles and methodological knowledge that can be used to tailor curricular modules and
“应用”级别的总体目标是让学生构建一个扎实且可转移的概念知识结构和相关技能集，并习惯于将以人为本的思维方式和伦理原则应用于指导 AI 工具的评估、学习和实践。表 3 中的课程目标旨在指导核心价值取向、实用伦理原则和方法论知识的绘制，这些可以用于定制课程模块和
specify exit competencies for all students. The suggested pedagogical methods are intended to catalyse problem-based inquiry of conceptual knowledge and task-based appreciation of operational skills while integrating strategies to maintain students’ curiosity for further study. Providing desirable learning environments at the 'Apply’level involves setting up hardware, software and applications to support practices of AI operation and co-creation, with considerations of open source options.
指定所有学生的毕业能力。建议的教学方法旨在催化对概念知识的基于问题的探究和对操作技能的任务导向欣赏，同时整合策略以保持学生对进一步学习的好奇心。在“应用”级别提供理想的学习环境涉及设置硬件、软件和应用程序，以支持 AI 操作和共同创造的实践，同时考虑开源选项。

Table 3. Competency blocks for level 2: Apply
表 3. 第二级别：应用的能力模块

STUDENT COMPETENCY 学生能力

课程目标（所有课程或学习计划应...）

CURRICULAR GOALS

(Al curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习环境。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Humancentred mindset 以人为本的思维方式

4.2.1 人类责任 - 学生应能够认识到人类责任是人工智能创作者和人工智能服务提供者的法律义务，并理解在设计和使用人工智能时他们应承担哪些人类责任。他们还应增强意识，认识到在人类决策中使用人工智能时，人类责任是一种法律和社会责任。

4.2.1 Human

accountability

- Students are expected to be able to recognize that human accountabilities are the legal obligations of AI creators and AI service providers, and understand what human accountabilities they should assume during the design and use of AI. They should also foster an awareness that human accountability is a legal and social responsibility when using Al to assist decisions on that affect humanity

- CG4.2.1.1 Develop a view that human accountability is a legal obligation of AI creators and AI service providers: Leveraging prior knowledge on the human-led AI life cycle and real-world lawsuits, guide students to understand that human AI creators and service providers, and institutions deploying AI tools, are accountable for legal issues, violations and infringements that the Al system or service may cause. Explain how to hold Al creators, providers and institutional users to assume human accountability for safety incidents, ethical risks in designing and training AI, and misuses of the Al service to control users. Guide students to understand what human accountabilities they should
- CG4.2.1.1 发展对人类责任是人工智能创作者和人工智能服务提供者法律义务的看法：利用对人类主导的人工智能生命周期和现实世界诉讼的先前知识，引导学生理解人工智能创作者和服务提供者，以及部署人工智能工具的机构，对人工智能系统或服务可能造成的法律问题、违规和侵权行为负责。解释如何让人工智能创作者、提供者和机构用户承担安全事件、设计和训练人工智能中的伦理风险以及滥用人工智能服务以控制用户的人类责任。引导学生理解他们应承担哪些人类责任。

- Writing guidelines on human accountability for Al creators and service providers: Facilitate students to play the roles of Al creators and data owners and discuss their key legal and ethical accountabilities in terms of maintaining human control of the collection and processing of data, training AI models, designing functionalities and interfaces, deployment of Al systems, and monitoring and feedback loops. Guide them to write self-discipline guidelines for their studies on the design, training and iteration of Al systems, holding AI creators to account for protecting the rights of data owners and Al users.
- 为人工智能创作者和服务提供者制定人类问责制的指导方针：促使学生扮演人工智能创作者和数据所有者的角色，并讨论他们在维护人类对数据收集和处理、训练人工智能模型、设计功能和界面、部署人工智能系统以及监控和反馈循环方面的关键法律和伦理责任。指导他们为自己在人工智能系统设计、训练和迭代方面的研究撰写自律指导方针，要求人工智能创作者对保护数据所有者和人工智能用户的权利负责。

- 不插电和/或离线学习环境和资源，包括基于印刷的案例研究、角色扮演剧本、视频、工作表和翻转图表。 - 在线人工智能工具，例如学习管理系统、社交媒体平台和生成性人工智能平台。

- Unplugged and/or offline learning settings and resources, including printbased case studies, role-play scripts, videos, worksheets and flipcharts.

- Online Al tools, for example learning management systems, social media platforms and generative AI platforms.

STUDENT COMPETENCY 学生能力

课程目标（人工智能课程或学习计划应……）

CURRICULAR GOALS

(Al curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习环境。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Humancentred mindset 以人为本的思维方式

and uphold the principle that humans should not cede the determination to Al when making high-stakes decisions. They are also expected to enhance their judgement on, and attitudinal resilience to, the illusive claims on about the use of outputs and as well as predictions that Al can usurp humans' thinking and decisionmaking.
并坚持人类在做出高风险决策时不应将决定权交给人工智能的原则。他们还应增强对人工智能所声称的输出和预测的判断力和态度韧性，以应对人工智能可能取代人类思维和决策的模糊主张。

assume themselves when learning how to create Al tools or design Al systems.
在学习如何创建人工智能工具或设计人工智能系统时，假设自己具备相关能力。

- CG4.2.1.2 Generate the understanding that human accountability is a legal and social responsibility when using Al in making decisions about humanity: Guide students to analyse the capabilities of AI tools used to assist decision-making. Critically interrogate the genuine capabilities of certain Al tools and debunk the hype around Al's supposed ability to make decisions. Assist students to evaluate the consequences of the institutional use of Al to make decisions about humans in complex situations such as profiling the aptitude of students to take up further learning opportunities or determining the employability of job candidates. Lead discussions on why human accountability in using AI is essential to safeguard human rights and human dignity. Facilitate students to understand why we should not use AI to replace humans when making high-stakes decisions, for example to assess the values, infer the emotions or predict the aptitudes of a natural person. AI algorithms should not be used to assign students'
- CG4.2.1.2 生成对人类责任的理解，即在使用人工智能做出关于人类的决策时，法律和社会责任是必要的：引导学生分析用于辅助决策的人工智能工具的能力。批判性地审视某些人工智能工具的真实能力，并揭穿关于人工智能所谓决策能力的夸大宣传。帮助学生评估在复杂情况下，机构使用人工智能做出关于人类的决策的后果，例如评估学生进一步学习机会的能力或确定求职者的就业能力。引导讨论为什么在使用人工智能时人类责任至关重要，以保护人权和人类尊严。帮助学生理解为什么我们不应该在做出高风险决策时使用人工智能，例如评估价值、推断情感或预测自然人的能力。人工智能算法不应被用于分配学生的。

- Investigating the impact of AI-assisted decisions on humans and avenues of redress within AI regulations: Ask students to find examples in which decisions about humans are determined or greatly influenced by AI (e.g. an AIassisted assessment system used by a bank to approve or deny a student loan application, or a profiling system used by a hotel to predict a person's socioeconomic background based on their location and the device they were using when they made their booking). Facilitate students to reveal the roles of humans and AI in the key steps of decision loops, and check whether human accountability for the decisions is in compliance with locally applicable or international regulations (e.g. the EU AI Act).
- 调查 AI 辅助决策对人类的影响以及 AI 法规中的救济途径：要求学生寻找一些例子，其中关于人类的决策是由 AI 决定或大大影响的（例如，银行使用的 AI 辅助评估系统来批准或拒绝学生贷款申请，或酒店使用的分析系统根据个人的位置和他们在预订时使用的设备来预测一个人的社会经济背景）。引导学生揭示人类和 AI 在决策循环关键步骤中的角色，并检查人类对决策的责任是否符合当地适用或国际法规（例如，欧盟 AI 法案）。

- Scenario-based practices of using AI with purpose: Engage students in activities where they use Al tools to purposefully practise their writing skills and foster their inquiry-based learning, higher-order thinking and creativity. Lead students to discuss how the use of Al without human accountability (e.g. handing in an essay produced by Al ) may reduce human intellectual development. Prompt them to outline concrete actions to protect themselves and their peers from the use of Al outputs
- 基于情境的 AI 使用实践：让学生参与活动，使用 AI 工具有目的地练习写作技能，促进他们的探究式学习、高阶思维和创造力。引导学生讨论在没有人类责任的情况下使用 AI（例如，提交由 AI 生成的论文）可能会减少人类智力发展。促使他们列出具体行动，以保护自己和同伴免受 AI 输出的影响。

STUDENT COMPETENCY "CURRICULAR GOALS (Al curricula or programmes of study should...)" "SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.)" "LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)" Humancentred mindset and uphold the principle that humans should not cede the determination to Al when making high-stakes decisions. They are also expected to enhance their judgement on, and attitudinal resilience to, the illusive claims on about the use of outputs and as well as predictions that Al can usurp humans' thinking and decisionmaking. "assume themselves when learning how to create Al tools or design Al systems. - CG4.2.1.2 Generate the understanding that human accountability is a legal and social responsibility when using Al in making decisions about humanity: Guide students to analyse the capabilities of AI tools used to assist decision-making. Critically interrogate the genuine capabilities of certain Al tools and debunk the hype around Al's supposed ability to make decisions. Assist students to evaluate the consequences of the institutional use of Al to make decisions about humans in complex situations such as profiling the aptitude of students to take up further learning opportunities or determining the employability of job candidates. Lead discussions on why human accountability in using AI is essential to safeguard human rights and human dignity. Facilitate students to understand why we should not use AI to replace humans when making high-stakes decisions, for example to assess the values, infer the emotions or predict the aptitudes of a natural person. AI algorithms should not be used to assign students'" "- Investigating the impact of AI-assisted decisions on humans and avenues of redress within AI regulations: Ask students to find examples in which decisions about humans are determined or greatly influenced by AI (e.g. an AIassisted assessment system used by a bank to approve or deny a student loan application, or a profiling system used by a hotel to predict a person's socioeconomic background based on their location and the device they were using when they made their booking). Facilitate students to reveal the roles of humans and AI in the key steps of decision loops, and check whether human accountability for the decisions is in compliance with locally applicable or international regulations (e.g. the EU AI Act). - Scenario-based practices of using AI with purpose: Engage students in activities where they use Al tools to purposefully practise their writing skills and foster their inquiry-based learning, higher-order thinking and creativity. Lead students to discuss how the use of Al without human accountability (e.g. handing in an essay produced by Al ) may reduce human intellectual development. Prompt them to outline concrete actions to protect themselves and their peers from the use of Al outputs"

| | STUDENT COMPETENCY | CURRICULAR GOALS (Al curricula or programmes of study should...) | SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) | LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) | | :---: | :---: | :---: | :---: | :---: | | Humancentred mindset | and uphold the principle that humans should not cede the determination to Al when making high-stakes decisions. They are also expected to enhance their judgement on, and attitudinal resilience to, the illusive claims on about the use of outputs and as well as predictions that Al can usurp humans' thinking and decisionmaking. | assume themselves when learning how to create Al tools or design Al systems. - CG4.2.1.2 Generate the understanding that human accountability is a legal and social responsibility when using Al in making decisions about humanity: Guide students to analyse the capabilities of AI tools used to assist decision-making. Critically interrogate the genuine capabilities of certain Al tools and debunk the hype around Al's supposed ability to make decisions. Assist students to evaluate the consequences of the institutional use of Al to make decisions about humans in complex situations such as profiling the aptitude of students to take up further learning opportunities or determining the employability of job candidates. Lead discussions on why human accountability in using AI is essential to safeguard human rights and human dignity. Facilitate students to understand why we should not use AI to replace humans when making high-stakes decisions, for example to assess the values, infer the emotions or predict the aptitudes of a natural person. AI algorithms should not be used to assign students' | - Investigating the impact of AI-assisted decisions on humans and avenues of redress within AI regulations: Ask students to find examples in which decisions about humans are determined or greatly influenced by AI (e.g. an AIassisted assessment system used by a bank to approve or deny a student loan application, or a profiling system used by a hotel to predict a person's socioeconomic background based on their location and the device they were using when they made their booking). Facilitate students to reveal the roles of humans and AI in the key steps of decision loops, and check whether human accountability for the decisions is in compliance with locally applicable or international regulations (e.g. the EU AI Act). - Scenario-based practices of using AI with purpose: Engage students in activities where they use Al tools to purposefully practise their writing skills and foster their inquiry-based learning, higher-order thinking and creativity. Lead students to discuss how the use of Al without human accountability (e.g. handing in an essay produced by Al ) may reduce human intellectual development. Prompt them to outline concrete actions to protect themselves and their peers from the use of Al outputs | |

STUDENT COMPETENCY 学生能力

课程目标（AI 课程或学习计划应...）

CURRICULAR GOALS

(AI curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习设置。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Humancentred mindset 以人为本的思维方式

scores (as happened during COVID-19) or decide on university admissions.
分数（如在 COVID-19 期间发生的情况）或决定大学录取。

- CG4.2.1.3 Nurture the personal attitude that human accountability requires personal competencies to steer the purposeful use of AI: Guide students to interrogate how the automation of literature reviews, writing and artistic creation may undermine human thinking processes and intellectual development. Guide students to discuss concrete actions that they can take to protect themselves and their peers from the use of Al outputs or predictions to usurp human thought, intellectual practices and continuous capacity enhancement.
- CG4.2.1.3 培养个人态度，即人类责任需要个人能力来引导 AI 的有目的使用：引导学生质疑文献综述、写作和艺术创作的自动化如何可能削弱人类思维过程和智力发展。引导学生讨论他们可以采取的具体行动，以保护自己和同伴免受 AI 输出或预测的影响，从而取代人类思维、智力实践和持续能力提升。

or predictions to usurp thinking processes, and give them insight into the competencies that students need in order to steer the use of Al toward serving human capability development.
或者预测以取代思维过程，并让他们了解学生需要具备的能力，以便引导人工智能的使用，服务于人类能力的发展。

Ethics of AI 人工智能的伦理

4.2.2 安全和负责任的使用 - 学生应能够在遵守伦理原则和当地适用法规的情况下，进行负责任的人工智能实践。他们应意识到披露数据隐私的风险，并采取措施确保他们的数据被收集、使用、共享和存档。

4.2.2 Safe and responsible use

- Students are expected to be able to carry out responsible Al practices in compliance with ethical principles and locally applicable regulations. They are expected to be conscious of the risks of disclosing data privacy and take measures to ensure that their data are collected, used, shared, archived and

- CG4.2.2.1 Foster self-awareness and habitual compliance with ethical principles for the responsible use of AI: Illustrate the ethical principles or regulatory articles concerning the responsible use of AI, drawing on concrete Al tools and real-world use scenarios. Support students to iteratively build and update a checkbox of ethical principles for ensuring their own lawful and responsible practices when engaging with Al systems. Guide students to practise and habituate their compliance with these principles, such
- CG4.2.2.1 培养自我意识和对人工智能负责任使用的习惯性遵守伦理原则：阐明与人工智能负责任使用相关的伦理原则或法规条款，借助具体的人工智能工具和现实世界的使用场景。支持学生迭代构建和更新一份伦理原则的检查清单，以确保他们在与人工智能系统互动时的合法和负责任的实践。引导学生练习并养成遵守这些原则的习惯。

- Designing an 'ethics kit' for the self-disciplined, responsible use of AI: Design simulated scenarios containing potential ethical conflicts (e.g. sharing private data or protected content when chatting with Al systems, putting Al-generated content in a school assignment, creating a video using images of other people, or distributing misinformation, disinformation or hate speech). Organize the drafting of an 'ethics kit' that users need to habitually check when using AI, including articles drawn from locally applicable
- 为自律、负责任地使用人工智能设计一个“伦理工具包”：设计包含潜在伦理冲突的模拟场景（例如，在与人工智能系统聊天时分享私人数据或受保护内容、在学校作业中使用人工智能生成的内容、使用他人的图像制作视频，或传播错误信息、虚假信息或仇恨言论）。组织起草一个用户在使用人工智能时需要习惯性检查的“伦理工具包”，包括来自当地适用的文章。

- 不插电学习环境和资源，包括纸质工作表、海报和伦理原则清单。 - 预下载的隐私政策和人工智能法规，以及有关人工智能安全、数据隐私和同意形式的法律或伦理案例示例。 - 本地可用的人工智能工具，包括智能手机应用程序。

- Unplugged learning settings and resources, including paperbased worksheets, posters and checklists of ethical principles.

- Predownloaded privacy policies and AI regulations, and examples of legal or ethical cases concerning Al safety, data privacy and forms of consent.

- Locally available Al tools including smartphone apps.

STUDENT COMPETENCY "CURRICULAR GOALS (AI curricula or programmes of study should...)" "SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.)" "LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)" Humancentred mindset "scores (as happened during COVID-19) or decide on university admissions. - CG4.2.1.3 Nurture the personal attitude that human accountability requires personal competencies to steer the purposeful use of AI: Guide students to interrogate how the automation of literature reviews, writing and artistic creation may undermine human thinking processes and intellectual development. Guide students to discuss concrete actions that they can take to protect themselves and their peers from the use of Al outputs or predictions to usurp human thought, intellectual practices and continuous capacity enhancement." or predictions to usurp thinking processes, and give them insight into the competencies that students need in order to steer the use of Al toward serving human capability development. Ethics of AI "4.2.2 Safe and responsible use - Students are expected to be able to carry out responsible Al practices in compliance with ethical principles and locally applicable regulations. They are expected to be conscious of the risks of disclosing data privacy and take measures to ensure that their data are collected, used, shared, archived and" - CG4.2.2.1 Foster self-awareness and habitual compliance with ethical principles for the responsible use of AI: Illustrate the ethical principles or regulatory articles concerning the responsible use of AI, drawing on concrete Al tools and real-world use scenarios. Support students to iteratively build and update a checkbox of ethical principles for ensuring their own lawful and responsible practices when engaging with Al systems. Guide students to practise and habituate their compliance with these principles, such - Designing an 'ethics kit' for the self-disciplined, responsible use of AI: Design simulated scenarios containing potential ethical conflicts (e.g. sharing private data or protected content when chatting with Al systems, putting Al-generated content in a school assignment, creating a video using images of other people, or distributing misinformation, disinformation or hate speech). Organize the drafting of an 'ethics kit' that users need to habitually check when using AI, including articles drawn from locally applicable "- Unplugged learning settings and resources, including paperbased worksheets, posters and checklists of ethical principles. - Predownloaded privacy policies and AI regulations, and examples of legal or ethical cases concerning Al safety, data privacy and forms of consent. - Locally available Al tools including smartphone apps."

| | STUDENT COMPETENCY | CURRICULAR GOALS (AI curricula or programmes of study should...) | SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) | LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) | | :---: | :---: | :---: | :---: | :---: | | Humancentred mindset | | scores (as happened during COVID-19) or decide on university admissions. - CG4.2.1.3 Nurture the personal attitude that human accountability requires personal competencies to steer the purposeful use of AI: Guide students to interrogate how the automation of literature reviews, writing and artistic creation may undermine human thinking processes and intellectual development. Guide students to discuss concrete actions that they can take to protect themselves and their peers from the use of Al outputs or predictions to usurp human thought, intellectual practices and continuous capacity enhancement. | or predictions to usurp thinking processes, and give them insight into the competencies that students need in order to steer the use of Al toward serving human capability development. | | | Ethics of AI | 4.2.2 Safe and responsible use - Students are expected to be able to carry out responsible Al practices in compliance with ethical principles and locally applicable regulations. They are expected to be conscious of the risks of disclosing data privacy and take measures to ensure that their data are collected, used, shared, archived and | - CG4.2.2.1 Foster self-awareness and habitual compliance with ethical principles for the responsible use of AI: Illustrate the ethical principles or regulatory articles concerning the responsible use of AI, drawing on concrete Al tools and real-world use scenarios. Support students to iteratively build and update a checkbox of ethical principles for ensuring their own lawful and responsible practices when engaging with Al systems. Guide students to practise and habituate their compliance with these principles, such | - Designing an 'ethics kit' for the self-disciplined, responsible use of AI: Design simulated scenarios containing potential ethical conflicts (e.g. sharing private data or protected content when chatting with Al systems, putting Al-generated content in a school assignment, creating a video using images of other people, or distributing misinformation, disinformation or hate speech). Organize the drafting of an 'ethics kit' that users need to habitually check when using AI, including articles drawn from locally applicable | - Unplugged learning settings and resources, including paperbased worksheets, posters and checklists of ethical principles. - Predownloaded privacy policies and AI regulations, and examples of legal or ethical cases concerning Al safety, data privacy and forms of consent. - Locally available Al tools including smartphone apps. |

\begin{array}{lllll} \begin{array}{ll} STUDENT \\ COMPETENCY \end{array} & \begin{matrix} CURRICULAR GOALS \\ (AI curricula or programmes \\ of study should...) \end{matrix} & \begin{matrix} SUGGESTED \\ PEDAGOGICAL METHODS \end{matrix} \\ Ethics of AI \\ (Institutions and teachers \\ can consider and adapt the \\ following learning methods.) \end{array} \begin{array}{l} LeARNING \\ ENVIRONMENTS \\ (The following learning \\ settings can be \\ provided and adapted.) \end{array}]

STUDENT COMPETENCY 学生能力

课程目标（人工智能课程或学习计划应……）

CURRICULAR GOALS

(Al curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习环境。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Ethics of AI 人工智能的伦理

their knowledge on human rights to data protection and privacy and the legal responsibilities of AI creators to collect data with consent, and guide them to practise strategies for ensuring their personal data is collected, used, shared, archived and deleted only with their informed consent. Expose students to simulated scenarios containing typical Al incidents, so they can practise precautionary and interactive strategies for the safe use of Al and become familiar with regulations that can protect their safety or mitigate the negative impacts of Al incidents.
他们对数据保护和隐私的人权知识，以及人工智能创作者在收集数据时的法律责任，确保他们在收集、使用、共享、存档和删除个人数据时仅在知情同意的情况下进行。让学生接触包含典型人工智能事件的模拟场景，以便他们能够练习预防性和互动性策略，安全使用人工智能，并熟悉可以保护他们安全或减轻人工智能事件负面影响的法规。

- Debate the ownership of AI-generated content and outputs from human-AI interactions: Organize a debate to trigger students' reflections around the ownership of content created using AI. Examine the availability and applicability of regulations on the recognition of copyright for Al-generated content and resources, and how relevant regulations recognize intellectual work that integrates different extents of Al-generated content.
- 辩论 AI 生成内容和人机交互输出的所有权：组织一场辩论，以激发学生对使用 AI 创建的内容所有权的反思。考察关于 AI 生成内容和资源的版权认可的法规的可用性和适用性，以及相关法规如何认可整合不同程度 AI 生成内容的知识产权。

AI techniques and applications
AI 技术和应用

4.2.3 应用技能 - 学生应能够构建适合年龄的数据、AI 算法和编程的知识结构，并获得可转移的应用技能。学生应能够批判性地评估和利用免费和/或开源的 AI 工具、编程库和数据集。

4.2.3 Application skills

- Students are expected to be able to construct an age-appropriate knowledge structure on data, Al algorithms and programming, and acquire transferable application skills. Students are expected to be able to critically evaluate and leverage free and/ or open-source AI tools, programming libraries and datasets.

- CG4.2.3.1 Offer opportunities to strengthen knowledge and skills on data modelling, engineering and analysis: Provide students with task-based learning opportunities to acquire age-appropriate knowledge and skills on datasets, including applying age-appropriate tools or programming languages to acquire, clean and transform data into a suitable format for storing, processing, and analysing databases (e.g. SQL, NoSQL, SparkSQL or Apache Flink).
- CG4.2.3.1 提供机会以增强数据建模、工程和分析的知识和技能：为学生提供基于任务的学习机会，以获取适合年龄的数据集知识和技能，包括应用适合年龄的工具或编程语言来获取、清理和转换数据，以便存储、处理和分析数据库（例如 SQL、NoSQL、SparkSQL 或 Apache Flink）。

- CG4.2.3.2 Provide opportunities to acquire age-appropriate technical skills in Al programming: Explain examples of AI systems that use different
- CG4.2.3.2 提供机会以获取适合年龄的人工智能编程技术技能：解释使用不同的人工智能系统的示例。

- Data biases lab: Provide students with sample datasets with and without outliers, guide students to conduct hands-on experimentation on how the outliers impact the model (e.g. in regression or clustering examples). For image classification, ask students to conduct an experiment on how class imbalance (e.g. significantly more data in one class than the other) affects model performance per class. Guide students to learn age-appropriate skills in data engineering to remove identifiable biases and compare the results.
- 数据偏差实验室：为学生提供带有和不带有异常值的样本数据集，指导学生进行动手实验，了解异常值如何影响模型（例如，在回归或聚类示例中）。对于图像分类，要求学生进行实验，了解类别不平衡（例如，一类数据显著多于另一类）如何影响每个类别的模型性能。指导学生学习适合年龄的数据工程技能，以消除可识别的偏差并比较结果。

- Tailored optional modular courses on various AI algorithms to support cohort-based learning: Tailor free and/or open-source
- 针对各种 AI 算法的定制化可选模块课程，以支持基于小组的学习：定制免费和/或开源

- 连接互联网的计算机。 - 计算机基础的数据集样本或本地可访问的公共数据集。 - 用于 AI 编程的计算机基础应用程序或本地可访问的在线开源 AI 编程库。 - 计算机基础或本地可访问的在线 AI 工具。

- Computers with internet connection.

- Computer-based samples of datasets or locally accessible public datasets.

- Computer-based applications for AI programming or locally accessible online open-source Al programming libraries.

- Computer-based or locally accessible online AI tools.

STUDENT COMPETENCY "CURRICULAR GOALS (Al curricula or programmes of study should...)" "SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.)" "LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)" Ethics of AI their knowledge on human rights to data protection and privacy and the legal responsibilities of AI creators to collect data with consent, and guide them to practise strategies for ensuring their personal data is collected, used, shared, archived and deleted only with their informed consent. Expose students to simulated scenarios containing typical Al incidents, so they can practise precautionary and interactive strategies for the safe use of Al and become familiar with regulations that can protect their safety or mitigate the negative impacts of Al incidents. - Debate the ownership of AI-generated content and outputs from human-AI interactions: Organize a debate to trigger students' reflections around the ownership of content created using AI. Examine the availability and applicability of regulations on the recognition of copyright for Al-generated content and resources, and how relevant regulations recognize intellectual work that integrates different extents of Al-generated content. AI techniques and applications "4.2.3 Application skills - Students are expected to be able to construct an age-appropriate knowledge structure on data, Al algorithms and programming, and acquire transferable application skills. Students are expected to be able to critically evaluate and leverage free and/ or open-source AI tools, programming libraries and datasets." "- CG4.2.3.1 Offer opportunities to strengthen knowledge and skills on data modelling, engineering and analysis: Provide students with task-based learning opportunities to acquire age-appropriate knowledge and skills on datasets, including applying age-appropriate tools or programming languages to acquire, clean and transform data into a suitable format for storing, processing, and analysing databases (e.g. SQL, NoSQL, SparkSQL or Apache Flink). - CG4.2.3.2 Provide opportunities to acquire age-appropriate technical skills in Al programming: Explain examples of AI systems that use different" "- Data biases lab: Provide students with sample datasets with and without outliers, guide students to conduct hands-on experimentation on how the outliers impact the model (e.g. in regression or clustering examples). For image classification, ask students to conduct an experiment on how class imbalance (e.g. significantly more data in one class than the other) affects model performance per class. Guide students to learn age-appropriate skills in data engineering to remove identifiable biases and compare the results. - Tailored optional modular courses on various AI algorithms to support cohort-based learning: Tailor free and/or open-source" "- Computers with internet connection. - Computer-based samples of datasets or locally accessible public datasets. - Computer-based applications for AI programming or locally accessible online open-source Al programming libraries. - Computer-based or locally accessible online AI tools."

| | STUDENT COMPETENCY | CURRICULAR GOALS (Al curricula or programmes of study should...) | SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) | LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) | | :---: | :---: | :---: | :---: | :---: | | Ethics of AI | | their knowledge on human rights to data protection and privacy and the legal responsibilities of AI creators to collect data with consent, and guide them to practise strategies for ensuring their personal data is collected, used, shared, archived and deleted only with their informed consent. Expose students to simulated scenarios containing typical Al incidents, so they can practise precautionary and interactive strategies for the safe use of Al and become familiar with regulations that can protect their safety or mitigate the negative impacts of Al incidents. | - Debate the ownership of AI-generated content and outputs from human-AI interactions: Organize a debate to trigger students' reflections around the ownership of content created using AI. Examine the availability and applicability of regulations on the recognition of copyright for Al-generated content and resources, and how relevant regulations recognize intellectual work that integrates different extents of Al-generated content. | | | AI techniques and applications | 4.2.3 Application skills - Students are expected to be able to construct an age-appropriate knowledge structure on data, Al algorithms and programming, and acquire transferable application skills. Students are expected to be able to critically evaluate and leverage free and/ or open-source AI tools, programming libraries and datasets. | - CG4.2.3.1 Offer opportunities to strengthen knowledge and skills on data modelling, engineering and analysis: Provide students with task-based learning opportunities to acquire age-appropriate knowledge and skills on datasets, including applying age-appropriate tools or programming languages to acquire, clean and transform data into a suitable format for storing, processing, and analysing databases (e.g. SQL, NoSQL, SparkSQL or Apache Flink). - CG4.2.3.2 Provide opportunities to acquire age-appropriate technical skills in Al programming: Explain examples of AI systems that use different | - Data biases lab: Provide students with sample datasets with and without outliers, guide students to conduct hands-on experimentation on how the outliers impact the model (e.g. in regression or clustering examples). For image classification, ask students to conduct an experiment on how class imbalance (e.g. significantly more data in one class than the other) affects model performance per class. Guide students to learn age-appropriate skills in data engineering to remove identifiable biases and compare the results. - Tailored optional modular courses on various AI algorithms to support cohort-based learning: Tailor free and/or open-source | - Computers with internet connection. - Computer-based samples of datasets or locally accessible public datasets. - Computer-based applications for AI programming or locally accessible online open-source Al programming libraries. - Computer-based or locally accessible online AI tools. |

STUDENT COMPETENCY 学生能力

课程目标（AI 课程或学习计划应...）

CURRICULAR GOALS

(AI curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习环境。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

AI techniques and applications
人工智能技术和应用

categories of AI algorithms to scaffold students' ageappropriate understanding of AI algorithms including supervised learning, unsupervised learning and reinforcement learning. This should include how they scrape and process data, how they're trained, how they function, and the concrete types of algorithms that underlie these categories. Where appropriate, provide students with task-based learning opportunities to cultivate methodological knowledge on selected AI algorithms.
人工智能算法的类别，以帮助学生适应年龄的理解，包括监督学习、无监督学习和强化学习。这应包括它们如何抓取和处理数据，如何训练，如何运作，以及支撑这些类别的具体算法类型。在适当的情况下，为学生提供基于任务的学习机会，以培养对选定人工智能算法的方法论知识。

- CG4.2.3.3 Encourage students to develop analytical and synthesis skills to leverage opensource datasets and AI tools: Organize problembased learning to facilitate students' acquisition of skills to critically evaluate and leverage open-source AI datasets (e.g. MNIST.12 CIFAR,

^{13}

or ImageNet)

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and tools from free and/or open-source AI algorithm libraries (e.g. Teachable Machine,

^{15}

MIT App Inventor,

^{16}

PyTorch,

^{17}

or Keras

)^{18}

to address authentic tasks. Drawing on variations of problems, guide students to practise and enhance the transferability of their knowledge and skills on data and algorithms into complex contexts.
- CG4.2.3.3 鼓励学生发展分析和综合能力，以利用开源数据集和人工智能工具：组织基于问题的学习，以促进学生获得批判性评估和利用开源人工智能数据集（例如 MNIST、CIFAR、

^{13}

或 ImageNet）

^{14}

和来自免费和/或开源人工智能算法库的工具（例如 Teachable Machine、

^{15}

MIT App Inventor、

^{16}

PyTorch、

^{17}

或 Keras

)^{18}

）来解决真实任务。通过不同问题的变体，引导学生练习并增强他们在复杂背景下对数据和算法的知识和技能的可转移性。

Al datasets and Al algorithm libraries according to the age and prior knowledge of target students. Develop optional modular courses on various Al algorithms and support cohorts of students to choose the courses that align with their interests, to acquire methodological knowledge and skills in applying AI algorithms.
根据目标学生的年龄和先前知识选择人工智能数据集和人工智能算法库。开发关于各种人工智能算法的可选模块课程，并支持学生群体选择与他们兴趣相符的课程，以获取应用人工智能算法的方法论知识和技能。

- Al hackathons based on variations of authentic tasks: Schedule a significant amount of continuous learning hours to challenge interested students to conduct task-based hackathons. Design a series of tasks with variations to enable students to practise their transferable AI programming skills.
- 基于真实任务变体的人工智能黑客马拉松：安排大量的持续学习时间，挑战有兴趣的学生进行基于任务的黑客马拉松。设计一系列具有变体的任务，以使学生能够练习他们可转移的人工智能编程技能。

- Debunking claims that Al will automate coding and human students don't need to learn AI programming: Facilitate students' research into the professional knowledge and skills demanded by the creation and iteration of Al systems, especially the foundation of methodological knowledge necessary to explore more human-centred and innovative Al algorithms and methods. Challenge students to contemplate how using Al to replace humans' programming skills will lead to fewer people acquiring these foundational skills, and exacerbate the inequality between those with and without AI-related knowledge.
- 驳斥人工智能将自动化编码的说法，以及人类学生不需要学习人工智能编程：促进学生研究创建和迭代人工智能系统所需的专业知识和技能，特别是探索更以人为中心和创新的人工智能算法和方法所需的基础方法论知识。挑战学生思考，使用人工智能替代人类编程技能将导致更少的人获得这些基础技能，并加剧拥有和没有人工智能相关知识之间的不平等。

STUDENT COMPETENCY "CURRICULAR GOALS (AI curricula or programmes of study should...)" "SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.)" "LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)" AI techniques and applications "categories of AI algorithms to scaffold students' ageappropriate understanding of AI algorithms including supervised learning, unsupervised learning and reinforcement learning. This should include how they scrape and process data, how they're trained, how they function, and the concrete types of algorithms that underlie these categories. Where appropriate, provide students with task-based learning opportunities to cultivate methodological knowledge on selected AI algorithms. - CG4.2.3.3 Encourage students to develop analytical and synthesis skills to leverage opensource datasets and AI tools: Organize problembased learning to facilitate students' acquisition of skills to critically evaluate and leverage open-source AI datasets (e.g. MNIST.12 CIFAR, ^(13) or ImageNet) ^(14) and tools from free and/or open-source AI algorithm libraries (e.g. Teachable Machine, ^(15) MIT App Inventor, ^(16) PyTorch, ^(17) or Keras )^(18) to address authentic tasks. Drawing on variations of problems, guide students to practise and enhance the transferability of their knowledge and skills on data and algorithms into complex contexts." "Al datasets and Al algorithm libraries according to the age and prior knowledge of target students. Develop optional modular courses on various Al algorithms and support cohorts of students to choose the courses that align with their interests, to acquire methodological knowledge and skills in applying AI algorithms. - Al hackathons based on variations of authentic tasks: Schedule a significant amount of continuous learning hours to challenge interested students to conduct task-based hackathons. Design a series of tasks with variations to enable students to practise their transferable AI programming skills. - Debunking claims that Al will automate coding and human students don't need to learn AI programming: Facilitate students' research into the professional knowledge and skills demanded by the creation and iteration of Al systems, especially the foundation of methodological knowledge necessary to explore more human-centred and innovative Al algorithms and methods. Challenge students to contemplate how using Al to replace humans' programming skills will lead to fewer people acquiring these foundational skills, and exacerbate the inequality between those with and without AI-related knowledge."

| | STUDENT COMPETENCY | CURRICULAR GOALS (AI curricula or programmes of study should...) | SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) | LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) | | :---: | :---: | :---: | :---: | :---: | | AI techniques and applications | | categories of AI algorithms to scaffold students' ageappropriate understanding of AI algorithms including supervised learning, unsupervised learning and reinforcement learning. This should include how they scrape and process data, how they're trained, how they function, and the concrete types of algorithms that underlie these categories. Where appropriate, provide students with task-based learning opportunities to cultivate methodological knowledge on selected AI algorithms. - CG4.2.3.3 Encourage students to develop analytical and synthesis skills to leverage opensource datasets and AI tools: Organize problembased learning to facilitate students' acquisition of skills to critically evaluate and leverage open-source AI datasets (e.g. MNIST.12 CIFAR, ${ }^{13}$ or ImageNet) ${ }^{14}$ and tools from free and/or open-source AI algorithm libraries (e.g. Teachable Machine, ${ }^{15}$ MIT App Inventor, ${ }^{16}$ PyTorch, ${ }^{17}$ or Keras $)^{18}$ to address authentic tasks. Drawing on variations of problems, guide students to practise and enhance the transferability of their knowledge and skills on data and algorithms into complex contexts. | Al datasets and Al algorithm libraries according to the age and prior knowledge of target students. Develop optional modular courses on various Al algorithms and support cohorts of students to choose the courses that align with their interests, to acquire methodological knowledge and skills in applying AI algorithms. - Al hackathons based on variations of authentic tasks: Schedule a significant amount of continuous learning hours to challenge interested students to conduct task-based hackathons. Design a series of tasks with variations to enable students to practise their transferable AI programming skills. - Debunking claims that Al will automate coding and human students don't need to learn AI programming: Facilitate students' research into the professional knowledge and skills demanded by the creation and iteration of Al systems, especially the foundation of methodological knowledge necessary to explore more human-centred and innovative Al algorithms and methods. Challenge students to contemplate how using Al to replace humans' programming skills will lead to fewer people acquiring these foundational skills, and exacerbate the inequality between those with and without AI-related knowledge. | |

STUDENT COMPETENCY 学生能力

课程目标（人工智能课程或学习计划应...）

CURRICULAR GOALS

(AI curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习环境。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Al system design 人工智能系统设计

4.2.4 Architecture design
4.2.4 架构设计

- Students are expected to be able to cultivate basic methodological knowledge and technical skills to configure a scalable, maintainable and reusable architecture for an Al system covering layers of data, algorithms, models and application interfaces. Students are expected to develop the interdisciplinary skills necessary to leverage datasets, programming tools and computational resources to construct a prototype AI system. This includes the expectation that they apply deepened humancentred values and ethical principles in their configuration, construction and optimization.
- 学生应能够培养基本的方法论知识和技术技能，以配置一个可扩展、可维护和可重用的人工智能系统架构，涵盖数据、算法、模型和应用接口的各个层面。学生应发展必要的跨学科技能，以利用数据集、编程工具和计算资源构建原型人工智能系统。这包括期望他们在配置、构建和优化过程中应用深化的人本价值观和伦理原则。

- CG4.2.4.1 Scaffold the acquisition of methodological knowledge and technical skills on AI architecture:
- CG4.2.4.1 支持人工智能架构的方法论知识和技术技能的获取：

Facilitate students to acquire and practise the necessary engineering thinking and operational skills to evaluate a variety of Al architectures with an aim to choose an appropriate solution based on a defined problem statement, while considering opensource options. Provide project-based learning opportunities to support their acquisition of methodological knowledge on the configuration of a prototype AI architecture encompassing an antibias data structure, an energy-efficient AI model to minimize the negative environmental impact, the human-centred design of performance and services, and metrics to test and improvise the maturity of the configuration.
促进学生获取和实践必要的工程思维和操作技能，以评估各种人工智能架构，旨在根据定义的问题陈述选择合适的解决方案，同时考虑开源选项。提供基于项目的学习机会，以支持他们获取关于配置原型人工智能架构的方法论知识，包括反偏见数据结构、旨在最小化负面环境影响的节能人工智能模型、以人为本的性能和服务设计，以及测试和改进配置成熟度的指标。

- CG4.2.4.2 Support the preparation of advanced technical skills and project management competencies needed by Al system building: Offer project-based learning opportunities to facilitate students to acquire and apply the interdisciplinary technical skills demanded by the building of a prototype AI system designed for a simple specific task (e.g. a chatbot imitating the responses of
- CG4.2.4.2 支持人工智能系统构建所需的高级技术技能和项目管理能力的准备：提供基于项目的学习机会，以促进学生获取和应用构建针对简单特定任务（例如模仿响应的聊天机器人）所需的跨学科技术技能。

- Simulating the evaluation of frameworks and components for Al architectural configuration: Based on the problem statement and feasibility study, facilitate students to evaluate a variety of frameworks for Al architectures (e.g. TensorFlow, PyTorch, or Scikit-learn). Simulate the evaluation and selection of solutions to the components of the architecture (e.g. data layer, algorithm layer, AI model layer and interface layer) based on the selected framework. Configure a prototype architecture encompassing the required datasets, algorithm tools, Al model and required computational resources, the design of main functionalities and interface, and the plans for deployment. Guide students to communicate the configuration through abstractions such as flowcharts, diagrams or pseudocode.
- 模拟对 AI 架构配置的框架和组件的评估：基于问题陈述和可行性研究，帮助学生评估各种 AI 架构的框架（例如 TensorFlow、PyTorch 或 Scikit-learn）。根据所选框架模拟对架构组件（例如数据层、算法层、AI 模型层和接口层）的评估和选择解决方案。配置一个原型架构，包括所需的数据集、算法工具、AI 模型和所需的计算资源，主要功能和接口的设计，以及部署计划。指导学生通过流程图、图表或伪代码等抽象方式传达配置。

- Simulating the leveraging of resources to build an AI system: Facilitate students to build the simulated AI system based on locally hosted computing devices or locally accessible cloud computing platforms (e.g. Hadoop or Spark), and the operating systems (e.g. GNU) and software needed to train the machine-learning models. Guide students to conduct trade-offs between cost and computing capability needs, and between the robustness
- 模拟利用资源构建 AI 系统：引导学生基于本地托管的计算设备或本地可访问的云计算平台（例如 Hadoop 或 Spark）构建模拟的 AI 系统，以及训练机器学习模型所需的操作系统（例如 GNU）和软件。指导学生在成本与计算能力需求之间，以及在稳健性之间进行权衡。

- 展示如何进行 AI 模型的伦理和技术评估的视频和指标。 - 计算机基础或本地可访问的在线 AI 系统示例。 - 计算机基础的数据集样本或本地可访问的公共数据集。 - 用于 AI 编程的计算机基础应用程序或本地可访问的在线开源 AI 编程库。 - 通过云平台由机构共享的本地托管或开源云计算及其他资源。

- Videos and metrics showing how to conduct ethical and technical evaluations of AI models.

- Computer-based or locally accessible online examples of Al systems.

- Computer-based samples of datasets or locally accessible public datasets.

- Computer-based applications for AI programming or locally accessible online open-source Al programming libraries.

- Locally hosted or open-source cloud computing and other resources shared by institutions through cloud platforms.

STUDENT COMPETENCY "CURRICULAR GOALS (AI curricula or programmes of study should...)" "SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.)" "LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)" Al system design "4.2.4 Architecture design - Students are expected to be able to cultivate basic methodological knowledge and technical skills to configure a scalable, maintainable and reusable architecture for an Al system covering layers of data, algorithms, models and application interfaces. Students are expected to develop the interdisciplinary skills necessary to leverage datasets, programming tools and computational resources to construct a prototype AI system. This includes the expectation that they apply deepened humancentred values and ethical principles in their configuration, construction and optimization." "- CG4.2.4.1 Scaffold the acquisition of methodological knowledge and technical skills on AI architecture: Facilitate students to acquire and practise the necessary engineering thinking and operational skills to evaluate a variety of Al architectures with an aim to choose an appropriate solution based on a defined problem statement, while considering opensource options. Provide project-based learning opportunities to support their acquisition of methodological knowledge on the configuration of a prototype AI architecture encompassing an antibias data structure, an energy-efficient AI model to minimize the negative environmental impact, the human-centred design of performance and services, and metrics to test and improvise the maturity of the configuration. - CG4.2.4.2 Support the preparation of advanced technical skills and project management competencies needed by Al system building: Offer project-based learning opportunities to facilitate students to acquire and apply the interdisciplinary technical skills demanded by the building of a prototype AI system designed for a simple specific task (e.g. a chatbot imitating the responses of" "- Simulating the evaluation of frameworks and components for Al architectural configuration: Based on the problem statement and feasibility study, facilitate students to evaluate a variety of frameworks for Al architectures (e.g. TensorFlow, PyTorch, or Scikit-learn). Simulate the evaluation and selection of solutions to the components of the architecture (e.g. data layer, algorithm layer, AI model layer and interface layer) based on the selected framework. Configure a prototype architecture encompassing the required datasets, algorithm tools, Al model and required computational resources, the design of main functionalities and interface, and the plans for deployment. Guide students to communicate the configuration through abstractions such as flowcharts, diagrams or pseudocode. - Simulating the leveraging of resources to build an AI system: Facilitate students to build the simulated AI system based on locally hosted computing devices or locally accessible cloud computing platforms (e.g. Hadoop or Spark), and the operating systems (e.g. GNU) and software needed to train the machine-learning models. Guide students to conduct trade-offs between cost and computing capability needs, and between the robustness" "- Videos and metrics showing how to conduct ethical and technical evaluations of AI models. - Computer-based or locally accessible online examples of Al systems. - Computer-based samples of datasets or locally accessible public datasets. - Computer-based applications for AI programming or locally accessible online open-source Al programming libraries. - Locally hosted or open-source cloud computing and other resources shared by institutions through cloud platforms."

| | STUDENT COMPETENCY | CURRICULAR GOALS (AI curricula or programmes of study should...) | SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) | LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) | | :---: | :---: | :---: | :---: | :---: | | Al system design | 4.2.4 Architecture design - Students are expected to be able to cultivate basic methodological knowledge and technical skills to configure a scalable, maintainable and reusable architecture for an Al system covering layers of data, algorithms, models and application interfaces. Students are expected to develop the interdisciplinary skills necessary to leverage datasets, programming tools and computational resources to construct a prototype AI system. This includes the expectation that they apply deepened humancentred values and ethical principles in their configuration, construction and optimization. | - CG4.2.4.1 Scaffold the acquisition of methodological knowledge and technical skills on AI architecture: Facilitate students to acquire and practise the necessary engineering thinking and operational skills to evaluate a variety of Al architectures with an aim to choose an appropriate solution based on a defined problem statement, while considering opensource options. Provide project-based learning opportunities to support their acquisition of methodological knowledge on the configuration of a prototype AI architecture encompassing an antibias data structure, an energy-efficient AI model to minimize the negative environmental impact, the human-centred design of performance and services, and metrics to test and improvise the maturity of the configuration. - CG4.2.4.2 Support the preparation of advanced technical skills and project management competencies needed by Al system building: Offer project-based learning opportunities to facilitate students to acquire and apply the interdisciplinary technical skills demanded by the building of a prototype AI system designed for a simple specific task (e.g. a chatbot imitating the responses of | - Simulating the evaluation of frameworks and components for Al architectural configuration: Based on the problem statement and feasibility study, facilitate students to evaluate a variety of frameworks for Al architectures (e.g. TensorFlow, PyTorch, or Scikit-learn). Simulate the evaluation and selection of solutions to the components of the architecture (e.g. data layer, algorithm layer, AI model layer and interface layer) based on the selected framework. Configure a prototype architecture encompassing the required datasets, algorithm tools, Al model and required computational resources, the design of main functionalities and interface, and the plans for deployment. Guide students to communicate the configuration through abstractions such as flowcharts, diagrams or pseudocode. - Simulating the leveraging of resources to build an AI system: Facilitate students to build the simulated AI system based on locally hosted computing devices or locally accessible cloud computing platforms (e.g. Hadoop or Spark), and the operating systems (e.g. GNU) and software needed to train the machine-learning models. Guide students to conduct trade-offs between cost and computing capability needs, and between the robustness | - Videos and metrics showing how to conduct ethical and technical evaluations of AI models. - Computer-based or locally accessible online examples of Al systems. - Computer-based samples of datasets or locally accessible public datasets. - Computer-based applications for AI programming or locally accessible online open-source Al programming libraries. - Locally hosted or open-source cloud computing and other resources shared by institutions through cloud platforms. |

STUDENT COMPETENCY 学生能力

课程目标（AI 课程或学习计划应...）

CURRICULAR GOALS

(Al curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习环境。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Al system design 人工智能系统设计

an experienced teacher). Explore the leveraging and normalization of datasets, assembling of virtual computational resources, and selection and enhancements of AI models (e.g. hyperparameter optimization). Guide students to simulate the training of a machinelearning model, including the practical use of computational resources and calling of data to train the models based on the selected and preprocessed datasets. Design and arrange opportunities for students to acquire project management skills including balancing the scope of the Al systems with the resources available, coordinating the division and sharing of responsibilities, and critically evaluating and leveraging AI resources.
一位经验丰富的教师）。探索数据集的利用和规范化，虚拟计算资源的组装，以及人工智能模型的选择和增强（例如超参数优化）。指导学生模拟机器学习模型的训练，包括实际使用计算资源和调用数据以根据所选和预处理的数据集训练模型。设计和安排机会，让学生获得项目管理技能，包括平衡人工智能系统的范围与可用资源，协调责任的划分和共享，以及批判性地评估和利用人工智能资源。

AI 模型及其对环境的影响，旨在优化效率并最小化计算资源的浪费。模拟架构的增强，包括超参数的优化和/或对现有 AI 模型的微调，以解决简单问题（例如，在现有模型上进行迁移学习，或应用新颖的神经网络或对基础模型进行非平凡的修改）。练习使用计算资源和调用数据，以基于所选和预处理的数据集训练机器学习模型。

of the AI models and their environmental impact, aiming to optimize efficiency and minimize the waste of computational resources. Simulate the enhancement of the architecture including the optimization of hyperparameters and/or finetuning of existing AI models to solve simple problems (e.g. transfer learning on top of a pre-existing model, or apply novel neural networks or non-trivial modifications to foundational models).

Practise using computational resources and calling data to train machine-learning models based on the selected and preprocessed datasets.

4.3 Level 3: Create
4.3 级别 3：创建

The overall goal of the ‘Create’ level is to challenge and enable students to develop advanced competencies to configure AI solutions or craft new Al tools based on customizable datasets, programming tools or Al models, with consideration of open-source options. Students will also be supported to reinforce a sense of belonging to a broader community of Al co-creators and enhance their intellectual engagement with the social responsibilities that are required for being a citizen in Al societies. The curricular goals shown in Table 4 aim to inspire the outlining of a set of high-level competencies composed of advanced methodological knowledge on Al , engineering skills for Al system design,
“创建”级别的总体目标是挑战并使学生能够发展高级能力，以根据可定制的数据集、编程工具或 AI 模型配置 AI 解决方案或制作新的 AI 工具，同时考虑开源选项。学生还将获得支持，以增强对更广泛的 AI 共同创作者社区的归属感，并提升他们与作为 AI 社会公民所需的社会责任的智力参与。表 4 中显示的课程目标旨在激励概述一组由高级方法论知识、AI 工程技能组成的高级能力。
and adaptivity in compliance with personal and corporate social responsibilities when creating and testing AI systems. The suggested pedagogical methods and approaches are designed to help solve ill-structured problems and nurture higher-order thinking, including through project-based learning, problem-based exploration of methodological knowledge, and multi-faceted ethical assessments. The suggested learning environments present recommendations on the configuration of datasets, Al programming tools and necessary computational devices to support complex learning with consideration of sharing AI resources and critically leveraging open-source options.
在创建和测试人工智能系统时，遵循个人和企业社会责任的适应性和合规性。建议的教学方法和策略旨在帮助解决结构不清晰的问题，并培养高阶思维，包括通过基于项目的学习、基于问题的方法论知识探索和多方面的伦理评估。建议的学习环境提供了关于数据集配置、人工智能编程工具和必要计算设备的建议，以支持复杂学习，同时考虑共享人工智能资源和批判性利用开源选项。

Table 4. Competency blocks for level 3: Create
表 4. 第三级能力模块：创建

STUDENT COMPETENCY 学生能力

课程目标（人工智能课程或学习计划应...）

CURRICULAR GOALS

(Al curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习环境。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Humancentred mindset 以人为本的思维方式

4.3.1 人工智能社会公民身份 - 学生应能够对人工智能对人类社会的影响形成批判性看法，并扩展他们以人为本的价值观，以促进人工智能的设计和使用，以实现包容性和可持续发展。他们应能够巩固自己的公民价值观和社会责任感。

4.3.1 Al society citizenship

- Students are expected to be able to build critical views on the impact of Al on human societies and expand their humancentred values to promoting the design and use of Al for inclusive and sustainable development. They should be able to solidify their civic values and the sense of social responsibility as

- CG4.3.1.1 Foster awareness of being a critical AI citizen: Enable students' to gain evidencebased insights into the pervasive adoption of AI as a supporting infrastructure of social activities in human societies. Foster their awareness and critical views on challenges that human societies are facing, such as prioritizing the acceleration of AI innovation while sacrificing safety and inclusivity, or prioritizing safety first, or and inclusive access. Develop students' skills in critiquing AIamplified biases against females, marginalized
- CG4.3.1.1 培养成为批判性人工智能公民的意识：使学生能够获得基于证据的见解，了解人工智能作为人类社会社会活动支持基础设施的广泛采用。培养他们对人类社会面临的挑战的意识和批判性看法，例如在牺牲安全性和包容性的情况下优先加速人工智能创新，或者优先考虑安全性和包容性。发展学生批判人工智能加剧对女性和边缘化群体的偏见的能力。

- Case studies on conflicts between an inclusive and just AI society and the threats AI poses to inclusion, justice and sustainability: Organize case studies or projectbased learning on the typical conflicts between an inclusive and just AI society and the risks Al poses to human-centred values. Organize a discussion of what is meant by sustainable, inclusive and just societies. Ask students to analyse cases where AI has been pervasively embedded into the infrastructure of societies, and interrogate how Al may amplify biases, widen economic and social
- 关于包容和公正的人工智能社会与人工智能对包容、公正和可持续性威胁之间冲突的案例研究：组织关于包容和公正的人工智能社会与人工智能对以人为本的价值观所带来的风险之间典型冲突的案例研究或基于项目的学习。组织讨论什么是可持续、包容和公正的社会。要求学生分析人工智能如何深入嵌入社会基础设施的案例，并质疑人工智能如何可能加剧偏见，扩大经济和社会差距。

- 断开连接的学习环境和资源，包括关于人工智能社会中职业和职业发展的工作表、翻转图表、报告或视频，以及关于人工智能的社会影响和环境影响的基于印刷的分析案例研究。 - 在线人工智能系统或本地可用的人工智能工具，用于体验和分析

- Unplugged learning settings and resources, including worksheets, flipcharts, reports or videos on jobs and career development in Al societies, and printbased analytical case studies on the societal implications and environmental impact of AI.

- Online AI systems or locally available Al tools for experiential and analytical

STUDENT COMPETENCY 学生能力

课程目标（人工智能课程或学习计划应...）

CURRICULAR GOALS

(Al curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)
学习环境（可以提供和调整以下学习设置。）

Humancentred mindset 以人为本的思维方式

a citizen in an AI society. Students are also expected to be able to reinforce their open-minded attitude and lifelong curiosity about learning and using Al to support self-actualization in the Al era.
在人工智能社会中的公民。学生还应能够增强他们的开放心态和对学习的终身好奇心，并利用人工智能支持在人工智能时代的自我实现。

ethnic groups and socioeconomically disadvantaged people, and the effects of Al on social relationships, norms and structures. Help reveal the reasons behind Al's profound impact on societies and assess how legal, ethical and social rules should be adapted to respond to the challenges.
族群和社会经济弱势群体，以及人工智能对社会关系、规范和结构的影响。帮助揭示人工智能对社会深远影响的原因，并评估法律、伦理和社会规则应如何调整以应对挑战。

- CG4.3.1.2 Nurture personal and social responsibilities in AI societies: Encourage students to share their views on what desirable Al societies would look like and delineate the main responsibilities and obligations that citizens need to undertake in order to build an inclusive, sustainable and just Al society, from the perspectives of both users and designers of AI. Support students to continuously refine their personal responsibilities as AI society citizens. Challenge students to examine challenges in upholding ethical principles for the design and use of AI in complex authentic situations with an aim to reinforce the resilience of their human-centred mindset.
- CG4.3.1.2 在人工智能社会中培养个人和社会责任感：鼓励学生分享他们对理想人工智能社会的看法，并阐明公民在建立一个包容、可持续和公正的人工智能社会中需要承担的主要责任和义务，既从人工智能的用户角度，也从设计者的角度出发。支持学生不断完善他们作为人工智能社会公民的个人责任。挑战学生审视在复杂真实情境中维护人工智能设计和使用的伦理原则所面临的挑战，旨在增强他们以人为本的思维方式的韧性。

- CG4.3.1.3 Nurture the sense of self-actualization as an AI citizen and the lifelong learning attitude to AI: Guide students to dynamically review the impact of the adoption of Al across sectors and the
- CG4.3.1.3 培养作为人工智能公民的自我实现感和对人工智能的终身学习态度：引导学生动态回顾人工智能在各个领域的应用影响和

inequality, undermine human agency and worsen climate change. Challenge students to take and defend positions on how existing AI technology can be regulated and how the design of the next generations of AI could be steered to make positive contributions to the building of inclusive and just societies.
不平等，削弱人类自主性并加剧气候变化。挑战学生对现有人工智能技术的监管方式以及下一代人工智能的设计如何引导以积极的方式为建立包容和公正的社会做出贡献进行辩论和辩护。

- Inquiry on the personal social responsibilities of being an Al society citizen: Arrange for students to conduct group discussions on the rights of citizens in an Al society, and jointly outline the main obligations and responsibilities that citizens should assume, taking into consideration both global and local contexts, as well as the perspectives of inclusion, equity, social justice, humancentred purposes and impacts on the environment and ecosystems. This includes ensuring humans have control and accountability over all key steps of the AI life cycle. Allow students to conduct and share their self-reflections on personal social responsibilities in an AI society.
- 关于作为人工智能社会公民的个人社会责任的探讨：安排学生进行小组讨论，讨论人工智能社会中公民的权利，并共同概述公民应承担的主要义务和责任，考虑全球和地方的背景，以及包容性、公平、社会正义、人本目的和对环境及生态系统的影响。这包括确保人类对人工智能生命周期的所有关键步骤拥有控制权和问责制。允许学生进行并分享他们对人工智能社会中个人社会责任的自我反思。

- Case studies on selfactualization in AI societies and their implications for lifelong learning: Organize case studies for students on the adoption of Al in work, life and social practices, and challenge them to review the implications of the adoption of AI for their personal goals, career development
- 关于人工智能社会中自我实现的案例研究及其对终身学习的影响：为学生组织关于人工智能在工作、生活和社会实践中应用的案例研究，并挑战他们审视人工智能的应用对个人目标和职业发展的影响。

tests including applications on smartphones that provide personal assistants, chatbots, and intelligent tutoring systems.
包括在智能手机上提供个人助理、聊天机器人和智能辅导系统的应用测试。

STUDENT COMPETENCY "CURRICULAR GOALS (Al curricula or programmes of study should...)" "SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.)" LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) Humancentred mindset a citizen in an AI society. Students are also expected to be able to reinforce their open-minded attitude and lifelong curiosity about learning and using Al to support self-actualization in the Al era. "ethnic groups and socioeconomically disadvantaged people, and the effects of Al on social relationships, norms and structures. Help reveal the reasons behind Al's profound impact on societies and assess how legal, ethical and social rules should be adapted to respond to the challenges. - CG4.3.1.2 Nurture personal and social responsibilities in AI societies: Encourage students to share their views on what desirable Al societies would look like and delineate the main responsibilities and obligations that citizens need to undertake in order to build an inclusive, sustainable and just Al society, from the perspectives of both users and designers of AI. Support students to continuously refine their personal responsibilities as AI society citizens. Challenge students to examine challenges in upholding ethical principles for the design and use of AI in complex authentic situations with an aim to reinforce the resilience of their human-centred mindset. - CG4.3.1.3 Nurture the sense of self-actualization as an AI citizen and the lifelong learning attitude to AI: Guide students to dynamically review the impact of the adoption of Al across sectors and the" "inequality, undermine human agency and worsen climate change. Challenge students to take and defend positions on how existing AI technology can be regulated and how the design of the next generations of AI could be steered to make positive contributions to the building of inclusive and just societies. - Inquiry on the personal social responsibilities of being an Al society citizen: Arrange for students to conduct group discussions on the rights of citizens in an Al society, and jointly outline the main obligations and responsibilities that citizens should assume, taking into consideration both global and local contexts, as well as the perspectives of inclusion, equity, social justice, humancentred purposes and impacts on the environment and ecosystems. This includes ensuring humans have control and accountability over all key steps of the AI life cycle. Allow students to conduct and share their self-reflections on personal social responsibilities in an AI society. - Case studies on selfactualization in AI societies and their implications for lifelong learning: Organize case studies for students on the adoption of Al in work, life and social practices, and challenge them to review the implications of the adoption of AI for their personal goals, career development" tests including applications on smartphones that provide personal assistants, chatbots, and intelligent tutoring systems.

| | STUDENT COMPETENCY | CURRICULAR GOALS (Al curricula or programmes of study should...) | SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) | LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) | | :---: | :---: | :---: | :---: | :---: | | Humancentred mindset | a citizen in an AI society. Students are also expected to be able to reinforce their open-minded attitude and lifelong curiosity about learning and using Al to support self-actualization in the Al era. | ethnic groups and socioeconomically disadvantaged people, and the effects of Al on social relationships, norms and structures. Help reveal the reasons behind Al's profound impact on societies and assess how legal, ethical and social rules should be adapted to respond to the challenges. - CG4.3.1.2 Nurture personal and social responsibilities in AI societies: Encourage students to share their views on what desirable Al societies would look like and delineate the main responsibilities and obligations that citizens need to undertake in order to build an inclusive, sustainable and just Al society, from the perspectives of both users and designers of AI. Support students to continuously refine their personal responsibilities as AI society citizens. Challenge students to examine challenges in upholding ethical principles for the design and use of AI in complex authentic situations with an aim to reinforce the resilience of their human-centred mindset. - CG4.3.1.3 Nurture the sense of self-actualization as an AI citizen and the lifelong learning attitude to AI: Guide students to dynamically review the impact of the adoption of Al across sectors and the | inequality, undermine human agency and worsen climate change. Challenge students to take and defend positions on how existing AI technology can be regulated and how the design of the next generations of AI could be steered to make positive contributions to the building of inclusive and just societies. - Inquiry on the personal social responsibilities of being an Al society citizen: Arrange for students to conduct group discussions on the rights of citizens in an Al society, and jointly outline the main obligations and responsibilities that citizens should assume, taking into consideration both global and local contexts, as well as the perspectives of inclusion, equity, social justice, humancentred purposes and impacts on the environment and ecosystems. This includes ensuring humans have control and accountability over all key steps of the AI life cycle. Allow students to conduct and share their self-reflections on personal social responsibilities in an AI society. - Case studies on selfactualization in AI societies and their implications for lifelong learning: Organize case studies for students on the adoption of Al in work, life and social practices, and challenge them to review the implications of the adoption of AI for their personal goals, career development | tests including applications on smartphones that provide personal assistants, chatbots, and intelligent tutoring systems. |

学生能力

STUDENT

COMPETENCY

课程目标（人工智能课程或学习计划应...）

CURRICULAR GOALS

(AI curricula or programmes

of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers

can consider and adapt the

following learning methods.)

学习环境（可以提供和调整以下学习环境。）

LEARNING

ENVIRONMENTS

(The following learning

settings can be

provided and adapted.)

centred 以...为中心

mindset 心态

STUDENT COMPETENCY 学生能力

课程目标（所有课程或学习计划应...）

CURRICULAR GOALS

(Al curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习设置。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Ethics of AI 人工智能的伦理

multi-stakeholders to review AI regulations and inform adaptation.
多方利益相关者审查人工智能法规并提供适应建议。

to applying principles and regulations to the evaluation of the 'ethics by design' of specific AI systems or tools. Develop their critical thinking skills by requesting them to propose recommendations to the creators of Al systems to remedy any identified violations of ethical principles or regulations, and mitigate any harms their Al tools have caused.
将原则和法规应用于特定人工智能系统或工具的“设计伦理”评估。通过要求他们向人工智能系统的创建者提出建议，以补救任何已识别的伦理原则或法规的违反，并减轻他们的人工智能工具造成的任何伤害，来培养他们的批判性思维能力。

- CG4.3.2.3 Cultivating social responsibilities to uphold 'ethics by design' in regulations on Al: Drawing on selected Al regulations, guide students to evaluate how they align with the ethics-by-design approach and the extent to which corresponding measures are sufficient in monitoring and regulating typical ethical risks embedded in algorithms and AI systems. Enhance students' awareness of, and skills in carrying out, their social responsibilities by guiding them to recommend modifications of existing local regulations or draft proposals on the development of regulations to govern ethics by design in their communities.
- CG4.3.2.3 培养社会责任，以在人工智能的法规中坚持“设计伦理”：通过选定的人工智能法规，引导学生评估这些法规与设计伦理方法的对齐程度，以及相应措施在监测和规范嵌入算法和人工智能系统的典型伦理风险方面的充分性。通过引导学生建议修改现有地方法规或起草关于在其社区中治理设计伦理的法规的提案，增强他们对社会责任的意识和履行这些责任的能力。

items). Guide students to construct or adapt an ethical label to audit the intent of the designers of selected AI systems and services, including collecting information beyond their published statements (e.g. the creators of a shoppingrecommendation platform state that its intent is to help customers find the most appropriate products, while the hidden purpose may be to make users dependent on or addicted to using the platform). Write reports on the findings of the audit.
引导学生构建或调整一个伦理标签，以审计选定人工智能系统和服务设计者的意图，包括收集超出其发布声明的信息（例如，某购物推荐平台的创建者声称其意图是帮助客户找到最合适的产品，而隐藏的目的可能是让用户依赖或上瘾于使用该平台）。撰写审计结果的报告。

- Simulating the use of an ethics matrix to review regulations on Al and suggest adaptations: Invite students to research an ethics matrix for involving relevant stakeholders in regulations on AI. Support them to construct an adaptive ethics matrix with core ethical principles forming its columns and relevant stakeholders forming the rows (e.g. Al creators, regulators, institutional deployers and individual users). Students can apply their matrix to analyse relevant articles of a selected regulation and draft reports or reviews including recommendations for further adapting or iterating the regulations. Where local regulations are not available, write a proposal on the creation of a new Al regulation with an outline of articles for relevant stakeholders.
- 模拟使用伦理矩阵来审查人工智能的法规并提出适应建议：邀请学生研究一个伦理矩阵，以便在人工智能的法规中涉及相关利益相关者。支持他们构建一个适应性伦理矩阵，以核心伦理原则作为列，相关利益相关者作为行（例如，人工智能创造者、监管者、机构部署者和个人用户）。学生可以应用他们的矩阵分析所选法规的相关条款，并撰写报告或评论，包括对进一步适应或迭代法规的建议。如果当地法规不可用，则撰写关于创建新人工智能法规的提案，并概述相关利益相关者的条款。

STUDENT COMPETENCY 学生能力

课程目标（人工智能课程或学习计划应...）

CURRICULAR GOALS

(Al curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习设置。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

AI techniques and applications
人工智能技术和应用

4.3.3 创建人工智能工具 - 学生应能够深化和应用数据和算法的知识和技能，以定制现有的人工智能工具包，创建基于任务的人工智能工具。学生还应将以人为本的思维方式和伦理考虑融入对现有人工智能资源的评估以及对自创人工智能工具的测试中。他们还应培养参与人工智能创作所需的社会和情感技能，包括适应性、复杂沟通和团队合作技能。

4.3.3 Creating AI tools

- Students are expected to be able to deepen and apply knowledge and skills on data and algorithms to customize existing Al toolkits to create task-based Al tools. Students are expected to integrate their human-centred mindset and ethical considerations into the assessment of the existing AI resources and the test of self-created Al tools. They are also expected to foster social and emotional skills needed to engage in creation with Al including adaptivity, complex communication and teamwork skills.

- CG4.3.3.1 Challenge and enable advanced skills to develop task-based AI tools: Provide task-based learning opportunities so students can transfer their values, knowledge and skills to crafting an AI tool based on existing AI models or toolkits. Support their mastery of advanced skills in critically analysing the relevance of existing Al tools to specific tasks, assessing its data collection and processing needs, deciding on whether a low-code approach will be adopted or AI algorithms and programming language are required, and carrying out the operational customization and/or programming.
- CG4.3.3.1 挑战并激发高级技能以开发基于任务的人工智能工具：提供基于任务的学习机会，使学生能够将他们的价值观、知识和技能转移到基于现有人工智能模型或工具包的人工智能工具的制作上。支持他们掌握高级技能，批判性地分析现有人工智能工具与特定任务的相关性，评估其数据收集和处理需求，决定是否采用低代码方法或需要人工智能算法和编程语言，并进行操作定制和/或编程。

- CG4.3.3.2 Enhance students' creativity in applying AI knowledge and skills to customize Al toolkits and coding: Design tasks around customizing Al tools to solve authentic tasks. Guide students to acquire skills in leveraging AI-development platforms or toolkits, enhancing datasets, and modifying programming codes, including those based on open-source options; challenge and support students to explore and test creative ideas on the design of Al tools to solve variations of problems.
- CG4.3.3.2 提升学生在应用人工智能知识和技能定制人工智能工具包和编码方面的创造力：围绕定制人工智能工具以解决真实任务设计任务。指导学生掌握利用人工智能开发平台或工具包、增强数据集和修改编程代码（包括基于开源选项的代码）的技能；挑战并支持学生探索和测试在设计人工智能工具以解决各种问题时的创造性想法。

- Task-based enhancement of datasets and programming codes for crafting an Al tool: Organize students to modify a dataset or create a new one for real-world contexts, by drawing on an authentic task such as monitoring the energy consumption of local schools or households, forecasting weather for a specific location or route, or tracking an epidemic disease. Teach and facilitate students to leverage automatic data-collection tools (e.g. BeautifulSoup

^{19}

for scraping information from webpages); apply AI programming skills to clean, encode and preprocess the data; and use the data to customize AI models or craft Al tools.
- 基于任务的数据集和编程代码增强以制作 AI 工具：组织学生修改数据集或为现实世界情境创建新数据集，借助真实任务，例如监测当地学校或家庭的能源消耗、为特定地点或路线预测天气，或追踪流行病。教导并引导学生利用自动数据收集工具（例如 BeautifulSoup

^{19}

从网页抓取信息）；应用 AI 编程技能清理、编码和预处理数据；并使用数据定制 AI 模型或制作 AI 工具。

- Al application performance test lab: Guide students to search for and adapt a free and/or open-source performance matrix for the testing of AI applications (e.g. accuracy, precision, F-1 score, confusion matrices and ROC curves). Let students experience the use of adapted tools to test the performance and technological robustness of the crafted AI application, and simulate users' feedback on ethical compliance. Use automated tools to generate visualized reporting and summarize recommendations on the optimization of the AI application.
- AI 应用性能测试实验室：指导学生搜索并适应免费的和/或开源的性能矩阵，用于测试 AI 应用的性能（例如准确率、精确率、F-1 分数、混淆矩阵和 ROC 曲线）。让学生体验使用适应工具测试所制作 AI 应用的性能和技术稳健性，并模拟用户对伦理合规性的反馈。使用自动化工具生成可视化报告并总结关于优化 AI 应用的建议。

- 本地可访问的免费和/或开源在线数据集、人工智能工具和编程库。 - 本地可访问的免费和/或开源数据分析工具。 - 本地可访问的基于云的计算资源、本地托管的计算资源（例如学校服务器）或由可信机构或行业机构共享的计算资源。

- Locally accessible free and/or opensource online datasets, Al tools and programming libraries.

- Locally accessible free and/or open-source data analytics tools.

- Locally accessible cloud-based computing resources, locally hosted computing resources (e.g. a school server), or computing resources shared by trustable institutions or industry agencies.

STUDENT COMPETENCY "CURRICULAR GOALS (Al curricula or programmes of study should...)" "SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.)" "LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)" AI techniques and applications "4.3.3 Creating AI tools - Students are expected to be able to deepen and apply knowledge and skills on data and algorithms to customize existing Al toolkits to create task-based Al tools. Students are expected to integrate their human-centred mindset and ethical considerations into the assessment of the existing AI resources and the test of self-created Al tools. They are also expected to foster social and emotional skills needed to engage in creation with Al including adaptivity, complex communication and teamwork skills." "- CG4.3.3.1 Challenge and enable advanced skills to develop task-based AI tools: Provide task-based learning opportunities so students can transfer their values, knowledge and skills to crafting an AI tool based on existing AI models or toolkits. Support their mastery of advanced skills in critically analysing the relevance of existing Al tools to specific tasks, assessing its data collection and processing needs, deciding on whether a low-code approach will be adopted or AI algorithms and programming language are required, and carrying out the operational customization and/or programming. - CG4.3.3.2 Enhance students' creativity in applying AI knowledge and skills to customize Al toolkits and coding: Design tasks around customizing Al tools to solve authentic tasks. Guide students to acquire skills in leveraging AI-development platforms or toolkits, enhancing datasets, and modifying programming codes, including those based on open-source options; challenge and support students to explore and test creative ideas on the design of Al tools to solve variations of problems." "- Task-based enhancement of datasets and programming codes for crafting an Al tool: Organize students to modify a dataset or create a new one for real-world contexts, by drawing on an authentic task such as monitoring the energy consumption of local schools or households, forecasting weather for a specific location or route, or tracking an epidemic disease. Teach and facilitate students to leverage automatic data-collection tools (e.g. BeautifulSoup ^(19) for scraping information from webpages); apply AI programming skills to clean, encode and preprocess the data; and use the data to customize AI models or craft Al tools. - Al application performance test lab: Guide students to search for and adapt a free and/or open-source performance matrix for the testing of AI applications (e.g. accuracy, precision, F-1 score, confusion matrices and ROC curves). Let students experience the use of adapted tools to test the performance and technological robustness of the crafted AI application, and simulate users' feedback on ethical compliance. Use automated tools to generate visualized reporting and summarize recommendations on the optimization of the AI application." "- Locally accessible free and/or opensource online datasets, Al tools and programming libraries. - Locally accessible free and/or open-source data analytics tools. - Locally accessible cloud-based computing resources, locally hosted computing resources (e.g. a school server), or computing resources shared by trustable institutions or industry agencies."

| | STUDENT COMPETENCY | CURRICULAR GOALS (Al curricula or programmes of study should...) | SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) | LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) | | :---: | :---: | :---: | :---: | :---: | | AI techniques and applications | 4.3.3 Creating AI tools - Students are expected to be able to deepen and apply knowledge and skills on data and algorithms to customize existing Al toolkits to create task-based Al tools. Students are expected to integrate their human-centred mindset and ethical considerations into the assessment of the existing AI resources and the test of self-created Al tools. They are also expected to foster social and emotional skills needed to engage in creation with Al including adaptivity, complex communication and teamwork skills. | - CG4.3.3.1 Challenge and enable advanced skills to develop task-based AI tools: Provide task-based learning opportunities so students can transfer their values, knowledge and skills to crafting an AI tool based on existing AI models or toolkits. Support their mastery of advanced skills in critically analysing the relevance of existing Al tools to specific tasks, assessing its data collection and processing needs, deciding on whether a low-code approach will be adopted or AI algorithms and programming language are required, and carrying out the operational customization and/or programming. - CG4.3.3.2 Enhance students' creativity in applying AI knowledge and skills to customize Al toolkits and coding: Design tasks around customizing Al tools to solve authentic tasks. Guide students to acquire skills in leveraging AI-development platforms or toolkits, enhancing datasets, and modifying programming codes, including those based on open-source options; challenge and support students to explore and test creative ideas on the design of Al tools to solve variations of problems. | - Task-based enhancement of datasets and programming codes for crafting an Al tool: Organize students to modify a dataset or create a new one for real-world contexts, by drawing on an authentic task such as monitoring the energy consumption of local schools or households, forecasting weather for a specific location or route, or tracking an epidemic disease. Teach and facilitate students to leverage automatic data-collection tools (e.g. BeautifulSoup ${ }^{19}$ for scraping information from webpages); apply AI programming skills to clean, encode and preprocess the data; and use the data to customize AI models or craft Al tools. - Al application performance test lab: Guide students to search for and adapt a free and/or open-source performance matrix for the testing of AI applications (e.g. accuracy, precision, F-1 score, confusion matrices and ROC curves). Let students experience the use of adapted tools to test the performance and technological robustness of the crafted AI application, and simulate users' feedback on ethical compliance. Use automated tools to generate visualized reporting and summarize recommendations on the optimization of the AI application. | - Locally accessible free and/or opensource online datasets, Al tools and programming libraries. - Locally accessible free and/or open-source data analytics tools. - Locally accessible cloud-based computing resources, locally hosted computing resources (e.g. a school server), or computing resources shared by trustable institutions or industry agencies. |

\begin{array}{lllll} \begin{array}{ll} STUDENT \\ COMPETENCY \end{array} & \begin{matrix} CURRICULAR GOALS \\ (AI curricula or programmes \\ of study should...) \end{matrix} & \begin{matrix} SUGGESTED \\ PEDAGOGICAL METHODS \end{matrix} \\ (Institutions and teachers \\ can consider and adapt the \\ following learning methods.) \end{array} \begin{array}{l} LEARNING \\ ENVIRONMENTS \\ (The following learning \\ settings can be \\ provided and adapted.) \end{array}}

STUDENT COMPETENCY 学生能力

课程目标（人工智能课程或学习计划应...）

CURRICULAR GOALS

(AI curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习设置。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

Al system design 人工智能系统设计

人工智能模型及其对个体用户、社会和环境的影响。他们应该能够获得适合年龄的技术技能，以改善数据集的质量，重新配置算法，并根据测试结果和反馈增强架构。他们应该能够在模拟决策时应用以人为本的思维方式和伦理原则，判断何时应关闭人工智能系统以及如何减轻其负面影响。他们还应被期望在更大的人工智能社区中培养作为共同创造者的身份。

an AI model and its impact on individual users, societies and the environment.

They should be able to acquire age-appropriate technical skills to improve the quality of datasets, reconfigure algorithms and enhance architectures in response to results of tests and feedback. They should be able to apply human-centred mindset and ethical principles in simulating decision-making on when an Al system should be shut down and how its negative impact can be mitigated. They are also be expected to cultivate their identities as co-creators in the larger Al community.

protection of data privacy; measuring the performance of the Al system; and studying users' feedback to evaluate its broader societal and environmental impact.
保护数据隐私；衡量人工智能系统的性能；以及研究用户反馈以评估其对更广泛社会和环境的影响。

- CG4.3.4.2 Support the building of technical skills and social responsibilities in optimizing, reconfiguring or shutting down an AI system: Offer simulation activities for students to understand corporate social responsibility and acquire interdisciplinary skills to make decisions on the iteration of an Al system based on the results of testing and users' feedback. The activities should involve development of students' technical skills for three possible scenarios: (1) optimization: optimizing the datasets, algorithms, model, design functionalities and/or interface; (2) reconfiguration: revisiting problem scoping and reconfiguring the Al system; and, (3) shutting down: where it is proven that the AI system violates human rights or harms vulnerable groups, students should learn to make decisions to shut down the Al model and quickly put remedial strategies in place.
- CG4.3.4.2 支持在优化、重新配置或关闭人工智能系统中建立技术技能和社会责任：提供模拟活动，让学生理解企业社会责任，并获得跨学科技能，以便根据测试结果和用户反馈对人工智能系统的迭代做出决策。活动应涉及学生技术技能的发展，针对三种可能的情境：（1）优化：优化数据集、算法、模型、设计功能和/或界面；（2）重新配置：重新审视问题范围并重新配置人工智能系统；（3）关闭：在证明人工智能系统侵犯人权或对弱势群体造成伤害的情况下，学生应学习做出关闭人工智能模型的决策，并迅速制定补救策略。

- CG4.3.4.3 Foster students' self-identities as cocreators in the Al era: Guide students to nurture the responsibilities of being
- CG4.3.4.3 培养学生在人工智能时代作为共同创造者的自我认同：引导学生培养作为共同创造者的责任。

matrices and ROC curves) to measure the performance of the AI system. Design and apply research methods (e.g. gathering age-appropriate qualitative and quantitative market data) including feedback from (simulated) end users to study the societal implications and environmental impact of the adoption of the Al model. Synthesize the results and report them in a visual format.
使用矩阵和 ROC 曲线来衡量 AI 系统的性能。设计并应用研究方法（例如，收集适合年龄的定性和定量市场数据），包括来自（模拟）最终用户的反馈，以研究采用 AI 模型的社会影响和环境影响。综合结果并以可视化格式报告。

- Simulating Al engineers' corporate decision-making on the iteration of an AI model: Organize students to play the roles of AI engineers to integrate and interpret results from feedback, considering both AI system design and corporate social responsibility. Make an appropriate decision from multiple choices on the iteration of the AI model: (1) optimization, where the problem scoping is validated and the datasets, algorithms, Al model or interfaces may need to be optimized; (2) reconfiguration, where fundamental flaws are discovered through tests and/or users' feedback in the problem scoping and/ or configuration of the architecture; or (3) shutdown, where it is proven that an Al model violates human rights or harms vulnerable groups. Support students to acquire technical skills for optimization and reconfiguration, and learn
- 模拟人工智能工程师在人工智能模型迭代中的企业决策：组织学生扮演人工智能工程师的角色，整合和解释反馈结果，考虑人工智能系统设计和企业社会责任。从多个选择中做出适当的决策关于人工智能模型的迭代：（1）优化，验证问题范围并可能需要优化数据集、算法、人工智能模型或接口；（2）重新配置，通过测试和/或用户反馈发现问题范围和/或架构配置中的根本缺陷；或（3）关闭，证明人工智能模型侵犯人权或伤害弱势群体。支持学生获得优化和重新配置的技术技能，并学习

和人工智能模型的性能测试。 - 获取适用的人工智能法规或治理框架。 - 本地可访问的在线协作平台，以支持资源共享、同伴学习以及人工智能工具的协作设计和创建（例如 GitHub、arXiV 或论坛小组）。

and performance testing of AI models.

- Access to applicable regulations on Al or governance frameworks.

- Locally accessible online collaborative platforms to support resource sharing, peer learning, and the collaborative design and creation of Al tools (e.g. GitHub, arXiV or forum groups).

STUDENT COMPETENCY "CURRICULAR GOALS (AI curricula or programmes of study should...)" "SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.)" "LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.)" Al system design "an AI model and its impact on individual users, societies and the environment. They should be able to acquire age-appropriate technical skills to improve the quality of datasets, reconfigure algorithms and enhance architectures in response to results of tests and feedback. They should be able to apply human-centred mindset and ethical principles in simulating decision-making on when an Al system should be shut down and how its negative impact can be mitigated. They are also be expected to cultivate their identities as co-creators in the larger Al community." "protection of data privacy; measuring the performance of the Al system; and studying users' feedback to evaluate its broader societal and environmental impact. - CG4.3.4.2 Support the building of technical skills and social responsibilities in optimizing, reconfiguring or shutting down an AI system: Offer simulation activities for students to understand corporate social responsibility and acquire interdisciplinary skills to make decisions on the iteration of an Al system based on the results of testing and users' feedback. The activities should involve development of students' technical skills for three possible scenarios: (1) optimization: optimizing the datasets, algorithms, model, design functionalities and/or interface; (2) reconfiguration: revisiting problem scoping and reconfiguring the Al system; and, (3) shutting down: where it is proven that the AI system violates human rights or harms vulnerable groups, students should learn to make decisions to shut down the Al model and quickly put remedial strategies in place. - CG4.3.4.3 Foster students' self-identities as cocreators in the Al era: Guide students to nurture the responsibilities of being" "matrices and ROC curves) to measure the performance of the AI system. Design and apply research methods (e.g. gathering age-appropriate qualitative and quantitative market data) including feedback from (simulated) end users to study the societal implications and environmental impact of the adoption of the Al model. Synthesize the results and report them in a visual format. - Simulating Al engineers' corporate decision-making on the iteration of an AI model: Organize students to play the roles of AI engineers to integrate and interpret results from feedback, considering both AI system design and corporate social responsibility. Make an appropriate decision from multiple choices on the iteration of the AI model: (1) optimization, where the problem scoping is validated and the datasets, algorithms, Al model or interfaces may need to be optimized; (2) reconfiguration, where fundamental flaws are discovered through tests and/or users' feedback in the problem scoping and/ or configuration of the architecture; or (3) shutdown, where it is proven that an Al model violates human rights or harms vulnerable groups. Support students to acquire technical skills for optimization and reconfiguration, and learn" "and performance testing of AI models. - Access to applicable regulations on Al or governance frameworks. - Locally accessible online collaborative platforms to support resource sharing, peer learning, and the collaborative design and creation of Al tools (e.g. GitHub, arXiV or forum groups)."

| | STUDENT COMPETENCY | CURRICULAR GOALS (AI curricula or programmes of study should...) | SUGGESTED PEDAGOGICAL METHODS (Institutions and teachers can consider and adapt the following learning methods.) | LEARNING ENVIRONMENTS (The following learning settings can be provided and adapted.) | | :---: | :---: | :---: | :---: | :---: | | Al system design | an AI model and its impact on individual users, societies and the environment. They should be able to acquire age-appropriate technical skills to improve the quality of datasets, reconfigure algorithms and enhance architectures in response to results of tests and feedback. They should be able to apply human-centred mindset and ethical principles in simulating decision-making on when an Al system should be shut down and how its negative impact can be mitigated. They are also be expected to cultivate their identities as co-creators in the larger Al community. | protection of data privacy; measuring the performance of the Al system; and studying users' feedback to evaluate its broader societal and environmental impact. - CG4.3.4.2 Support the building of technical skills and social responsibilities in optimizing, reconfiguring or shutting down an AI system: Offer simulation activities for students to understand corporate social responsibility and acquire interdisciplinary skills to make decisions on the iteration of an Al system based on the results of testing and users' feedback. The activities should involve development of students' technical skills for three possible scenarios: (1) optimization: optimizing the datasets, algorithms, model, design functionalities and/or interface; (2) reconfiguration: revisiting problem scoping and reconfiguring the Al system; and, (3) shutting down: where it is proven that the AI system violates human rights or harms vulnerable groups, students should learn to make decisions to shut down the Al model and quickly put remedial strategies in place. - CG4.3.4.3 Foster students' self-identities as cocreators in the Al era: Guide students to nurture the responsibilities of being | matrices and ROC curves) to measure the performance of the AI system. Design and apply research methods (e.g. gathering age-appropriate qualitative and quantitative market data) including feedback from (simulated) end users to study the societal implications and environmental impact of the adoption of the Al model. Synthesize the results and report them in a visual format. - Simulating Al engineers' corporate decision-making on the iteration of an AI model: Organize students to play the roles of AI engineers to integrate and interpret results from feedback, considering both AI system design and corporate social responsibility. Make an appropriate decision from multiple choices on the iteration of the AI model: (1) optimization, where the problem scoping is validated and the datasets, algorithms, Al model or interfaces may need to be optimized; (2) reconfiguration, where fundamental flaws are discovered through tests and/or users' feedback in the problem scoping and/ or configuration of the architecture; or (3) shutdown, where it is proven that an Al model violates human rights or harms vulnerable groups. Support students to acquire technical skills for optimization and reconfiguration, and learn | and performance testing of AI models. - Access to applicable regulations on Al or governance frameworks. - Locally accessible online collaborative platforms to support resource sharing, peer learning, and the collaborative design and creation of Al tools (e.g. GitHub, arXiV or forum groups). |

STUDENT COMPETENCY 学生能力

课程目标（人工智能课程或学习计划应...）

CURRICULAR GOALS

(AI curricula or programmes of study should...)

建议的教学方法（机构和教师可以考虑并调整以下学习方法。）

SUGGESTED

PEDAGOGICAL METHODS

(Institutions and teachers can consider and adapt the following learning methods.)

学习环境（可以提供和调整以下学习环境。）

LEARNING ENVIRONMENTS

(The following learning settings can be provided and adapted.)

AI system design 人工智能系统设计

a co-creator of Al tools and the 'driver' of the design of the next generation of Al technology. Develop their sense of belonging to the larger AI community, and encourage them to critically analyse the longterm impacts of AI systems on social relations and individual behaviours by drawing on real experiences of designing and building Al systems. Discuss how regulations or policies should be adapted or created to enhance the governance of AI.
作为人工智能工具的共同创造者和下一代人工智能技术设计的“推动者”。培养他们对更大人工智能社区的归属感，并鼓励他们通过真实的设计和构建人工智能系统的经验，批判性地分析人工智能系统对社会关系和个人行为的长期影响。讨论如何调整或创建法规或政策，以增强人工智能的治理。

就关闭人工智能模型进行谈判和决策，以及可能的补救策略。- 与人工智能创作者社区的互动：促进有兴趣的学生加入本地或在线的人工智能共同创造者社区。鼓励他们参与在线讨论或协作开发人工智能工具，并分享开源数据集和算法或人工智能工具包的示例。

to negotiate and make decisions about shutting down the Al model and what are the possible remedy strategies.

- Engagement with communities of AI creators: Facilitate interested students to join local or online communities of AI co-creators. Encourage them to participate in online discussions or collaborative development of Al tools, and share open-source datasets and examples of algorithms or Al toolkits.

Chapter 5: Applying the framework
第五章：应用框架

This chapter provides some further guidance on the types of considerations that can feed into the successful development and deployment of curricula.
本章提供了一些进一步的指导，关于成功开发和部署课程所需考虑的类型。

5.1 Aligning Al competencies as the foundation for national Al strategies
5.1 将人工智能能力作为国家人工智能战略的基础

The development and implementation of national strategies for Al vary across countries. Around 70 countries have released strategy documents on Al, which often position education as the sector to build local human resources and talent in AI.
各国在人工智能的国家战略发展和实施上存在差异。大约 70 个国家发布了关于人工智能的战略文件，这些文件通常将教育定位为培养本地人力资源和人工智能人才的领域。

Abstract 摘要

In countries with well-entrenched national strategies, the AI CFS can be aligned with existing policy frameworks as a foundation to foster the human-centred mindset and values needed to implement regulations on the ethics of AI, prepare people to be responsible Al users and citizens, and develop local communities of Al co-creators at scale.
在拥有成熟国家战略的国家，人工智能能力框架可以与现有政策框架对齐，作为促进人本思维和价值观的基础，以实施人工智能伦理的相关法规，培养人们成为负责任的人工智能用户和公民，并大规模发展本地人工智能共同创造者社区。

Box 1: Recommendation on the Ethics of Artificial Intelligence
框架 1：关于人工智能伦理的建议

Member States should promote the acquisition of’prerequisite skills’ for Al education, such as basic literacy, numeracy, coding and digital skills, and media and information literacy, as well as critical and creative thinking, teamwork, communication, socio-emotional and AI ethics skills, especially in countries and in regions or areas within countries where there are notable gaps in the education of these skills.
各成员国应促进人工智能教育的“先决技能”的获取，例如基本的读写能力、算术能力、编程和数字技能、媒体和信息素养，以及批判性和创造性思维、团队合作、沟通、社会情感和人工智能伦理技能，特别是在那些在这些技能教育方面存在显著差距的国家和地区。

Member States should promote general awareness programmes about AI developments, including on data and the opportunities and challenges brought about by Al technologies, the impact of AI systems on human rights and their implications, including children’s rights. These programmes should be accessible to nontechnical as well as technical groups.
各成员国应促进关于人工智能发展的公众意识项目，包括数据以及人工智能技术带来的机遇和挑战，人工智能系统对人权的影响及其含义，包括儿童权利。这些项目应对技术和非技术群体均可获取。

Source: UNESCO, 2022a, pp. 33-34
来源：联合国教科文组织，2022a，第 33-34 页

If a national strategy for Al is released and well-implemented, the implementation of the AI CFS and AI curricula for students should be planned and supported administratively and financially within the broad framework of the Al strategy. Such national strategies are usually triggered by policy responses to the wide-ranging and disruptive impact of AI on work, in terms of both Al-driven job displacement
如果发布并有效实施国家人工智能战略，则应在人工智能战略的广泛框架内，计划并在行政和财务上支持学生的人工智能能力框架和人工智能课程。这种国家战略通常是对人工智能在工作中广泛而颠覆性影响的政策响应所引发的，包括人工智能驱动的就业置换。
and AI-supported job creation, as well as the prospect of new employment skills that the adoption of Al may require. The foremost policy response to this disruption is system-wide strategies on AI competency development that are comprised of funding and incentive mechanisms as well as specific courses on Al that streamline different trajectories as appropriate for each sector, including school education, technical and
以及人工智能支持的就业创造，以及采用人工智能可能需要的新就业技能的前景。对此次变革的首要政策响应是针对人工智能能力发展的系统性策略，这些策略包括资金和激励机制，以及针对人工智能的特定课程，旨在根据各个行业的需要，简化不同的发展路径，包括学校教育、技术和
vocational education and training (TVET), higher education, upskilling and reskilling for employees, and lifelong learning programmes for citizens. For countries without an adopted strategy, the AI CFS can serve as a trigger to raise awareness about the importance of national policies on Al in general, and on the development of Al competencies in particular.
职业教育与培训（TVET）、高等教育、员工技能提升与再培训，以及面向公民的终身学习项目。对于没有采纳战略的国家，AI 能力框架可以作为一个契机，提高人们对国家在人工智能方面政策重要性的认识，特别是在人工智能能力发展方面。

The implementation of such strategies and policies is expected to start with the assessment of readiness and programme gaps. The processes and outcomes of the implementation are usually monitored and evaluated, and policy-makers should establish early and regular monitoring of Al competency development programmes
这些策略和政策的实施预计将从评估准备情况和项目差距开始。实施的过程和结果通常会被监测和评估，政策制定者应建立对人工智能能力发展项目的早期和定期监测。
when setting up overall mechanisms and methodologies to track implementation. To evaluate AI curricula or agile education programmes, it is particularly important to formulate criteria that cover: the readiness of students and teachers; deficiencies in training and support for teachers’ professional development; gaps in curricular goals and content that need to be addressed to support the national Al vision; additions needed to the curricular content to meet immediate and near future needs of the markets; mechanisms for the mobilization and validation of intersectoral support; the degree of curriculum integration; readiness of learning environments; and the quality of the implementation of the curriculum.
在建立整体机制和方法来跟踪实施时，评估人工智能课程或敏捷教育项目时，特别重要的是制定涵盖以下内容的标准：学生和教师的准备情况；教师专业发展培训和支持的不足；需要解决的课程目标和内容的差距，以支持国家人工智能愿景；满足市场近期和未来需求的课程内容的补充；动员和验证跨部门支持的机制；课程整合的程度；学习环境的准备情况；以及课程实施的质量。

Box 2: Supporting human resource development: The Republic of Korea's National Strategy for Artificial Intelligence
案例 2：支持人力资源发展：韩国的国家人工智能战略

The Republic of Korea’s National Strategy for Artificial Intelligence has three main focus areas:
韩国的国家人工智能战略有三个主要重点领域：
(1) Establish reliable AI infrastructure, including to support human talent and improve technology; (2) expand the utilization of Al throughout the industrial and social sectors; and (3) respond proactively to social changes, including labour market needs. The strategy seeks to develop an AI ecosystem that results in the full-scale utilization of AI, and establishes the Republic of Korea as a global leader in people-centred artificial intelligence.
（1）建立可靠的人工智能基础设施，包括支持人才能和改善技术；（2）扩大人工智能在工业和社会各个领域的应用；（3）积极应对社会变化，包括劳动市场需求。该战略旨在发展一个人工智能生态系统，实现人工智能的全面应用，并将韩国确立为以人为本的人工智能全球领导者。

To support the achievement of this goal, the Republic of Korea has focused on revising regulations to create a more industry-friendly environment and nurture the productive use of data and AI innovations, the use of AI to streamline governance, the establishment of regulations on Al ethics, and the building of human capital in Al from as early as primary school. The strategy proposes an interdisciplinary AI curriculum and the definition of AI competencies based on the needs of four categories of populations: (1) the general public, who need to be able to use AI, as well as acquire basic Al and data literacy, including knowledge of AI ethics; (2) AI practitioners, who apply AI and software tools in ‘AI + X’ environments in the labour market; (3) AI professionals, who develop AI platforms and systems; and (4) Al talents, who will resolve AI issues and develop new AI models and algorithms.
为了支持实现这一目标，韩国专注于修订法规，以创造一个更有利于行业的环境，并培养数据和人工智能创新的有效使用，利用人工智能简化治理，建立人工智能伦理的相关法规，以及从小学阶段开始培养人工智能的人力资本。该战略提出了跨学科的人工智能课程，并根据四类人群的需求定义人工智能能力：（1）普通公众，他们需要能够使用人工智能，并获得基本的人工智能和数据素养，包括人工智能伦理知识；（2）人工智能从业者，他们在劳动市场的“人工智能 + X”环境中应用人工智能和软件工具；（3）人工智能专业人员，他们开发人工智能平台和系统；（4）人工智能人才，他们将解决人工智能问题并开发新的人工智能模型和算法。

In alignment with competency development for these four categories, the strategy suggests regulations to upskill and upgrade industry professionals to the level of professorships in Al , support the expansion of existing AI departments, and mobilize more departments to offer programmes related to Al , including through expanding the scale and diversity of education and research programmes in Al at the master’s and doctoral levels and through creating interdisciplinary majors on AI.
为了与这四个类别的能力发展保持一致，该战略建议制定法规，以提升和升级行业专业人员到人工智能教授的水平，支持现有人工智能部门的扩展，并动员更多部门提供与人工智能相关的课程，包括通过扩大人工智能硕士和博士层次的教育和研究项目的规模和多样性，以及通过创建跨学科的人工智能专业。

As for school level, the strategy seeks to expand learning opportunities on AI with a focus on computational thinking. At the lower grades of primary school, students are offered experiential engagement with Al to foster their interest and curiosity; at the higher primary grades, students are supported to extend their knowledge and skills through applying AI in the learning of core subjects. Secondary-level students have the opportunity to attend Al-centred schools to complete a more advanced AI curriculum. Teachers are also supported to enhance their knowledge and skills on Al through integrating AI in their initial training programmes and providing new degrees on Al pedagogy integration.
在学校层面，该战略旨在扩大人工智能的学习机会，重点关注计算思维。在小学低年级，学生通过体验式参与人工智能来培养他们的兴趣和好奇心；在高年级小学，学生通过在核心学科的学习中应用人工智能来扩展他们的知识和技能。中学生有机会参加以人工智能为中心的学校，完成更高级的人工智能课程。教师也得到支持，以通过在其初始培训项目中整合人工智能和提供新的人工智能教育整合学位来增强他们的知识和技能。

Source: Ministry of Science and ICT, Republic of Korea, 2019
来源：韩国科学技术信息部，2019 年

5.2 Building interdisciplinary core and cluster AI curricula for AI competency
5.2 建立跨学科核心和集群 AI 课程以提升 AI 能力

The development of students’ Al competency needs to integrate varied channels for learning and practice, including formal courses within the framework of the national curriculum, extracurricular programmes, and informal learning through engagement with families and local communities. While promoting the development and implementation of a national AI curriculum as the main channel for the implementation of the AI CFS, it is also important to consider whether the study programmes provided by the private sector or non-governmental channels are in compliance with the human-centred vision and ethical principles. Reviewing and steering the impact of informal learning channels including digital platforms is also essential, and can be enacted by mandating providers’ accountability for safety and ethics if their programmes target students, especially children.
学生 AI 能力的发展需要整合多种学习和实践渠道，包括国家课程框架内的正式课程、课外项目以及通过与家庭和地方社区的互动进行的非正式学习。在推动国家 AI 课程的发展和实施作为 AI 能力框架实施的主要渠道时，还需要考虑私营部门或非政府渠道提供的学习项目是否符合以人为本的愿景和伦理原则。审查和引导非正式学习渠道的影响，包括数字平台，也是至关重要的，如果这些项目针对学生，尤其是儿童，可以通过要求提供者对安全和伦理负责来实施。

Al has an interdisciplinary nature and complex intrinsic conceptual and practical connections with mathematics, science, engineering, languages, social studies, art, civic and citizenship education, and history as well as various combinations of these subjects. Al also represents both an iterative step and a technological leap in the continuum of digital technologies. In this context, the AI CFS is built upon multidisciplinary knowledge and skills on data, programming, computing structures and the internet as well as the integrated set of conceptual knowledge and skills based on computing and engineering thinking, and scientific reasoning. In parallel, the fostering
人工智能具有跨学科的特性，与数学、科学、工程、语言、社会研究、艺术、公民与公民教育以及历史等学科之间存在复杂的内在概念和实践联系，以及这些学科的各种组合。人工智能还代表了数字技术连续体中的一个迭代步骤和技术飞跃。在这种背景下，人工智能能力框架建立在数据、编程、计算结构和互联网的多学科知识和技能之上，以及基于计算和工程思维、科学推理的综合概念知识和技能的整合集。同时，促进
of a human-centred mindset and the ethics of Al are anchored in students’ broad social and emotional skills.
以人为本的思维方式和人工智能的伦理植根于学生广泛的社会和情感技能。

It is therefore necessary to align the AI CFS to countries’ general competency frameworks for students, and examine whether the latter need to be adapted or reformed to respond to the new requirements of the AI era. In countries where national digital or ICT competency frameworks for students have been adopted and implemented, an adaptive approach can be considered to integrate Al aspects into them. This requires a redefinition of digital competencies to cover the uncharted values, knowledge and skills required for new iterations or novel domains of AI, and their connections with previous generations of digital technologies.
因此，有必要将 AI 能力框架与各国学生的一般能力框架对齐，并检查后者是否需要进行调整或改革，以应对 AI 时代的新要求。在已经采用和实施国家数字或 ICT 能力框架的国家，可以考虑采取适应性方法将 AI 方面整合到其中。这需要重新定义数字能力，以涵盖新一代或新领域的 AI 所需的未知价值、知识和技能，以及它们与前几代数字技术的联系。

A set of core AI curricula within formal education programmes is usually considered to be the main channel for providing inclusive opportunities for all students, particularly those who may not have access to Al other than at school. This will require the reconfiguration of national curricula to accommodate the time to be committed to Al courses. The cluster curricula related to Al should be adapted or reformed to enhance their connections with Al competencies, without losing their focus on students’ other core competencies. These interdisciplinary core and cluster AI curricula can be integrated into agile structures that are appropriate to national or local educational contexts.
在正式教育项目中，核心人工智能课程的设置通常被认为是为所有学生提供包容性机会的主要渠道，特别是那些可能除了在学校之外没有接触人工智能的学生。这将需要重新配置国家课程，以适应投入到人工智能课程的时间。与人工智能相关的集群课程应进行调整或改革，以增强其与人工智能能力的联系，同时不失去对学生其他核心能力的关注。这些跨学科的核心和集群人工智能课程可以整合到适合国家或地方教育背景的灵活结构中。

The UNESCO report K-12 AI curricula: A mapping of government-endorsed AI curricula (2022b) identified four main strategies for integrating AI curricula into K -12 education systems. These include the implementation of Al as a discrete subject; the integration of

A

into other existing subjects (usually
联合国教科文组织报告《K-12 人工智能课程：政府认可的人工智能课程的映射》（2022b）确定了将人工智能课程整合到 K-12 教育系统中的四种主要策略。这些策略包括将人工智能作为一个独立科目实施；将

A

整合到其他现有科目中（通常是

ICT); cross-curricular approaches in which Al outcomes are integrated into multiple cluster subjects; and AI as an optional, extracurricular or co-curricular activity (e.g. for an extra-curricular club). AI as a discrete subject may be mandated for all students and can be supported by a series of complementary courses in science, technology, mathematics, engineering and design, to meet the diverse abilities, backgrounds and learning needs of students. Under any one or combination of these approaches, the interdisciplinarity has double implications: the core AI curriculum
信息与通信技术（ICT）；跨学科的方法，其中人工智能成果整合到多个学科群中；以及人工智能作为一项可选的课外或共同课程活动（例如，课外俱乐部）。人工智能作为一门独立学科可能会被要求所有学生学习，并可以通过一系列互补课程在科学、技术、数学、工程和设计领域提供支持，以满足学生的多样化能力、背景和学习需求。在任何一种或多种这些方法下，跨学科性具有双重意义：核心人工智能课程
should mobilize students’ multidisciplinary values, knowledge and skills in relevant subjects, especially science, technology, engineering, arts and mathematics (STEAM), to act as the foundation of the AI curriculum as exemplified by the United Arab Emirates’ Computing, Creative Design and Innovation curriculum (UNESCO, 2024); and the cluster AI curriculum should promote the intrinsic integration of key aspects of the AI competencies into the learning outcomes and navigate them at corresponding progression levels.
应该动员学生在相关学科中的多学科价值观、知识和技能，特别是科学、技术、工程、艺术和数学（STEAM），作为人工智能课程的基础，正如阿拉伯联合酋长国的计算、创意设计和创新课程所示（联合国教科文组织，2024）；而集群人工智能课程应促进关键人工智能能力的主要方面与学习成果的内在整合，并在相应的进展水平上进行导航。

Box 3: The United Arab Emirates' interdisciplinary approach to K-12 Al curricula
案例 3：阿拉伯联合酋长国对 K-12 人工智能课程的跨学科方法

‘By covering computer science, engineering, design, sustainability and visual communication, the Ministry of Education’s Computing, Creative Design and Innovation curriculum offers a comprehensive and concise educational framework. It prepares students to thrive in the dynamic and interconnected world by nurturing critical thinking, problemsolving abilities and innovation.’
“通过涵盖计算机科学、工程、设计、可持续性和视觉传播，教育部的计算、创意设计和创新课程提供了一个全面而简明的教育框架。它通过培养批判性思维、解决问题的能力和创新，帮助学生在动态和互联的世界中蓬勃发展。”

The United Arab Emirates takes an interdisciplinary approach to its AI curriculum for K-12 schools by integrating it into a curriculum called Computing, Creative Design and Innovation (CCDI). By including a focus on Al , the CCDI curriculum encourages students to develop their creativity and problem-solving skills; build an awareness of ethics and ethical impacts; learn and rehearse fundamental AI principles and concepts; and cross-fertilize their knowledge across fields. The curriculum was first established in 2016 as a technology-focused subject area, over and above the already-existing computer science curriculum.
阿拉伯联合酋长国对 K-12 学校的人工智能课程采取跨学科的方法，将其整合到一个名为计算、创意设计和创新（CCDI）的课程中。通过关注人工智能，CCDI 课程鼓励学生发展他们的创造力和解决问题的能力；建立对伦理和伦理影响的意识；学习和练习基本的人工智能原则和概念；并在各个领域之间交叉融合他们的知识。该课程于 2016 年首次建立，作为一个以技术为重点的学科领域，超越了已经存在的计算机科学课程。

Since then, and with the recent developments in the field of AI, the CCDI has progressively integrated robotics, programming, 3D-modelling and electronics. In 2020 the curriculum was revised to cover five domains: (1) computer science, with a focus on computer systems, networks and the internet, data and analysis, algorithms and programming, and the impacts of computing; (2) engineering principles and systems, with a focus on electricity and electrons, robotics and systems, and embedded systems; (3) design and innovation, including entrepreneurship and the engineering design process; (4) sustainability, with an emphasis on the sustainable society; and (5) visual communication, concentrating on graphics for design, computer-aided design and design realization.
自那时以来，随着人工智能领域的最新发展，CCDI 逐步整合了机器人技术、编程、3D 建模和电子学。2020 年，课程进行了修订，涵盖五个领域：（1）计算机科学，重点关注计算机系统、网络和互联网、数据与分析、算法与编程以及计算的影响；（2）工程原理与系统，重点关注电力与电子、机器人技术与系统以及嵌入式系统；（3）设计与创新，包括创业和工程设计过程；（4）可持续性，强调可持续社会；（5）视觉传播，集中于设计图形、计算机辅助设计和设计实现。

Source: UNESCO, 2024 来源：联合国教科文组织，2024 年

5.3 Framing future-proofing and locally feasible Al domains as carriers of the curriculum
5.3 将未来保障和本地可行的人工智能领域框架作为课程的载体

After determining the interdisciplinary alignment structure of core and cluster Al curricula, curriculum developers will need to integrate the AI CFS into national or institutional core AI curricula. The framing of the core AI curriculum is built on interlinked key aspects of the Al competencies, subdomains of AI under each aspect, and specific Al systems to act as carriers of the curriculum. Decisions about making the curriculum compulsory or elective is framed by at least three factors: the foundational value of different aspects, the futureproofing potential of AI knowledge and skills, and the feasibility of implementation in local schools. The feasibility of AI domains and systems is determined by the AI readiness of teachers and students, and the local availability and affordability of generic Al systems and specific hardware, software, programming languages and essential applications for the majority of schools.
在确定核心和集群 AI 课程的跨学科对齐结构后，课程开发者需要将 AI 能力框架（AI CFS）整合到国家或机构的核心 AI 课程中。核心 AI 课程的框架建立在 AI 能力的相互关联的关键方面、每个方面下的 AI 子领域以及作为课程载体的特定 AI 系统之上。关于将课程设为必修或选修的决策受到至少三个因素的影响：不同方面的基础价值、AI 知识和技能的未来适应潜力，以及在当地学校实施的可行性。AI 领域和系统的可行性取决于教师和学生的 AI 准备情况，以及大多数学校对通用 AI 系统和特定硬件、软件、编程语言及基本应用的本地可用性和可负担性。

As explained in Chapter 4, the humancentred mindset, Al ethics, and Al techniques and applications are crucial to all students’ lives and work in the AI era, and thus should be included in all AI curricula. Some domains, such as Al system design, may be more appropriate for students who have a strong interest and ability in AI. Assessing the extent of local AI readiness can inform decisions on whether AI system design should be defined as a set of thinking skills that can be merged into other aspects or should be taught as a discrete domain if the necessary databases, computing resources and AI models are available.
正如第 4 章所解释的，以人为本的思维方式、人工智能伦理以及人工智能技术和应用对所有学生在人工智能时代的生活和工作至关重要，因此应纳入所有人工智能课程。一些领域，如人工智能系统设计，可能更适合对人工智能有强烈兴趣和能力的学生。评估当地人工智能准备程度可以为是否将人工智能系统设计定义为可以与其他方面融合的思维技能，或在必要的数据库、计算资源和人工智能模型可用的情况下作为一个独立领域进行教学提供信息。

Within the framework of a selected aspect or domain of Al , the next step is the scoping of the subdomains of AI techniques and technologies to be covered, and the specific Al systems to recommended as carriers of the curriculum or learning practices. This is more pronounced for the aspect of Al techniques and applications. The range of techniques is vast, including logic systems or algorithms crafted from general deductive principles to solve specific problems (e.g. human-coded decision trees, alpha-beta pruning and minimax), as well as models trained on large amounts of data (e.g. deep learning and generative AI). Curriculum developers need to choose subdomains from a large list of exemplar Al techniques and specify their relations, such as classical AI or ‘rule-based AI’, machine learning, deep learning and generative AI models. The range of AI technologies and human-facing products and services is expanding rapidly, and it’s more challenging to choose from Al technologies being developed across sectors, including from the categories of computer vision, natural language processing, automated speech recognition, and automated planning and scheduling (Al planning). Following the selection and scoping of subdomains of AI techniques and technologies, examples of AI systems and tools should be considered, with a view to being agnostic towards commercial brands or products as much as possible. As stated in Principle 5 of Chapter 2, rigorous public validation mechanisms should be applied to prevent the use of AI systems that discriminate against marginalized groups or produce bias(es) related to gender, ability, socio-economic status, language and/or culture. The principle of inclusivity should be upheld when choosing AI tools.
在选定的人工智能（AI）方面或领域框架内，下一步是界定要涵盖的 AI 技术和技术子领域，以及推荐作为课程或学习实践载体的具体 AI 系统。这在 AI 技术和应用方面尤为明显。技术范围广泛，包括从一般推理原则构建的逻辑系统或算法，以解决特定问题（例如，人类编码的决策树、α-β剪枝和极小极大算法），以及在大量数据上训练的模型（例如，深度学习和生成性 AI）。课程开发者需要从大量示例 AI 技术中选择子领域，并指定它们之间的关系，例如经典 AI 或“基于规则的 AI”、机器学习、深度学习和生成性 AI 模型。AI 技术和面向人类的产品与服务的范围正在迅速扩大，从各个领域开发的 AI 技术中选择变得更加具有挑战性，包括计算机视觉、自然语言处理、自动语音识别以及自动规划和调度（AI 规划）等类别。在选择和界定人工智能技术和技术子领域后，应考虑人工智能系统和工具的示例，尽可能对商业品牌或产品保持中立。如第二章第五原则所述，应实施严格的公共验证机制，以防止使用歧视边缘群体或产生与性别、能力、社会经济地位、语言和/或文化相关的偏见的人工智能系统。在选择人工智能工具时，应坚持包容性原则。

Furthermore, which Al domains should be defined as compulsory and which can be elective will be determined by the national context, including the aims and ambitions of relevant policies and readiness as stated above. The depth and breadth of domainspecific Al knowledge and skills should be defined based on the typical readiness and abilities of the target cohorts of students. It is imperative for all students to reach the first two levels of Human-centred mindset, Ethics of AI, and Al techniques and applications, but it is less necessary for them to reach the third level, ‘Create’, especially for AI system design. Therefore, it might be useful to consider an agile or contextualized implementation strategy, in which both compulsory and elective subjects or courses will be designed and offered to students for different AI techniques and key domains of Al knowledge.
此外，哪些人工智能领域应被定义为必修，哪些可以是选修，将由国家背景决定，包括上述相关政策的目标和愿景以及准备情况。领域特定的人工智能知识和技能的深度和广度应根据目标学生群体的典型准备情况和能力来定义。所有学生都必须达到以人为本的思维方式、人工智能伦理以及人工智能技术和应用的前两个层级，但他们达到第三个层级“创造”，尤其是在人工智能系统设计方面，则不那么必要。因此，考虑一种灵活或情境化的实施策略可能是有益的，在这种策略中，将为学生设计和提供不同人工智能技术和关键领域的必修和选修科目或课程。

By anchoring Al competencies in a humancentred mindset and embodied and social knowledge and skills in ethics, the AI CFS aims to prepare students to collaborate with future-oriented Al in a range of contexts. The systemic AI design thinking, knowledge and skills are intended to foster an open knowledge schema that can support students to understand, use and create future generations of Al systems. The AI CFS emphasizes the importance of transferable knowledge and skills under the aspect of AI techniques and applications that can help the majority of students to be ready for the further iterations of Al tools. While efforts have been made to ensure that this curriculum framework responds to emerging technologies, new tools and innovations will emerge after it is published, and the example tools and activities may become obsolete or dated. The curriculum itself will need to include content that
通过将人工智能能力锚定在人本中心的思维方式以及在伦理方面的体现和社会知识与技能，人工智能能力框架旨在准备学生与面向未来的人工智能在各种环境中进行合作。系统的人工智能设计思维、知识和技能旨在培养一个开放的知识框架，以支持学生理解、使用和创造未来几代人工智能系统。人工智能能力框架强调可转移知识和技能的重要性，特别是在人工智能技术和应用的方面，这可以帮助大多数学生为人工智能工具的进一步迭代做好准备。尽管已经努力确保该课程框架能够响应新兴技术，但在发布后会出现新的工具和创新，示例工具和活动可能会变得过时或陈旧。课程本身需要包含的内容是
can be adjusted going forward in order to remain relevant and ‘future-proof’. A modular curriculum design is suggested, in which multiple modules based on AI domains or different Al systems or tools can be developed and recommended to local educational institutions. A modular structure allows the curriculum to be reviewed and updated more dynamically, as it is not necessary to change the entire curriculum to add or remove a specific tool, domain concept or other content. On the other end of the spectrum, future-proofing can involve schools and students co-designing Al curricula. This means encouraging the drafting of school-based AI curricula and teachers’ contextual adaptations of specific domains or tools selected for general competency development. To enact this framework, curriculum developers should consider the dynamism of an AI curriculum and make efforts to future-proof the learning process.
为了保持相关性和“未来适应性”，可以进行调整。建议采用模块化课程设计，其中可以开发和推荐基于人工智能领域或不同人工智能系统或工具的多个模块给地方教育机构。模块化结构允许课程更动态地进行审查和更新，因为不需要更改整个课程就可以添加或删除特定工具、领域概念或其他内容。在另一端，未来适应性可以涉及学校和学生共同设计人工智能课程。这意味着鼓励起草基于学校的人工智能课程以及教师对为一般能力发展选择的特定领域或工具的上下文适应。为了实施这一框架，课程开发者应考虑人工智能课程的动态性，并努力使学习过程具备未来适应性。

5.4 Tailoring age-appropriate spiral curricular sequences
5.4 定制适合年龄的螺旋课程序列

The AI CFS naturally entails a paradigm shift towards competency-based education. A competency-based education aims to transition from models of fixed time and flexible learning (implying completing instruction within a fixed curricular schedule regardless of whether all students have reached the expected mastery level) to more flexible time and fixed learning (implying that flexible learning schedules are allowed so that students of all abilities can reach the expected mastery level). With competencybased education, students are expected to demonstrate performance-based knowledge, skills and values that constitute the competencies, and students who do not
AI 能力框架自然意味着向基于能力的教育转变。基于能力的教育旨在从固定时间和灵活学习的模式（意味着在固定的课程安排内完成教学，无论所有学生是否达到预期的掌握水平）过渡到更灵活的时间和固定学习（意味着允许灵活的学习时间表，以便各类能力的学生都能达到预期的掌握水平）。在基于能力的教育中，学生需要展示构成能力的基于表现的知识、技能和价值观，而未能达到的学生则不
meet these minimal standards are provided with additional support until they do (Patrick and Sturgis, 2017).
满足这些最低标准的学生将获得额外支持，直到他们达到标准为止（Patrick 和 Sturgis，2017）。

This framework does not break down the progression of learning or activities by grade level, focusing instead on the exitlevel outcomes which systems should seek to achieve for all students. Curriculum developers will therefore need to leverage the framework and its components to develop a scaffolded spiral learning pattern across all four aspects, allowing for students to start the learning of AI with the domains and difficulty level that match their abilities and the readiness of their schools. The spiral curricular pattern should provide spaced and iterated engagement with a set of foundational AI knowledge that will encourage both memory retrieval and cyclically upgraded practices to deepen their understanding and associations with problem-solving contexts. This design helps
该框架并未按年级水平划分学习或活动的进展，而是专注于系统应为所有学生实现的最终成果。因此，课程开发者需要利用该框架及其组成部分，开发一个跨越所有四个方面的分层螺旋学习模式，使学生能够以与他们的能力和学校的准备程度相匹配的领域和难度水平开始学习人工智能。螺旋课程模式应提供与一组基础人工智能知识的间隔和迭代接触，这将鼓励记忆提取和周期性升级的实践，以加深他们对问题解决情境的理解和关联。这种设计有助于
ensure a transfer of information from the working memory to the long-term memory to support sustained learning gains, as well as enable students to leverage existing schemas to learn novel AI knowledge, or adapt application skills to solve problems in varied contexts. Conversely, a curriculum developed and delivered as a one-off over a short period of time (e.g. for hackathons or bootcamps) may spark interest but is less likely to lead to sustained AI competency.
确保信息从工作记忆转移到长期记忆，以支持持续的学习成果，并使学生能够利用现有的知识结构来学习新颖的人工智能知识，或调整应用技能以在不同的情境中解决问题。相反，作为一次性课程在短时间内（例如黑客马拉松或训练营）开发和交付的课程可能会激发兴趣，但不太可能导致持续的人工智能能力。

The work of curriculum developers will be to outline the main elements of Al ethics, foundational knowledge and skills as well as system design thinking, and then identify appropriate levels of difficulty, breadth and depth of these elements for different grade levels. This will enable them to create spiral iterations of lessons and project-based tasks that help students to progressively advance and expand their learning and practice.
课程开发者的工作将是概述人工智能伦理、基础知识和技能以及系统设计思维的主要元素，然后为不同年级水平确定这些元素的适当难度、广度和深度。这将使他们能够创建课程和项目任务的螺旋迭代，帮助学生逐步推进和扩展他们的学习和实践。

Box 4: The spiral curricular sequence of 'Day of AI' courses
案例 4：'人工智能日'课程的螺旋课程序列

The AI curriculum developed by MIT’s RAISE

^{2}

initiative, Day of AI, adopted the spiral design approach by clustering curricular content around key topics such as ‘What AI is, and what AI does well and what AI does not do as well’, ‘How AI works’,‘How a machine learns’ and ‘How a machine creates’. Students at different ages were given opportunities to continuously engage in topics such as ‘What is AI?’, while being gradually exposed to novel or upgraded knowledge and skills such as algorithms and Al programming, teachable machines and generative AI. Cross-cutting topics around ethics, including AI biases, human rights, humanAl interaction and the social impact of AI were tailored to students at different age levels.
麻省理工学院的 RAISE

^{2}

倡议“人工智能日”开发的人工智能课程采用了螺旋设计方法，通过围绕关键主题聚集课程内容，例如“什么是人工智能，人工智能擅长什么，人工智能不擅长什么”，“人工智能是如何工作的”，“机器是如何学习的”和“机器是如何创造的”。不同年龄的学生有机会持续参与“什么是人工智能？”等主题，同时逐渐接触到新的或升级的知识和技能，如算法和人工智能编程、可教机器和生成式人工智能。围绕伦理的跨学科主题，包括人工智能偏见、人权、人机交互和人工智能的社会影响，针对不同年龄段的学生进行了量身定制。

For more information: https://dayofai.org
更多信息请访问：https://dayofai.org

5.5 Building enabling learning environments for Al curricula
5.5 为人工智能课程构建支持性学习环境

While the required resources for the implementation of AI curricula may vary depending on the breadth and depth of expected curricular goals and overall digital readiness in local schools, a basic learning environment is required to meet minimum standards for effective study of the essential aspects and domains of Al to the basic mastery level. According to UNESCO’s report K-12 AI curricula: A mapping of government-endorsed Al curricula (2022b), implementation for school students requires the following essential conditions, ranked by importance: teacher training and
虽然实施人工智能课程所需的资源可能因预期课程目标的广度和深度以及当地学校的整体数字准备情况而异，但基本的学习环境是满足有效学习人工智能基本方面和领域的最低标准所必需的。根据联合国教科文组织的报告《K-12 人工智能课程：政府认可的人工智能课程的映射》（2022b），学校学生的实施需要以下基本条件，按重要性排序：教师培训和
support, teaching resources on Al , needs analysis and school-based research, updated digital infrastructure in schools, and the provision of AI resources including through procurement of hardware and software as well as engagement with the private or third sector to share AI devices and systems. If these conditions are not provided, the curriculum is unlikely to be implemented as intended or achieve its anticipated learning and competency objectives. The report highlighted typical learning environments that had been set up by the 11 countries that were implementing their own governmental K-12 AI curricula as of 2022, detailed below.
支持、关于人工智能的教学资源、需求分析和基于学校的研究、学校中更新的数字基础设施，以及通过采购硬件和软件提供人工智能资源，以及与私营或第三部门的合作以共享人工智能设备和系统。如果这些条件没有得到满足，课程不太可能按预期实施或实现其预期的学习和能力目标。报告强调了截至 2022 年，11 个国家实施自己政府的 K-12 人工智能课程所建立的典型学习环境，详见下文。

Box 5: Typical enabling learning environment set up by governments' AI curricula
案例 5：政府人工智能课程设立的典型支持学习环境

Hardware and robotics: The hardware needed for AI curricula may include computers, tablets, laptops and internet access. Not all AI curricula include content on robots or robotics. When the learning on robots is required, curricula can leverage free online virtual applications or locally affordable kits. Devices like Raspberry Pi are used by some curricula that require students to create programs and test them using low-cost devices.
硬件和机器人：人工智能课程所需的硬件可能包括计算机、平板电脑、笔记本电脑和互联网接入。并非所有人工智能课程都包括机器人或机器人技术的内容。当学习机器人是必需时，课程可以利用免费的在线虚拟应用程序或当地可负担的工具包。一些课程使用树莓派等设备，要求学生创建程序并使用低成本设备进行测试。
Software: The Ubuntu $^{3}$ open-source operating systems were used by some curricula as less expensive alternatives to other operating systems.
软件：一些课程使用 Ubuntu $^{3}$ 开源操作系统作为其他操作系统的更便宜替代品。
Programming languages: National AI curricula have often leveraged free programming languages such as HTML, Javascript, Python, Micropython, NumPy, R and Scratch.
编程语言：国家人工智能课程通常利用免费的编程语言，如 HTML、Javascript、Python、Micropython、NumPy、R 和 Scratch。
Tools for learning AI techniques: A number of tools have been developed or made accessible free of charge to facilitate understanding and allow the exploration of complex concepts and Al techniques, with the following mentioned in the 11 governmental Al curricula: MachineLearningForKids (an educational tool for teaching kids about machine learning by letting them train a computer to recognize text, pictures, numbers, sounds or other inputs), $^{4}$ Teachable Machine (a platform developed by Google to train a computer to recognize the user’s own images, sounds and poses), $^{5}$ TensorFlow (an end-to-end platform for machine learning), $^{6}$ and Keras (deep learning for humans). $^{7}$
学习 AI 技术的工具：为了促进理解并允许探索复杂概念和 AI 技术，已经开发或免费提供了一些工具，在 11 个政府 AI 课程中提到的有：MachineLearningForKids（一个教育工具，通过让孩子们训练计算机识别文本、图片、数字、声音或其他输入来教授机器学习）， $^{4}$ Teachable Machine（由谷歌开发的平台，用于训练计算机识别用户自己的图像、声音和姿势）， $^{5}$ TensorFlow（一个端到端的机器学习平台）， $^{6}$ 和 Keras（为人类设计的深度学习）。 $^{7}$

Source: UNESCO, 2022b, p. 47
来源：联合国教科文组织，2022b，第 47 页

To provide enabling learning environments for Al competency development and the implementation of an Al curriculum in particular, governments should commit to universal access to internet connectivity for all schools and students, including through agile ‘online + offline’ solutions, to engage with online or mobile AI systems, customizable applications, basic and extendable learning resources, and peer learners or co-creators. The prerequisite digital infrastructure also includes a modest number of well-functioning digital devices with basic connectivity as well as a minimum amount of software or applications for students to learn operational skills, practise programming, and train virtual machine or Al models.
为了提供支持人工智能能力发展的学习环境，特别是实施人工智能课程，政府应承诺为所有学校和学生提供普遍的互联网连接，包括通过灵活的“在线 + 离线”解决方案，以便与在线或移动人工智能系统、可定制的应用程序、基本和可扩展的学习资源以及同伴学习者或共同创作者进行互动。先决的数字基础设施还包括适量的功能良好的数字设备，具备基本的连接性，以及最低限度的软件或应用程序，以便学生学习操作技能、练习编程和训练虚拟机或人工智能模型。

Where these essential conditions are not yet realized, but the government is determined to initiate an Al curriculum at the earliest possible stage, alternative options should be considered in the provision of enabling learning environments. With regard to the AI CFS, most objectives under the first two aspects, Human-centred mindset and Ethics of AI, can be engaged with, at least partially, through online and offline solutions, which are also defined as unplugged solutions. For the aspect of Al techniques and applications, some well-designed unplugged activities have been made available by academic and non-profit organizations to demonstrate conceptual knowledge on Al tools and the understanding of Al techniques (e.g. the unplugged Al activities designed by Everyday

{Al}^{8}

AI Unplugged,

^{9}

and the International Society for Technology in Education).

^{10}

Even in fully connected learning settings, unplugged solutions have value by providing students with opportunities to retreat from algorithmcontrolled information cocoons and
在这些基本条件尚未实现的情况下，但政府决心尽早启动人工智能课程，应考虑在提供支持性学习环境时的替代选项。关于人工智能能力框架，前两个方面的多数目标，即以人为本的思维方式和人工智能伦理，至少可以通过在线和离线解决方案部分实现，这些也被定义为无插电解决方案。对于人工智能技术和应用方面，一些学术和非营利组织提供了精心设计的无插电活动，以展示对人工智能工具的概念知识和对人工智能技术的理解（例如，Everyday

{Al}^{8}

AI Unplugged 和国际教育技术学会设计的无插电人工智能活动）。

^{10}

即使在完全连接的学习环境中，无插电解决方案也具有价值，因为它为学生提供了从算法控制的信息茧中退回的机会。
interactions with digital platforms to practise independent, autonomous contemplation, which is critical for the progressive construction and deepening of conceptual knowledge on AI.
与数字平台的互动，以练习独立、自主的思考，这对 AI 概念知识的逐步构建和深化至关重要。

5.6 Promoting the professionalization of AI teachers and streamlining their support
5.6 促进 AI 教师的专业化和简化他们的支持。

As stated above, the most important preconditions for the implementation of Al curricula for school students are teacher training and support as well as the provision of teaching resources on AI. The achievement of the goals outlined by the AI CFS will require teachers, particularly those in ICT or Al , to continuously develop and update their subject knowledge and pedagogical capacities in designing and facilitating ageappropriate learning activities on Al. National and institutional strategists need to plan and implement an integrated approach to the reform of pre-service programmes to prepare qualified AI teachers, design and provide competency-based training and long-term support for in-service ICT or Al teachers, and enhance upskilling for teachers in other core subjects to foster interdisciplinary Al competency. All these training and support programmes aim to strengthen the competencies of teachers who are tasked with teaching AI or implementing the national AI curriculum, implying a trend toward the professionalization of AI teachers. This professionalization includes setting up frameworks specifically for Al teachers, or alternative and more agile mechanisms, that define and develop a set of professional competencies to fully realize the goals of the AI curriculum for students. As ICT and AI are often categorized as marginal subjects
如上所述，实施学校学生 AI 课程的最重要前提是教师培训和支持，以及提供 AI 教学资源。实现 AI CFS 所概述的目标将需要教师，特别是信息与通信技术（ICT）或人工智能（AI）领域的教师，持续发展和更新他们的学科知识和教学能力，以设计和促进适合年龄的 AI 学习活动。国家和机构的战略家需要规划和实施一种综合方法，以改革师范教育项目，培养合格的 AI 教师，设计和提供基于能力的培训以及对在职 ICT 或 AI 教师的长期支持，并增强其他核心学科教师的技能提升，以促进跨学科的 AI 能力。所有这些培训和支持项目旨在增强负责教授 AI 或实施国家 AI 课程的教师的能力，这意味着 AI 教师专业化的趋势。这种专业化包括为人工智能教师建立专门的框架，或替代的更灵活的机制，以定义和发展一套专业能力，以充分实现学生人工智能课程的目标。由于信息与通信技术和人工智能通常被归类为边缘学科
in school curricula, the professional status of ICT and AI teachers has not been fully recognized. The professionalization of AI teachers also means that AI should be classified as one of the core subjects and
在学校课程中，信息与通信技术和人工智能教师的专业地位尚未得到充分认可。人工智能教师的专业化也意味着人工智能应被归类为核心学科之一，并且

Al teachers should be entitled to the same professional status as teachers in other core subjects, with their teaching hours and performance being equally recognized in personnel management systems.
人工智能教师应享有与其他核心学科教师相同的专业地位，他们的教学时间和表现应在人员管理系统中得到同等认可。

Box 6: An Al competency framework for Al subject teachers in China
案例 6：针对中国人工智能学科教师的人工智能能力框架

In China, an AI competency framework for AI subject teachers was developed by the National Institute for Education, East China Normal University and Tencent. Even though it’s not a government-driven national Al competency framework, it is a clear indication of the professionalization of AI teachers. It defines a comprehensive set of competencies for Al teachers, which encompasses six dimensions: understanding and awareness, basic knowledge, basic skills, problem-solving capability, teaching practices, and ethics and security. Accordingly, teachers must grasp Al’s foundational conceptual logic and societal impact, appreciating the distinctions between human and machine intelligence, and the significance of human-machine collaboration, with a view to Al’s educational roles. Unlike the UNESCO AI competency framework for teachers, the framework is aimed at AI teachers; the aspects of human-centred mindset and professional development are not covered, and no progression levels are provided.
在中国，由国家教育研究院、华东师范大学和腾讯共同开发了针对人工智能学科教师的人工智能能力框架。尽管这不是由政府推动的国家级人工智能能力框架，但它清晰地表明了人工智能教师的专业化。该框架定义了一套全面的人工智能教师能力，包括六个维度：理解与意识、基础知识、基本技能、解决问题的能力、教学实践以及伦理与安全。因此，教师必须掌握人工智能的基础概念逻辑和社会影响，理解人类与机器智能之间的区别，以及人机协作的重要性，以便于人工智能在教育中的角色。与联合国教科文组织的教师人工智能能力框架不同，该框架针对的是人工智能教师；人本思维和专业发展的方面未被涵盖，也没有提供进阶水平。
For more information: http://www.jyb.cn/rmtzcg/xwy/wzxw/202203/t20220325 686401.html
更多信息请访问：http://www.jyb.cn/rmtzcg/xwy/wzxw/202203/t20220325 686401.html

In countries where public teacher education institutions do not have sufficient capacities to upskill teachers to keep pace with the rapid changes of Al technologies, publicprivate partnerships for the development and provision of AI curricula are often mobilized to leverage the human and material resources of the private Al industry or NGOs to partly or fully substitute for a public AI curriculum and ICT or AI teachers. As these resourceful Al companies and NGOs have a strong interest in reinforcing their presence and dominance in the teaching of Al based on their own brands, this approach risks the de-professionalization of public Al teachers. It is recommended that public-private partnerships are mobilized with a clear purpose of contributing to the preparation of upskilling public AI teachers and supporting their continuous professional
在公共教师教育机构没有足够能力提升教师以跟上人工智能技术快速变化的国家，通常会动员公私合营伙伴关系来开发和提供人工智能课程，以利用私营人工智能行业或非政府组织的人力和物质资源，部分或完全替代公共人工智能课程和信息通信技术或人工智能教师。由于这些资源丰富的人工智能公司和非政府组织对加强其在基于自身品牌的人工智能教学中的存在和主导地位有强烈兴趣，这种方法有可能导致公共人工智能教师的去专业化。建议公私合营伙伴关系的动员应明确旨在为提升公共人工智能教师的能力做出贡献，并支持他们的持续专业发展。
development. Moreover, the comprehensive competency frameworks for Al teachers to meet the needs of implementing the AI CFS and national AI curriculum should be used to define a rigorous set of criteria to validate whether the AI courses and trainers developed by the AI industry are trustworthy, anti-bias, relevant for AI competency development and sufficiently brand-agnostic. Such frameworks should also help verify how the Al courses can be properly integrated into school curriculum systems to supplement rather than replace the public curriculum. The accountability of public schools for continuously improving teachers’ capacities in implementing the AI curriculum should be prioritized instead of being weakened.
发展。此外，为了满足实施 AI CFS 和国家 AI 课程的需求，针对 AI 教师的综合能力框架应被用来定义一套严格的标准，以验证 AI 行业开发的 AI 课程和培训师是否值得信赖、反偏见、与 AI 能力发展相关且足够品牌中立。这些框架还应帮助验证 AI 课程如何能够适当地融入学校课程体系，以补充而不是替代公共课程。公共学校在持续提升教师实施 AI 课程能力方面的责任应被优先考虑，而不是被削弱。

To promote the professionalization of public Al teachers, it is also important to adopt the requirement of implementing the AI CFS as a benchmark to streamline pre-service and in-service training and continuing support for teachers’ professional development, to ensure they are aligned with a set of clearly defined competencies and are complementary in scaffolding teachers’ progressive improvement throughout their career. Special attention should be given to the engagement, review and adaptation of continuing education initiatives for teachers and school-based support for their professional development according to the value orientation, knowledge and practical skills required to teach the national Al curriculum.
为了促进公共人工智能教师的专业化，采用实施人工智能能力框架（AI CFS）作为基准，以简化教师的职前和在职培训以及持续支持教师专业发展的要求也非常重要，以确保他们与一套明确定义的能力相一致，并在教师职业生涯的逐步提升中相辅相成。应特别关注教师继续教育计划的参与、审查和调整，以及根据教授国家人工智能课程所需的价值取向、知识和实践技能，为教师的专业发展提供基于学校的支持。

5.7 Guiding the cohort-based design and organization of pedagogical activities
5.7 指导基于 cohort 的教学活动设计和组织

Al competency development is a three-helix bundle spanning the social and emotional learning of values and ethical principles, self-directed and collaborative construction of conceptual knowledge on Al , and practical skills to apply and co-create Al tools. A combination of innovative pedagogical methodologies is required in order to help students progress through the three helixes of competencies altogether, bridging between what they know and what they can do as well as transferring their prior knowledge and skills to novel concepts and new problem-solving contexts in the Al-rich workplaces and social spaces of the future.
人工智能能力的发展是一个三重螺旋的组合，涵盖了价值观和伦理原则的社会和情感学习、自主和协作构建人工智能概念知识，以及应用和共同创造人工智能工具的实践技能。为了帮助学生在这三条能力螺旋中共同进步，需要结合创新的教学方法，架起他们所知道的与他们能够做的之间的桥梁，同时将他们的先前知识和技能转移到人工智能丰富的工作场所和未来社会空间中的新概念和新问题解决情境中。

The pedagogical innovations that are tailored to the particularities of Al domains and varied abilities of students can be unlocked through the design and
针对人工智能领域的特性和学生的不同能力量身定制的教学创新可以通过设计和实现来解锁。
organization of activities based on a cohort of students who are enrolled in a certain Al course or share an interest in the same domain of AI. In this cohort-based approach to the design and organization of learning scenarios or projects, a certain cohort of students may be grouped together from different classes and grade levels. This approach does not represent any particular learning theory, and typically involves a wide range of pedagogical methods and practice-orientated learning scenarios including interactive activities, collaborative projects and peer support. Students build a community of practice and their learning often follows a curricular schedule where they share accountability and motivate and coach each other, and work with their teachers to get feedback. In this way, they deepen their understanding and tackle challenging questions together; collaborate in hands-on projects to apply knowledge and skills in practical ways; and exchange views and engage in debates on the societal impact and ethical issues of Al to enhance social construction.
基于一组注册了某个 AI 课程或对同一领域的 AI 感兴趣的学生的活动组织。在这种基于 cohort 的学习场景或项目设计和组织方法中，来自不同班级和年级的学生可以被分组在一起。这种方法并不代表任何特定的学习理论，通常涉及广泛的教学方法和以实践为导向的学习场景，包括互动活动、协作项目和同伴支持。学生们建立了一个实践社区，他们的学习通常遵循课程安排，在这个过程中，他们共同承担责任，互相激励和辅导，并与教师合作以获得反馈。通过这种方式，他们加深了理解，共同解决具有挑战性的问题；在实践项目中合作，以实际方式应用知识和技能；并就 AI 的社会影响和伦理问题交换观点并参与辩论，以增强社会建构。

When choosing or designing pedagogical methodologies for the understanding, application and creation of different aspects of the AI CFS, it is also important to consider the domain-specific needs for pedagogical practices:
在选择或设计用于理解、应用和创造人工智能能力框架不同方面的教学方法时，考虑特定领域的教学实践需求也很重要：

The nurturing of human-centred values and mindset, by nature, is built upon social and emotional learning processes, and requires conflict-based opinion-taking, social construction and social interactions.
以人为本的价值观和心态的培养本质上建立在社会和情感学习过程中，并需要基于冲突的观点采纳、社会建构和社会互动。
The learning of ethics is a process of understanding abstract principles and regulatory rules through practical
学习伦理是一种通过实践理解抽象原则和监管规则的过程
case studies, scenario-based critical evaluations, contextual application and collaborative rule-making.
案例研究、基于情境的批判性评估、情境应用和协作规则制定。
Al techniques and applications represents a domain that seamlessly blends the practice-oriented construction of conceptual knowledge on AI with authentic taskbased application, and requires real Al tools as the basis for constructing knowledge schema on Al techniques and technology, problem-based learning and practices of transferable application and scenariobased inquiry, and a deepened understanding of the values and ethics underlying Al tools and their uses.
人工智能技术和应用代表了一个领域，完美地融合了基于实践的人工智能概念知识构建与真实任务导向的应用，并需要真实的人工智能工具作为构建人工智能技术和技术知识框架的基础，基于问题的学习和可转移应用的实践，以及对人工智能工具及其使用背后价值和伦理的深入理解。
Al system design simulates real-world engineering projects, involving the life cycle of the creating, realizing and iterating Al systems to practise engineering thinking processes and foster integrated problem-solving skills. It requires teachers to design and organize project-based learning to allow students to identify and delineate the problems that can and should be solved by Al; assess needs for data and plan methods of data collection; configure the architecture of AI models; and train AI models or create prototypes, tests and iterations of them.
人工智能系统设计模拟现实世界的工程项目，涉及创建、实现和迭代人工智能系统的生命周期，以实践工程思维过程并培养综合问题解决能力。这要求教师设计和组织基于项目的学习，使学生能够识别和界定可以和应该由人工智能解决的问题；评估数据需求并规划数据收集方法；配置人工智能模型的架构；并训练人工智能模型或创建其原型、测试和迭代。

As AI competency is a three-helix bundle, specific pedagogical practices can potentially cover multiple aspects of Al
由于人工智能能力是一个三重螺旋的组合，特定的教学实践可能涵盖人工智能的多个方面。
competency within one lesson or unit. This requires instructional planners or teachers to infuse and navigate various pedagogical methods so students can engage with multiple aspects of the learning and practice of AI. The real-world research and development of Al technology and applications often leverages intensive and continuous conceptualization of AI methods and iterative programming, configuration and optimization. This prerequisite for developing practical Al competencies has been validated by the effectiveness of the pedagogical methodologies practised at hackathons and bootcamps using AI applications. To improve the efficacy of pedagogy in schools, opportunities should be scheduled for students to be engaged in more intensive units of lessons or activities that align with the formal Al curriculum.
在一节课或一个单元内的能力。这要求教学规划者或教师融入并导航各种教学方法，以便学生能够参与到人工智能学习和实践的多个方面。人工智能技术和应用的现实世界研究与开发通常依赖于对人工智能方法的深入和持续的概念化，以及迭代编程、配置和优化。开发实用人工智能能力的这一前提已通过在使用人工智能应用的黑客马拉松和训练营中实践的教学方法的有效性得到了验证。为了提高学校教学的有效性，应安排机会让学生参与与正式人工智能课程对齐的更密集的课程单元或活动。

The national or institutional AI curriculum should frame recommendations or guidance on pedagogical methodologies around the principles of engaging shared accountability and peer learning in the target cohort of students and the specificity of the AI domain and expected learning outcomes. When updated or novel pedagogical methodologies are introduced in AI curricula, sufficient training, practical guidance and instantly responsive services (e.g. online chatbots) should be made available for teachers. Locally relevant incentive mechanisms should be planned and implemented to review, validate and recognize practices in pilot testing and scaling up pedagogical innovations.
国家或机构的人工智能课程应围绕共享责任和同伴学习的原则，为目标学生群体及人工智能领域的具体性和预期学习成果提供教学方法的建议或指导。当在人工智能课程中引入更新或新颖的教学方法时，应为教师提供足够的培训、实用指导和即时响应服务（例如在线聊天机器人）。应计划和实施与当地相关的激励机制，以审查、验证和认可在试点测试和推广教学创新中的实践。

Box 7: Pedagogical methodologies in the MIT curriculum on the ethics of AI for middle school students
案例 7：麻省理工学院针对中学生的人工智能伦理课程中的教学方法

An ethics of artificial intelligence curriculum for middle school students was created by Blakeley H. Payne with support from the MIT Media Lab Personal Robots Group, directed by Cynthia Breazeal (Payne, 2019). The curriculum is designed to be implemented online and/or offline with students aged 12 to 14 who are early in their Al learning journeys. The curriculum focuses on improving students’ understanding of AI and the relationships between humans, technology and society. Parts of this curriculum have also been integrated into the MIT DAILy Curriculum,

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and into How to Train Your Robot: A Middle School AI and Ethics Curriculum. Research into the latter demonstrated the potential for such a curriculum to be delivered even by teachers with a limited computer science background (Williams et al., 2021).
由 Blakeley H. Payne 创建的人工智能伦理课程，旨在为中学生提供支持，得到了麻省理工学院媒体实验室个人机器人组的支持，该组由 Cynthia Breazeal 领导（Payne, 2019）。该课程旨在在线和/或离线实施，面向 12 至 14 岁、刚开始学习人工智能的学生。课程重点在于提高学生对人工智能的理解，以及人类、技术和社会之间的关系。该课程的部分内容也已整合到麻省理工学院 DAILy 课程中，以及《如何训练你的机器人：中学人工智能与伦理课程》中。对后者的研究表明，即使是计算机科学背景有限的教师也有能力教授这样的课程（Williams et al., 2021）。

This curriculum exemplifies a student-centred and inquiry-based approach, with learning outcomes that are aligned to enable a cycle of: initial orientation or information-gathering that supports students to build knowledge schemas on a new topic; conceptualization, where students begin to form a hypothesis around the purpose(s) of AI; investigation, in which students delve deeper into the different perspectives, benefits, values and risks of AI for different populations, and design potential solutions for the problems that emerge; and finally, the development of a potential solution prototype using a project-based approach. Throughout, discussion and reflection are leveraged to deepen understanding and thinking about the problem.
该课程体现了以学生为中心和基于探究的方法，学习成果旨在支持一个循环：初步的定向或信息收集，帮助学生在新主题上建立知识框架；概念化，学生开始围绕人工智能的目的形成假设；调查，学生深入探讨人工智能对不同人群的不同视角、益处、价值和风险，并设计可能的解决方案以应对出现的问题；最后，使用项目驱动的方法开发潜在解决方案的原型。在整个过程中，通过讨论和反思来加深对问题的理解和思考。

The curriculum includes six core goals, which are pursued through different online or offline activities depending on the context. The chart below outlines the goals as well as example activities for teachers or other facilitators that can help to achieve them
该课程包括六个核心目标，这些目标通过不同的在线或离线活动根据上下文进行追求。下表概述了这些目标以及可以帮助实现这些目标的教师或其他促进者的示例活动。

Learning outcomes 学习成果

Example activities and pedagogical advantages
示例活动和教学优势

理解人工智能系统的基本机制。这个学习成果包括子成果，例如识别日常生活中的人工智能应用；理解算法作为输入、输入变化和输出的过程；以及理解人工智能作为一种特定类型的算法，具有数据集、学习和预测功能。

Understand the basic mechanics of artificial

intelligence systems. This learning outcome

includes sub-outcomes such as recognizing

Al uses in everyday life; understanding

algorithms as a process of input, changes to

input and output; and understanding AI as

a specific type of algorithm with a dataset,

learning and prediction.

Play 'AI Bingo' with Al systems. Using a worksheet,
与人工智能系统一起玩“人工智能宾果”。使用工作表，

each student tries to find another classmate who
每个学生尝试找到另一位同学，

has used or experienced various AI applications
使用或体验过各种人工智能应用。

(for example, a tool that suggests emojis to
（例如，一个建议表情符号的工具）

replace words or an app that maps a route to a
替换单词或一个绘制路线的应用程序

destination). Together the pair must determine
目的地）。这对搭档必须确定

the dataset used and prediction made by each
每个使用的数据集和所做的预测

different type of Al system until one student has
不同类型的人工智能系统，直到一个学生完成五个连续的任务。

completed five in a row. This represents an example
这代表了一个例子。

of gamification, which can increase student interest
游戏化可以增加学生的兴趣。

and motivation, and is designed to support recall
并且旨在支持回忆。

in order to begin building knowledge schemas
为了开始构建知识框架

around core AI concepts.
围绕核心人工智能概念。

Learning outcomes 学习成果

Example activities and pedagogical advantages
示例活动和教学优势

编写一个算法来制作“最佳”的花生酱和果酱三明治（或面条、米饭、玉米饼菜肴，或其他孩子们熟悉的当地食品）。这可以单独进行，也可以作为一个小组进行。活动的核心要求学生通过获取关于算法是什么以及它是如何结构化的知识来练习回忆，并将其应用于一个在熟悉背景下框定的特定强制性问题。

Write an algorithm to make the 'best' peanut butter

and jelly sandwich (or noodle, rice or tamale dish,

or another locally-relevant food the children are

familiar with). This can be undertaken individually

or as a group. The core of the activity requires

students to practise recall through accessing

knowledge on what an algorithm is and how it is

structured, and apply this to a specific mandated

problem framed in a familiar context.

在这个回忆和识别活动中，学生们参与回忆、反思和构建知识框架。在这个课程中，这个活动为更高级的反思和创造性合作奠定了基础框架。

Identify the Al systems on the YouTube platform

as a group. In this recall-and-identification activity,

students engage in recalling, reflection on and

building of knowledge schemas. In this curriculum,

this activity forms foundational schemas for more

advanced reflective and creative collaborative

problem-solving in the later stages in the
后期的解决问题能力

curriculum. 课程。

Learning outcomes 学习成果

Example activities and pedagogical advantages
示例活动和教学优势

以 YouTube 为例，学生围绕 YouTube 推荐算法构建伦理矩阵。这个活动是一个以学生为中心的批判性思维练习，促使学生将课堂学习（包括程序性和内容性）与他们的生活现实联系起来。

Using YouTube as an example, students

construct an ethical matrix around the YouTube

Recommender Algorithm. This activity exemplifies

a student-centred critical thinking exercise which

pushes students to connect classroom learning

(both procedural and content) to their lived

realities.

认识到在特定的社会技术系统中有许多利益相关者，并且该系统可能对这些利益相关者产生不同的影响。学生识别人工智能利益相关者及其价值观，以及系统应具备的目标，以满足这些利益相关者的需求。

Recognize that there are many stakeholders

in a given socio-technical system and that

the system can affect these stakeholders

differently. Students identify Al stakeholders

and their values, and the goals that

systems should have in order to meet those

stakeholders' needs.

学生们反思与一系列技术相关的利益相关者，例如生成对抗网络（GAN）、情感识别和语音转文本软件。在这个练习中，学生们展示了将从食品项目的伦理利益相关者矩阵示例中获得的程序性知识转化的能力。

Students reflect on the stakeholders for a range

of technologies such as generative adversarial

network (GANs), emotional recognition and

speech-to-text software. In this exercise, students

demonstrate the ability to transpose the

procedural knowledge gained from the ethical

stakeholder matrix example for the food item and

YouTube to other technologies, an important step
YouTube 到其他技术，这是一个重要的步骤

in developing translational skills.
在发展转化技能方面。

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5.8 Constructing competencybased assessments on the progression of key Al aspects
5.8 基于能力的评估构建关键人工智能方面的进展

The assessment of students’ Al competencies naturally requires the use of competencybased assessments that need to be adapted to the specificity and integration of multiple aspects of AI. Methodologies and instruments designed for such assessments are essential to benchmark students’ starting point, measure their mastery levels of the key aspects of Al , and provide references for evaluating the effectiveness of teaching practices and overall implementation of the AI curriculum. However, few attempts have been made to develop these sorts of instruments for assessing comprehensive Al competencies cross-cutting multiple progression levels. Therefore, the implementation of the AI CFS or the local AI curriculum needs to include the construction of a competency-based assessment system encompassing purpose and objectives, authentic tasks and methodologies, benchmarking standards or indicators, and domain-appropriate criteria associated with a corresponding grading scale.
学生 AI 能力的评估自然需要使用基于能力的评估，这些评估需要适应 AI 多个方面的特性和整合。为此类评估设计的方法和工具对于基准学生的起点、衡量他们在 AI 关键方面的掌握水平，以及为评估教学实践的有效性和整体 AI 课程实施提供参考是至关重要的。然而，迄今为止，针对跨越多个进展水平的综合 AI 能力评估开发此类工具的尝试很少。因此，AI 能力框架或地方 AI 课程的实施需要包括构建一个基于能力的评估系统，涵盖目的和目标、真实任务和方法、基准标准或指标，以及与相应评分标准相关的领域适当标准。

Frame criterion-referenced assessments to measure the mastery of AI competencies
构建标准参照评估以衡量 AI 能力的掌握情况

The primary purpose of competencybased assessments is to measure students’ mastery level against predefined standards or benchmarking frameworks, implying the use of criterion-referenced assessments. As stated above, competency-based education aims to support all students to achieve the minimum mastery level of competencies, meaning the fixed learning outcomes with more flexible time schedules. Under these models, students
能力基础评估的主要目的是衡量学生在预定义标准或基准框架下的掌握水平，这意味着使用标准参照评估。如上所述，能力基础教育旨在支持所有学生达到能力的最低掌握水平，这意味着固定的学习成果与更灵活的时间安排。在这些模型下，学生
who do not meet the minimum standards within a certain timeframe should be given additional support until they can reach them. To support this aim, a set of reference criteria should be defined to diagnose students’ mastery levels compared with the predefined standards, and to recommend further learning experiences. In the context of the cohort-based design and organization of pedagogical activities, the criterionreferenced ipsative assessment of a single student or a target cohort of students should be implemented to diagnose the gaps between their mastery level and the minimum standard as well as their progressive performance over time. While the ipsative assessment of learning may help tailor students’ personalized experiences, the emphasis on the criterion reference can prevent the loss of targeted achievement of AI competencies. This can be extended to students’ self-assessment and setting of personal curricular goals.
在一定时间内未达到最低标准的学生应获得额外支持，直到他们能够达到这些标准。为了支持这一目标，应定义一套参考标准，以诊断学生的掌握水平与预定义标准的比较，并推荐进一步的学习体验。在基于群体的教学活动设计和组织的背景下，应实施对单个学生或目标学生群体的标准参照自我评估，以诊断他们的掌握水平与最低标准之间的差距，以及他们随时间的逐步表现。虽然学习的自我评估可能有助于量身定制学生的个性化体验，但对标准参照的强调可以防止 AI 能力的目标成就的丧失。这可以扩展到学生的自我评估和个人课程目标的设定。

The AI CFS interprets AI competencies into measurable learning outcomes and outlines expected exit-level behavioural performance for each competency block. These can be used as a basis for framing predefined benchmarking standards, against which a repository of criterion-referenced assessment items can be created to measure the mastery level of the cohort of students, including, more specifically, the aspects, domains or specific topics they have mastered and any areas in need of improvement.
AI 能力框架将 AI 能力解释为可衡量的学习成果，并概述了每个能力模块的预期毕业水平行为表现。这些可以作为制定预定义基准标准的基础，基于这些标准可以创建一套标准参考评估项目，以衡量学生群体的掌握水平，包括更具体地说，他们掌握的方面、领域或特定主题，以及需要改进的任何领域。

Norm-referenced assessments, which compare individual students to the rest of the cohort on the same course, are not the main focus of the competency-based assessments in the AI curriculum. However, national or institutional agencies in charge of AI curricula may consider building a
规范参考评估将个别学生与同一课程的其他学生进行比较，并不是 AI 课程中以能力为基础的评估的主要焦点。然而，负责 AI 课程的国家或机构机构可能会考虑建立一个
set of dynamically adjusted norms of students’ competency development in key aspects or domains of Al through the longterm tracking of students’ performance. The norm-referenced assessments can also provide a comprehensive view of a student’s abilities compared to their peers, and a benchmarking of local students’ competencies compared to same-age students in other countries. The mean of the norm should be measured against the predefined standards of AI competencies to monitor whether the learning outcomes of the majority of students exceed, meet or are below the minimum standards. Finally, the performance of different groups of students compared to the norms should be disaggregated and analysed by age, gender or demographic background, to help provide evidence for policies or strategies that enable remedial or supplementary support for students who are disadvantaged in learning AI.
通过对学生表现的长期跟踪，动态调整学生在人工智能关键方面或领域的能力发展的规范。这些规范参考评估还可以提供学生能力与同龄人相比的全面视图，以及本地学生的能力与其他国家同龄学生的基准比较。规范的平均值应与预定义的人工智能能力标准进行比较，以监测大多数学生的学习成果是否超过、达到或低于最低标准。最后，不同学生群体的表现与规范的比较应按年龄、性别或人口背景进行分解和分析，以帮助提供证据支持政策或策略，从而为在学习人工智能方面处于劣势的学生提供补救或补充支持。

Adapt performance scenarios to assess overt performance and latent competencies
调整表现场景以评估显性表现和潜在能力

Al technology is designed to address real-world problems, and its practiceorientated nature requires the use of real-world scenarios and authentic tasks to measure students’ performance in applying their mindset, ethical principles, skills and knowledge, and to psychometrically validate students’ development of multiple aspects of AI. The competency-based assessment should fully leverage tasks showing students’ measurable or overt behavioural performance (what they can do), which is often termed ‘performancebased assessment’. However, to fully meet the needs of assessing both observable behaviours and latent competencies
人工智能技术旨在解决现实世界的问题，其实践导向的特性要求使用真实场景和真实任务来衡量学生在应用思维方式、伦理原则、技能和知识方面的表现，并对学生在人工智能多个方面的发展进行心理测量验证。基于能力的评估应充分利用展示学生可测量或明显行为表现（他们能做什么）的任务，这通常被称为“基于表现的评估”。然而，为了充分满足评估可观察行为和潜在能力的需求
involving the human-centrality of mindset and ethics, transferability of conceptual knowledge, adaptivity of practical skills, and creativity in AI system design, the objectives and methods for assessing performance should be adapted as follows:
涉及以人为中心的思维方式和伦理、概念知识的可转移性、实践技能的适应性以及人工智能系统设计中的创造力，评估表现的目标和方法应进行如下调整：

Assess both observable performance and latent competencies: Move from pure assessment of observable behaviours (what students already do) to the psychometric testing or validation of students’ latent knowledge schema on AI techniques and application abilities (what they can potentially do), human-centred critical thinking and ethical evaluation and selection of AI tools to serve specific purposes (how they apply ethics to their use of AI).
评估可观察的表现和潜在能力：从对可观察行为（学生已经做的事情）的纯粹评估转向对学生在人工智能技术和应用能力（他们潜在能做的事情）、以人为本的批判性思维以及对特定目的的人工智能工具的伦理评估和选择（他们如何将伦理应用于人工智能使用）的心理测量测试或验证。
Shift from assessing rote learning to testing transferability, adaptivity and creativity: Assessment methods should move from the measurement of fixed, repetitive operations to the design and use of varied tasks to assess how students can transfer knowledge and skills across contexts (how students can transfer knowledge and skills) and adapt to novel situations. Methods should also shift (how students can adapt); move from a limited focus on the fluency of operating existing Al tools to how students can critically evaluate existing tools and collaboratively craft or co-create new Al tools (what students can co-create).
从评估死记硬背转向测试可转移性、适应性和创造力：评估方法应从对固定、重复操作的测量转向设计和使用多样化任务，以评估学生如何在不同情境中转移知识和技能（学生如何转移知识和技能）并适应新情况。方法还应转变（学生如何适应）；从对现有人工智能工具的流利操作的有限关注转向学生如何批判性地评估现有工具并协作创造或共同开发新的人工智能工具（学生可以共同创造什么）。
Balance domain-specific and integrative Al competency assessments: Building upon the domain-specific assessments of
平衡领域特定和综合人工智能能力评估：在领域特定评估的基础上，
mindset, understanding and practices of ethics, knowledge and skills, design and use authentic project-based testing to assess students’ integral competencies to blend and integrate ethical principles, Al knowledge and skills, and computational and engineering thinking to critically evaluate AI tools, redesign algorithms or co-create Al systems. These projectbased assessments of how students can integrate AI competencies to solve problems require curriculum developers or teachers to design open and authentic tasks; the breadth of the required competencies should be adapted to the different progression levels, and appropriate grading scales need to be designed to reflect the measurement of open and multilayered competencies.
心态、伦理的理解和实践、知识和技能，设计和使用真实的基于项目的测试来评估学生的综合能力，以融合和整合伦理原则、人工智能知识和技能，以及计算和工程思维，以批判性地评估人工智能工具，重新设计算法或共同创建人工智能系统。这些基于项目的评估如何让学生整合人工智能能力来解决问题，要求课程开发者或教师设计开放和真实的任务；所需能力的广度应根据不同的进展水平进行调整，并需要设计适当的评分标准，以反映开放和多层次能力的测量。
Configure authentic assessment tasks and grading scales for Al competencies: The design of assessment items can be framed by the detailed specifications of each competency block provided in Chapter 4. The configuration of assessment tasks, methods of administering assessment and formats of responses should be aligned with the requirements of each domain (mindset, ethics, conceptual
为人工智能能力配置真实的评估任务和评分标准：评估项目的设计可以根据第 4 章中提供的每个能力模块的详细规范进行框架化。评估任务的配置、评估的实施方法和响应格式应与每个领域（心态、伦理、概念）的要求保持一致。
knowledge on AI, operational AI skills and comprehensive AI system design). This means the specific assessment tasks should be tailored according to the cognitive and behavioural performance that can psychometrically validate the mastery of ‘Understand’, ‘Apply’ and ‘Create’. For the ‘Understand’ level, the tasks may focus more on the comprehension of the concepts and ethical principles underlying performance, with less focus on concrete practical skills, while tasks at the ‘Apply’ level can centre on problem-based practical skills and adaptivity in coping with task variation. For ‘Create’, the measurement tasks might be more about synthesis and algorithmic programming on the conceptualization of new ideas, design of virtual or physical prototypes of new Al tools or systems, the knowledge and skills to test and optimize AI models, the comprehensive computational skills and engineering demonstrated in the co-creation of AI, as well as the human-centred mindset and ethical principles underlying the design and testing.
关于人工智能的知识、操作性人工智能技能和综合人工智能系统设计）。这意味着具体的评估任务应根据能够心理测量验证“理解”、“应用”和“创造”掌握程度的认知和行为表现进行量身定制。对于“理解”层级，任务可能更侧重于对性能背后概念和伦理原则的理解，而较少关注具体的实践技能，而“应用”层级的任务可以集中在基于问题的实践技能和应对任务变化的适应性上。对于“创造”，测量任务可能更多地涉及对新想法的综合和算法编程、虚拟或物理原型的新人工智能工具或系统的设计、测试和优化人工智能模型的知识和技能、在共同创造人工智能中展示的综合计算技能和工程能力，以及设计和测试背后的人本思维和伦理原则。

The focuses of domain-specific assessments divided by three progression levels are recommended as follows for further deliberation, and a non-exhaustive list of examples of assessment items is provided in Table 5 to inspire configurations of assessment instruments that cover all topics and progression levels of the local curriculum.
针对特定领域评估的重点分为三个进展水平，建议如下以供进一步讨论，并在表 5 中提供了一份非详尽的评估项目示例列表，以激发涵盖地方课程所有主题和进展水平的评估工具的配置。

1. Human-centred mindset:
1. 以人为本的思维方式：

1.1 Conflict-based opinion taking
1.1 基于冲突的观点采纳
1.2 Conflict-based critical evaluation
1.2 基于冲突的批判性评估
1.3 Conflict-based social actions
1.3 基于冲突的社会行为
2. Ethics of AI:
2. 人工智能的伦理：
2.1 Scenario-based ethical value orientation
2.1 基于情境的伦理价值取向
2.2 Scenario-based ethical behaviour
2.2 基于情境的伦理行为
2.3 Scenario-based rule-making
2.3 基于情景的规则制定
3. Al techniques and applications:
3. 人工智能技术与应用：
3.1 Problem-based AI knowledge and understanding
3.1 基于问题的人工智能知识与理解
3.2 Tool-based conceptual insights and transferable operation
3.2 基于工具的概念洞察与可转移操作
3.3 Task-based tool crafting
3.3 基于任务的工具制作
4. Al system design:
4. 人工智能系统设计：
4.1 Project-based design thinking
4.1 基于项目的设计思维
4.2 Project-based system configuration
4.2 基于项目的系统配置
4.3 Project-based iteration
4.3 基于项目的迭代

The three forms of assessment under AI system design are based on the virtual environment of Teachable Machine and a simulation project on the design, training, testing and optimization of an Al system. The project should be defined around themes relating to the real-world needs of promoting social inclusion, and using data on local languages or cultural features when training AI models. One critical aspect of the integrated AI competency is the comprehensive ability to iterate Al systems based on feedback, and therefore traditional methods such as paper-based testing should be expanded upon to include metrics that capture a student’s ability to conduct technological conceptualization, and create prototypes and processes for improvements, together with their technical expertise demonstrated in the projects.
AI 系统设计下的三种评估形式基于可教机器的虚拟环境和一个关于 AI 系统设计、训练、测试和优化的模拟项目。该项目应围绕促进社会包容的现实需求主题进行定义，并在训练 AI 模型时使用当地语言或文化特征的数据。综合 AI 能力的一个关键方面是基于反馈迭代 AI 系统的全面能力，因此传统方法如纸质测试应扩展以包括捕捉学生进行技术概念化、创建原型和改进过程的能力的指标，以及他们在项目中展示的技术专长。

Table 5. Examples of assessment tasks
表 5. 评估任务示例

COMPETENCY ASPECTS 能力方面

PROGRESSION LEVELS 进展水平

Understand 理解

Apply 应用

Create 创造

Human-centred mindset 以人为本的思维方式

1.1 Conflict-based opinion taking
1.1 基于冲突的观点采纳

1.1.0 An integral paper and/or computer-based test on the main points of 'Human agency'.
1.1.0 关于“人类能动性”主要观点的综合纸质和/或计算机测试。

1.1.1 Can Al be used in supporting human decisions on values and social issues? Name a weakness of current AI technologies in supporting decisions in relation to values, social issues and personal emotional reactions.
1.1.1 人工智能能否用于支持人类在价值观和社会问题上的决策？请指出当前人工智能技术在支持与价值观、社会问题和个人情感反应相关的决策时的一个弱点。

1.1.2 What will happen if humans don't take accountability in the conceptualization and design of AI systems?
1.1.2 如果人类在人工智能系统的概念化和设计中不承担责任，会发生什么？

1.1.3 Will machine agency become stronger than human agency, and take over more and more human agency? Explain your opinion.
1.1.3 机器代理会变得比人类代理更强大，并逐渐取代越来越多的人类代理吗？请解释你的观点。

1.2 Conflict-based critical evaluation
1.2 基于冲突的批判性评估

1.2.0 An integral paper and/ or computer-based test on the main points of'Human accountability'.
1.2.0 关于“人类责任”主要观点的综合论文和/或计算机测试。

1.2.1 The media reported that artificial general intelligence will arrive by 2030 and will overpower humans in almost all areas, while some Al experts have said AGI may never emerge. Who is correct? Evaluate whether some selected media reports of AI go beyond the genuine capabilities of AI technologies.
1.2.1 媒体报道人工通用智能将在 2030 年到来，并将在几乎所有领域超越人类，而一些人工智能专家则表示 AGI 可能永远不会出现。谁是正确的？评估一些选定的媒体报道是否超出了人工智能技术的真实能力。

1.2.2 In the future, will all minutes of daily meetings and administrative reports be drafted by AI? Do the next generation of students still need to learn how to synthesize materials and draft reports? Assess whether a particular problem in life or subject learning can and/ or should be solved with AI methods.
1.2.2 在未来，所有日常会议和行政报告的记录是否都将由人工智能起草？下一代学生是否仍然需要学习如何综合材料和起草报告？评估生活中的某个特定问题或学科学习是否可以和/或应该通过人工智能方法来解决。

1.3 Conflict-based social interactions
1.3 基于冲突的社会互动

1.3.0 An integral paper and/or computer-based test on the main points of 'Social responsibility'.
1.3.0 关于“社会责任”主要观点的综合论文和/或计算机测试。

1.3.1 Will AI eventually help humans remove the drivers of climate change and protect the planet's well-being? Should human societies mobilize all resources to unlimitedly train Al models? Or has the training of Al models generated irreversible impacts on climate change?
1.3.1 人工智能最终会帮助人类消除气候变化的驱动因素并保护地球的福祉吗？人类社会是否应该动员所有资源无限制地训练人工智能模型？还是说训练人工智能模型已经对气候变化产生了不可逆转的影响？

Analyse how some Al systems can affect environments and climate change, and how their methods could be optimized.
分析一些人工智能系统如何影响环境和气候变化，以及它们的方法如何优化。

1.3.2 Will AI become indispensable and trustworthy co-workers of humans or will AI threaten the safety, inclusion, equity, justice and other social norms of human societies? Critically reflect on the potential impact of AI on human societies.
1.3.2 人工智能会成为人类不可或缺和可信赖的合作伙伴，还是会威胁人类社会的安全、包容、公平、正义和其他社会规范？批判性地反思人工智能对人类社会的潜在影响。

1.3.3 Will AI create jobs for all groups of people equally or will the deployment of AI cause more inequality in economic development in the connection of global markets and your local context? Critically evaluate why AI has become increasingly important and how it may affect your local economy and job market.
1.3.3 人工智能会为所有群体平等创造就业机会，还是人工智能的部署会导致全球市场与本地环境之间经济发展的不平等加剧？批判性地评估人工智能为何变得越来越重要，以及它可能如何影响您的地方经济和就业市场。

1.3.4 AI companies have claimed that they are developing AI tools for all. Will AI enhance or threaten inclusion and equity? Critically evaluate the implications of the wide adoption of Al for inclusion and equity in your local context.
1.3.4 AI 公司声称他们正在为所有人开发 AI 工具。AI 会增强还是威胁包容性和公平性？在您所在的地方，批判性地评估 AI 广泛应用对包容性和公平性的影响。

COMPETENCY ASPECTS PROGRESSION LEVELS Understand Apply Create Human-centred mindset "1.1 Conflict-based opinion taking 1.1.0 An integral paper and/or computer-based test on the main points of 'Human agency'. 1.1.1 Can Al be used in supporting human decisions on values and social issues? Name a weakness of current AI technologies in supporting decisions in relation to values, social issues and personal emotional reactions. 1.1.2 What will happen if humans don't take accountability in the conceptualization and design of AI systems? 1.1.3 Will machine agency become stronger than human agency, and take over more and more human agency? Explain your opinion." "1.2 Conflict-based critical evaluation 1.2.0 An integral paper and/ or computer-based test on the main points of'Human accountability'. 1.2.1 The media reported that artificial general intelligence will arrive by 2030 and will overpower humans in almost all areas, while some Al experts have said AGI may never emerge. Who is correct? Evaluate whether some selected media reports of AI go beyond the genuine capabilities of AI technologies. 1.2.2 In the future, will all minutes of daily meetings and administrative reports be drafted by AI? Do the next generation of students still need to learn how to synthesize materials and draft reports? Assess whether a particular problem in life or subject learning can and/ or should be solved with AI methods." "1.3 Conflict-based social interactions 1.3.0 An integral paper and/or computer-based test on the main points of 'Social responsibility'. 1.3.1 Will AI eventually help humans remove the drivers of climate change and protect the planet's well-being? Should human societies mobilize all resources to unlimitedly train Al models? Or has the training of Al models generated irreversible impacts on climate change? Analyse how some Al systems can affect environments and climate change, and how their methods could be optimized. 1.3.2 Will AI become indispensable and trustworthy co-workers of humans or will AI threaten the safety, inclusion, equity, justice and other social norms of human societies? Critically reflect on the potential impact of AI on human societies. 1.3.3 Will AI create jobs for all groups of people equally or will the deployment of AI cause more inequality in economic development in the connection of global markets and your local context? Critically evaluate why AI has become increasingly important and how it may affect your local economy and job market. 1.3.4 AI companies have claimed that they are developing AI tools for all. Will AI enhance or threaten inclusion and equity? Critically evaluate the implications of the wide adoption of Al for inclusion and equity in your local context."

| COMPETENCY ASPECTS | PROGRESSION LEVELS | | | | :---: | :---: | :---: | :---: | | | Understand | Apply | Create | | Human-centred mindset | 1.1 Conflict-based opinion taking 1.1.0 An integral paper and/or computer-based test on the main points of 'Human agency'. 1.1.1 Can Al be used in supporting human decisions on values and social issues? Name a weakness of current AI technologies in supporting decisions in relation to values, social issues and personal emotional reactions. 1.1.2 What will happen if humans don't take accountability in the conceptualization and design of AI systems? 1.1.3 Will machine agency become stronger than human agency, and take over more and more human agency? Explain your opinion. | 1.2 Conflict-based critical evaluation 1.2.0 An integral paper and/ or computer-based test on the main points of'Human accountability'. 1.2.1 The media reported that artificial general intelligence will arrive by 2030 and will overpower humans in almost all areas, while some Al experts have said AGI may never emerge. Who is correct? Evaluate whether some selected media reports of AI go beyond the genuine capabilities of AI technologies. 1.2.2 In the future, will all minutes of daily meetings and administrative reports be drafted by AI? Do the next generation of students still need to learn how to synthesize materials and draft reports? Assess whether a particular problem in life or subject learning can and/ or should be solved with AI methods. | 1.3 Conflict-based social interactions 1.3.0 An integral paper and/or computer-based test on the main points of 'Social responsibility'. 1.3.1 Will AI eventually help humans remove the drivers of climate change and protect the planet's well-being? Should human societies mobilize all resources to unlimitedly train Al models? Or has the training of Al models generated irreversible impacts on climate change? Analyse how some Al systems can affect environments and climate change, and how their methods could be optimized. 1.3.2 Will AI become indispensable and trustworthy co-workers of humans or will AI threaten the safety, inclusion, equity, justice and other social norms of human societies? Critically reflect on the potential impact of AI on human societies. 1.3.3 Will AI create jobs for all groups of people equally or will the deployment of AI cause more inequality in economic development in the connection of global markets and your local context? Critically evaluate why AI has become increasingly important and how it may affect your local economy and job market. 1.3.4 AI companies have claimed that they are developing AI tools for all. Will AI enhance or threaten inclusion and equity? Critically evaluate the implications of the wide adoption of Al for inclusion and equity in your local context. |

COMPETENCY ASPECTS 能力方面

PROGRESSION LEVELS 进展水平

Understand 理解

Apply 应用

Create 创建

Ethics of AI 人工智能伦理

2.1 Scenario-based ethical value orientation
2.1 基于情境的伦理价值导向

2.1.0 An integral paper and/or computer-based test on the main points of 'Ethical principles'.
2.1.0 关于“伦理原则”主要内容的综合纸质和/或计算机测试。

2.1.1 You have never expressed consent to the use of your personal data to train AI models, so are your personal data protected and safe? Describe how personal online data have been collected and used without consent.
2.1.1 你从未表示同意使用你的个人数据来训练人工智能模型，那么你的个人数据是否受到保护和安全？描述个人在线数据是如何在未获得同意的情况下被收集和使用的。

2.1.2 You have only entered your personal data in the prompt to request a 'trustworthy' generative Al system to help you draft a recommendation letter. Can you be sure your private data won't be disclosed? Describe how sensitive personal data may be collected through prompts or interactions with AI systems.
2.1.2 你仅在提示中输入了个人数据，以请求一个“可信赖”的生成式人工智能系统来帮助你撰写推荐信。你能确保你的私人数据不会被泄露吗？描述敏感个人数据是如何通过提示或与人工智能系统的互动中被收集的。

2.1.3 Video-sharing platforms such as YouTube and TikTok look as if they can understand what sorts of videos the users may like and know how to recommend videos that will be of interest to users. Please identify ethical issues around the video-recommendation algorithms used by video platforms.
2.1.3 视频分享平台如 YouTube 和 TikTok 似乎能够理解用户可能喜欢什么类型的视频，并知道如何推荐用户感兴趣的视频。请识别视频平台使用的视频推荐算法的伦理问题。

2.2 Scenario-based ethical behaviours
2.2 基于情境的伦理行为

2.2.0 An integral paper and/ or computer-based test on the main points of 'Safe and responsible use'.
2.2.0 关于“安全和负责任使用”主要点的综合纸质和/或计算机测试。

2.2.1 Explain why data security must be considered when developing and using AI applications.
2.2.1 解释为什么在开发和使用人工智能应用程序时必须考虑数据安全。

2.2.2 If we want to benefit from the useful services offered by an Al system, is it necessary to forego some of our personal privacy to enjoy the benefits? Explain why data privacy must be considered when developing and using AI applications.
2.2.2 如果我们想要从人工智能系统提供的有用服务中受益，是否有必要放弃一些个人隐私以享受这些好处？解释为什么在开发和使用人工智能应用程序时必须考虑数据隐私。

2.2.3 I have tried many AI platforms and they always provided service exceeding my expectations, so I don't need to be provided with the explanation on how these AI models work?' Evaluate this statement and describe the concept of explainable AI.
2.2.3 我尝试过许多人工智能平台，它们总是提供超出我预期的服务，所以我不需要被告知这些人工智能模型是如何工作的？评估这个陈述并描述可解释人工智能的概念。

2.2.4’I used a photo of one of my friends to generate a video using a generative AI tool and it looks very real, and I posted it online for fun; I used a generative AI system to author essays based on my 'creative' prompts and I published them in my name.' Evaluate one or both of these statements and describe potential legal problems that may arise when using Al-generated content or claiming it as 'your' work.
2.2.4 我用我朋友的一张照片生成了一个视频，使用了一个生成性人工智能工具，看起来非常真实，我为了好玩把它发布到网上；我使用生成性人工智能系统根据我的“创意”提示撰写文章，并以我的名义发表。评估这一个或两个陈述，并描述使用人工智能生成内容或声称它是“你的”作品时可能出现的法律问题。

2.3 Scenario-based rule-making
2.3 基于场景的规则制定

2.3.0 An integral paper and/or computer-based test on the main points of 'Co-creating ethical rules'.
2.3.0 关于“共同创造伦理规则”主要观点的综合论文和/或计算机测试。

2.3.1 Has your country or school (district) developed regulations on the use of AI (or generative AI)? If yes, critically evaluate the regulations against core principles of UNESCO's Recommendation on the Ethics of AI and/or with the EU AI Act. If no, develop a proposal to justify the necessity of regulations and outline the main points they should cover.
2.3.1 您的国家或学校（学区）是否制定了关于人工智能（或生成性人工智能）使用的规定？如果是，请根据联合国教科文组织《人工智能伦理建议》的核心原则和/或欧盟人工智能法案对这些规定进行批判性评估。如果不是，请提出一个建议，以证明规定的必要性，并概述它们应涵盖的主要内容。

2.3.2 Co-create ethical guidance for yourself and your peers on the use of video-recommendation platforms or generative AI systems.
2.3.2 与您和您的同龄人共同创建关于视频推荐平台或生成性人工智能系统使用的伦理指导。

2.3.3 Co-create a set of ethical rules for the safe and responsible use of Al in your schools and at home.
2.3.3 共同创建一套在学校和家庭中安全和负责任使用人工智能的伦理规则。

2.3.4 Co-create regulatory rules for the brain-computer interface (BCI) technology
2.3.4 共同创建脑机接口（BCI）技术的监管规则。

COMPETENCY ASPECTS PROGRESSION LEVELS Understand Apply Create Ethics of AI "2.1 Scenario-based ethical value orientation 2.1.0 An integral paper and/or computer-based test on the main points of 'Ethical principles'. 2.1.1 You have never expressed consent to the use of your personal data to train AI models, so are your personal data protected and safe? Describe how personal online data have been collected and used without consent. 2.1.2 You have only entered your personal data in the prompt to request a 'trustworthy' generative Al system to help you draft a recommendation letter. Can you be sure your private data won't be disclosed? Describe how sensitive personal data may be collected through prompts or interactions with AI systems. 2.1.3 Video-sharing platforms such as YouTube and TikTok look as if they can understand what sorts of videos the users may like and know how to recommend videos that will be of interest to users. Please identify ethical issues around the video-recommendation algorithms used by video platforms." "2.2 Scenario-based ethical behaviours 2.2.0 An integral paper and/ or computer-based test on the main points of 'Safe and responsible use'. 2.2.1 Explain why data security must be considered when developing and using AI applications. 2.2.2 If we want to benefit from the useful services offered by an Al system, is it necessary to forego some of our personal privacy to enjoy the benefits? Explain why data privacy must be considered when developing and using AI applications. 2.2.3 I have tried many AI platforms and they always provided service exceeding my expectations, so I don't need to be provided with the explanation on how these AI models work?' Evaluate this statement and describe the concept of explainable AI. 2.2.4’I used a photo of one of my friends to generate a video using a generative AI tool and it looks very real, and I posted it online for fun; I used a generative AI system to author essays based on my 'creative' prompts and I published them in my name.' Evaluate one or both of these statements and describe potential legal problems that may arise when using Al-generated content or claiming it as 'your' work." "2.3 Scenario-based rule-making 2.3.0 An integral paper and/or computer-based test on the main points of 'Co-creating ethical rules'. 2.3.1 Has your country or school (district) developed regulations on the use of AI (or generative AI)? If yes, critically evaluate the regulations against core principles of UNESCO's Recommendation on the Ethics of AI and/or with the EU AI Act. If no, develop a proposal to justify the necessity of regulations and outline the main points they should cover. 2.3.2 Co-create ethical guidance for yourself and your peers on the use of video-recommendation platforms or generative AI systems. 2.3.3 Co-create a set of ethical rules for the safe and responsible use of Al in your schools and at home. 2.3.4 Co-create regulatory rules for the brain-computer interface (BCI) technology"

| COMPETENCY ASPECTS | PROGRESSION LEVELS | | | | :---: | :---: | :---: | :---: | | | Understand | Apply | Create | | Ethics of AI | 2.1 Scenario-based ethical value orientation 2.1.0 An integral paper and/or computer-based test on the main points of 'Ethical principles'. 2.1.1 You have never expressed consent to the use of your personal data to train AI models, so are your personal data protected and safe? Describe how personal online data have been collected and used without consent. 2.1.2 You have only entered your personal data in the prompt to request a 'trustworthy' generative Al system to help you draft a recommendation letter. Can you be sure your private data won't be disclosed? Describe how sensitive personal data may be collected through prompts or interactions with AI systems. 2.1.3 Video-sharing platforms such as YouTube and TikTok look as if they can understand what sorts of videos the users may like and know how to recommend videos that will be of interest to users. Please identify ethical issues around the video-recommendation algorithms used by video platforms. | 2.2 Scenario-based ethical behaviours 2.2.0 An integral paper and/ or computer-based test on the main points of 'Safe and responsible use'. 2.2.1 Explain why data security must be considered when developing and using AI applications. 2.2.2 If we want to benefit from the useful services offered by an Al system, is it necessary to forego some of our personal privacy to enjoy the benefits? Explain why data privacy must be considered when developing and using AI applications. 2.2.3 I have tried many AI platforms and they always provided service exceeding my expectations, so I don't need to be provided with the explanation on how these AI models work?' Evaluate this statement and describe the concept of explainable AI. 2.2.4’I used a photo of one of my friends to generate a video using a generative AI tool and it looks very real, and I posted it online for fun; I used a generative AI system to author essays based on my 'creative' prompts and I published them in my name.' Evaluate one or both of these statements and describe potential legal problems that may arise when using Al-generated content or claiming it as 'your' work. | 2.3 Scenario-based rule-making 2.3.0 An integral paper and/or computer-based test on the main points of 'Co-creating ethical rules'. 2.3.1 Has your country or school (district) developed regulations on the use of AI (or generative AI)? If yes, critically evaluate the regulations against core principles of UNESCO's Recommendation on the Ethics of AI and/or with the EU AI Act. If no, develop a proposal to justify the necessity of regulations and outline the main points they should cover. 2.3.2 Co-create ethical guidance for yourself and your peers on the use of video-recommendation platforms or generative AI systems. 2.3.3 Co-create a set of ethical rules for the safe and responsible use of Al in your schools and at home. 2.3.4 Co-create regulatory rules for the brain-computer interface (BCI) technology |

COMPETENCY ASPECTS 能力方面

PROGRESSION LEVELS 进展水平

Understand 理解

Apply 应用

Create 创建

Al techniques and applications
人工智能技术与应用

3.1 Problem-based Al knowledge and understanding
3.1 基于问题的人工智能知识与理解

3.1.0 Competency-based or criterion-referenced examination on key conceptual knowledge on Al.
3.1.0 基于能力或标准参考的考试，考察人工智能的关键概念知识。

3.1.1 Describe or exemplify (using tools) what Al is and is not; or exemplify personal, schoolbased or public tools that are supported by Al.
3.1.1 描述或举例（使用工具）什么是人工智能，什么不是；或者举例个人、基于学校或公共的受人工智能支持的工具。

3.1.2 Explain the difference between strong AI and weak AI.
3.1.2 解释强人工智能和弱人工智能之间的区别。

3.1.3 Describe the basic concept of big data; give a couple of examples of misusing big data.
3.1.3 描述大数据的基本概念；举几个滥用大数据的例子。

3.1.4 Explain how machine-learning models are trained, tested and optimized; explain why data play an important role in the training, development and further iterations of an AI model.
3.1.4 解释机器学习模型是如何训练、测试和优化的；解释数据在人工智能模型的训练、开发和进一步迭代中为何扮演重要角色。

3.1.5 Explain how deep learning relates to machine learning.
3.1.5 解释深度学习与机器学习的关系。

3.1.6 Define the term 'artificial neural network' (or other key concepts applicable for the 'Understand'level).
3.1.6 定义“人工神经网络”一词（或其他适用于“理解”级别的关键概念）。

3.2 Tool-based conceptual insights and transferable operation
3.2 基于工具的概念洞察和可转移操作

3.2.0 Criterion-referenced, computer-based examination on the fluency, transferability and adaptability of operational skills on data, algorithms and programming.
3.2.0 基于标准的计算机化考试，评估在数据、算法和编程方面的操作技能的流畅性、可转移性和适应性。

3.2.1 Exemplify applications which use any of the following: natural language processing, computer vision, speech recognition, image recognition, autonomous agent systems, emotion detection, data-based prediction or generative AI.
举例说明使用以下任一项的应用程序：自然语言处理、计算机视觉、语音识别、图像识别、自动代理系统、情感检测、基于数据的预测或生成式人工智能。

3.2.2 Explain how supervised learning, unsupervised learning and reinforcement learning work on a basic level.
解释监督学习、无监督学习和强化学习在基本层面上的工作原理。

3.2.3 Exemplify typical Al algorithms under the categories of supervised learning, unsupervised learning and reinforcement learning; exemplify tools that use some of these typical algorithms.
举例说明监督学习、无监督学习和强化学习类别下的典型人工智能算法；举例说明使用这些典型算法的一些工具。

3.2.4 Explain what AI algorithms are used and integrated by a given generative AI system.
解释给定生成式人工智能系统使用和集成的人工智能算法。

3.2.5 Exemplify two to three open-source datasets and libraries of AI algorithms; explain the advantages and limitations of open-source datasets and algorithm libraries.
举例说明两个到三个开源数据集和 AI 算法库；解释开源数据集和算法库的优缺点。

3.3 Task-based tool crafting
基于任务的工具制作

3.3.0 Computer-based individual or group work to customize existing Al toolkit(s) to create a task-based Al tool.
基于计算机的个人或小组工作，以定制现有的 AI 工具包，创建基于任务的 AI 工具。

3.3.1 Explain how sensors, crawling software, and other tools are used by Al researchers and designers to collect data that can be used to train AI models.
解释传感器、爬虫软件和其他工具如何被 AI 研究人员和设计师用来收集可以用于训练 AI 模型的数据。

3.3.2 Explain and/or demonstrate by operation how to find and reuse open-source datasets and libraries of AI algorithms; evaluate the benefits and risks in comparison with Al options from proprietary enterprises.
3.3.2 通过操作解释和/或演示如何查找和重用开源数据集和 AI 算法库；评估与专有企业的 AI 选项相比的好处和风险。

3.3.3 Draft a design-anddevelopment plan on a task-based Al tool to address real-world needs in and beyond the local context. The plan should cover the following criteria on an ageappropriate level: critical analysis of existing Al tools, assessment of need for data, methods to collect and process data, appropriate Al algorithms and programming languages, open-source Al tools or systems that can be customized or fine-tuned, and parameters for the testing of the Al tools.
3.3.3 起草一个基于任务的 AI 工具的设计和开发计划，以满足本地及更广泛的现实需求。该计划应在适合年龄的水平上涵盖以下标准：对现有 AI 工具的批判性分析、对数据需求的评估、数据收集和处理的方法、适当的 AI 算法和编程语言、可以定制或微调的开源 AI 工具或系统，以及对 AI 工具测试的参数。

COMPETENCY ASPECTS PROGRESSION LEVELS Understand Apply Create Al techniques and applications "3.1 Problem-based Al knowledge and understanding 3.1.0 Competency-based or criterion-referenced examination on key conceptual knowledge on Al. 3.1.1 Describe or exemplify (using tools) what Al is and is not; or exemplify personal, schoolbased or public tools that are supported by Al. 3.1.2 Explain the difference between strong AI and weak AI. 3.1.3 Describe the basic concept of big data; give a couple of examples of misusing big data. 3.1.4 Explain how machine-learning models are trained, tested and optimized; explain why data play an important role in the training, development and further iterations of an AI model. 3.1.5 Explain how deep learning relates to machine learning. 3.1.6 Define the term 'artificial neural network' (or other key concepts applicable for the 'Understand'level)." "3.2 Tool-based conceptual insights and transferable operation 3.2.0 Criterion-referenced, computer-based examination on the fluency, transferability and adaptability of operational skills on data, algorithms and programming. 3.2.1 Exemplify applications which use any of the following: natural language processing, computer vision, speech recognition, image recognition, autonomous agent systems, emotion detection, data-based prediction or generative AI. 3.2.2 Explain how supervised learning, unsupervised learning and reinforcement learning work on a basic level. 3.2.3 Exemplify typical Al algorithms under the categories of supervised learning, unsupervised learning and reinforcement learning; exemplify tools that use some of these typical algorithms. 3.2.4 Explain what AI algorithms are used and integrated by a given generative AI system. 3.2.5 Exemplify two to three open-source datasets and libraries of AI algorithms; explain the advantages and limitations of open-source datasets and algorithm libraries." "3.3 Task-based tool crafting 3.3.0 Computer-based individual or group work to customize existing Al toolkit(s) to create a task-based Al tool. 3.3.1 Explain how sensors, crawling software, and other tools are used by Al researchers and designers to collect data that can be used to train AI models. 3.3.2 Explain and/or demonstrate by operation how to find and reuse open-source datasets and libraries of AI algorithms; evaluate the benefits and risks in comparison with Al options from proprietary enterprises. 3.3.3 Draft a design-anddevelopment plan on a task-based Al tool to address real-world needs in and beyond the local context. The plan should cover the following criteria on an ageappropriate level: critical analysis of existing Al tools, assessment of need for data, methods to collect and process data, appropriate Al algorithms and programming languages, open-source Al tools or systems that can be customized or fine-tuned, and parameters for the testing of the Al tools."

| COMPETENCY ASPECTS | PROGRESSION LEVELS | | | | :---: | :---: | :---: | :---: | | | Understand | Apply | Create | | Al techniques and applications | 3.1 Problem-based Al knowledge and understanding 3.1.0 Competency-based or criterion-referenced examination on key conceptual knowledge on Al. 3.1.1 Describe or exemplify (using tools) what Al is and is not; or exemplify personal, schoolbased or public tools that are supported by Al. 3.1.2 Explain the difference between strong AI and weak AI. 3.1.3 Describe the basic concept of big data; give a couple of examples of misusing big data. 3.1.4 Explain how machine-learning models are trained, tested and optimized; explain why data play an important role in the training, development and further iterations of an AI model. 3.1.5 Explain how deep learning relates to machine learning. 3.1.6 Define the term 'artificial neural network' (or other key concepts applicable for the 'Understand'level). | 3.2 Tool-based conceptual insights and transferable operation 3.2.0 Criterion-referenced, computer-based examination on the fluency, transferability and adaptability of operational skills on data, algorithms and programming. 3.2.1 Exemplify applications which use any of the following: natural language processing, computer vision, speech recognition, image recognition, autonomous agent systems, emotion detection, data-based prediction or generative AI. 3.2.2 Explain how supervised learning, unsupervised learning and reinforcement learning work on a basic level. 3.2.3 Exemplify typical Al algorithms under the categories of supervised learning, unsupervised learning and reinforcement learning; exemplify tools that use some of these typical algorithms. 3.2.4 Explain what AI algorithms are used and integrated by a given generative AI system. 3.2.5 Exemplify two to three open-source datasets and libraries of AI algorithms; explain the advantages and limitations of open-source datasets and algorithm libraries. | 3.3 Task-based tool crafting 3.3.0 Computer-based individual or group work to customize existing Al toolkit(s) to create a task-based Al tool. 3.3.1 Explain how sensors, crawling software, and other tools are used by Al researchers and designers to collect data that can be used to train AI models. 3.3.2 Explain and/or demonstrate by operation how to find and reuse open-source datasets and libraries of AI algorithms; evaluate the benefits and risks in comparison with Al options from proprietary enterprises. 3.3.3 Draft a design-anddevelopment plan on a task-based Al tool to address real-world needs in and beyond the local context. The plan should cover the following criteria on an ageappropriate level: critical analysis of existing Al tools, assessment of need for data, methods to collect and process data, appropriate Al algorithms and programming languages, open-source Al tools or systems that can be customized or fine-tuned, and parameters for the testing of the Al tools. |

COMPETENCY ASPECTS 能力方面

PROGRESSION LEVELS 进展水平

Understand 理解

Apply 应用

Create 创造

Al system design 人工智能系统设计

4.1 Project-based design thinking
4.1 基于项目的设计思维

4.1.0 Simulated tests on problem-scoping for Al system design. Request that students produce a report and/or oral defence on problem scoping or on a project proposal. The report can be evaluated according to the following criteria: why AI should be used for the problem based on a checklist; and the problem statement including key requirements or features of the AI systems such as algorithms, datasets and functionalities.
4.1.0 针对人工智能系统设计的问题范围进行模拟测试。要求学生就问题范围或项目提案撰写报告和/或进行口头辩护。报告可以根据以下标准进行评估：为什么应该使用人工智能来解决该问题，基于检查清单；以及问题陈述，包括人工智能系统的关键要求或特性，如算法、数据集和功能。

4.1.1 Explain why a specific real-world challenge (given by teachers) should not be solved by an AI tool.
4.1.1 解释为什么特定的现实挑战（由教师给出）不应由人工智能工具解决。

4.1.2 Computer-based test on data preprocessing techniques, drawing on open-source datasets including adjusting the data augmentation, handling outliers, analysing dataset skew or imbalance, training the model based on modified datasets, and observing how data preprocessing affects the model's performance compared to the given dataset.
4.1.2 基于计算机的数据预处理技术测试，利用开源数据集，包括调整数据增强、处理异常值、分析数据集偏斜或不平衡、基于修改后的数据集训练模型，并观察数据预处理如何影响模型的性能与给定数据集的比较。

4.2 Project-based system configuration
4.2 基于项目的系统配置

4.2.0 Computer-based tests on the architectural configuration of AI. The simulated operation can be evaluated using the following criteria: assessment and selection of frameworks for Al architectures; evaluation and choice of solutions for the layers and components of the AI architecture; the configuration of a prototype architecture; and the presentation of the configuration.
4.2.0 关于 AI 架构配置的计算机测试。可以使用以下标准评估模拟操作：对 AI 架构框架的评估和选择；对 AI 架构层和组件解决方案的评估和选择；原型架构的配置；以及配置的展示。

4.2.1 Explain how opensource datasets and libraries of Al programming can be leveraged to build an AI system including locally accessible cloud computing platforms or operating systems, and software needed by the training of machine-learning models.
4.2.1 解释如何利用开源数据集和 AI 编程库来构建 AI 系统，包括本地可访问的云计算平台或操作系统，以及训练机器学习模型所需的软件。

4.2.2 Explain what criteria should be considered to optimize for efficiency and minimize computational resource waste when configuring Al architecture.
4.2.2 解释在配置 AI 架构时应考虑哪些标准以优化效率并最小化计算资源浪费。

4.2.3 Calculate the selected Al model's consumption of computing resources, and design strategies for improving the efficiency of Al methods to reduce its environmental impact.
4.2.3 计算所选人工智能模型的计算资源消耗，并设计提高人工智能方法效率的策略，以减少其对环境的影响。

4.3 Project-based iteration
4.3 基于项目的迭代

4.3.0 Computer-based simulated optimization of a simple Al model, including operational optimization of the datasets, algorithms and parameter adjustment, and the design of functionalities and interfaces; and/or reconfiguration of the architectures, including modifying the problem-scoping.
4.3.0 基于计算机的简单人工智能模型的模拟优化，包括数据集、算法和参数调整的操作优化，以及功能和接口的设计；和/或架构的重新配置，包括修改问题范围。

4.3.1 Design a set of metrics for the performance-testing of an exemplar AI system. Explain what metrics can be designed or adapted to support the measurement of the system's performance and to collect feedback from end users on the societal implications and environmental impact. Exemplify open-source tools that can conduct and report on the performance-testing of an AI system.
4.3.1 设计一套用于示例人工智能系统性能测试的指标。解释可以设计或调整哪些指标以支持系统性能的测量，并收集最终用户对社会影响和环境影响的反馈。举例说明可以进行人工智能系统性能测试和报告的开源工具。

4.3.2 Draft a report to explain what decision should be taken on an Al system and why, based on the findings of simulated performance tests and user feedback. Include explanations of decisions to optimize, reconfigure and shut down the system; present the plan for optimization or reconfiguration, or for mitigation strategies if the AI system has the potential to cause harm.
4.3.2 起草一份报告，解释基于模拟性能测试和用户反馈应对人工智能系统采取何种决策及其原因。包括优化、重新配置和关闭系统的决策解释；呈现优化或重新配置的计划，或在人工智能系统可能造成危害的情况下的缓解策略。

4.3.3 Exemplify locally accessible online communities of Al cocreators; explain what a student can do in these communities.
4.3.3 举例说明本地可访问的人工智能共同创作者在线社区；解释学生在这些社区中可以做些什么。

COMPETENCY ASPECTS PROGRESSION LEVELS Understand Apply Create Al system design "4.1 Project-based design thinking 4.1.0 Simulated tests on problem-scoping for Al system design. Request that students produce a report and/or oral defence on problem scoping or on a project proposal. The report can be evaluated according to the following criteria: why AI should be used for the problem based on a checklist; and the problem statement including key requirements or features of the AI systems such as algorithms, datasets and functionalities. 4.1.1 Explain why a specific real-world challenge (given by teachers) should not be solved by an AI tool. 4.1.2 Computer-based test on data preprocessing techniques, drawing on open-source datasets including adjusting the data augmentation, handling outliers, analysing dataset skew or imbalance, training the model based on modified datasets, and observing how data preprocessing affects the model's performance compared to the given dataset." "4.2 Project-based system configuration 4.2.0 Computer-based tests on the architectural configuration of AI. The simulated operation can be evaluated using the following criteria: assessment and selection of frameworks for Al architectures; evaluation and choice of solutions for the layers and components of the AI architecture; the configuration of a prototype architecture; and the presentation of the configuration. 4.2.1 Explain how opensource datasets and libraries of Al programming can be leveraged to build an AI system including locally accessible cloud computing platforms or operating systems, and software needed by the training of machine-learning models. 4.2.2 Explain what criteria should be considered to optimize for efficiency and minimize computational resource waste when configuring Al architecture. 4.2.3 Calculate the selected Al model's consumption of computing resources, and design strategies for improving the efficiency of Al methods to reduce its environmental impact." "4.3 Project-based iteration 4.3.0 Computer-based simulated optimization of a simple Al model, including operational optimization of the datasets, algorithms and parameter adjustment, and the design of functionalities and interfaces; and/or reconfiguration of the architectures, including modifying the problem-scoping. 4.3.1 Design a set of metrics for the performance-testing of an exemplar AI system. Explain what metrics can be designed or adapted to support the measurement of the system's performance and to collect feedback from end users on the societal implications and environmental impact. Exemplify open-source tools that can conduct and report on the performance-testing of an AI system. 4.3.2 Draft a report to explain what decision should be taken on an Al system and why, based on the findings of simulated performance tests and user feedback. Include explanations of decisions to optimize, reconfigure and shut down the system; present the plan for optimization or reconfiguration, or for mitigation strategies if the AI system has the potential to cause harm. 4.3.3 Exemplify locally accessible online communities of Al cocreators; explain what a student can do in these communities."

| COMPETENCY ASPECTS | PROGRESSION LEVELS | | | | :---: | :---: | :---: | :---: | | | Understand | Apply | Create | | Al system design | 4.1 Project-based design thinking 4.1.0 Simulated tests on problem-scoping for Al system design. Request that students produce a report and/or oral defence on problem scoping or on a project proposal. The report can be evaluated according to the following criteria: why AI should be used for the problem based on a checklist; and the problem statement including key requirements or features of the AI systems such as algorithms, datasets and functionalities. 4.1.1 Explain why a specific real-world challenge (given by teachers) should not be solved by an AI tool. 4.1.2 Computer-based test on data preprocessing techniques, drawing on open-source datasets including adjusting the data augmentation, handling outliers, analysing dataset skew or imbalance, training the model based on modified datasets, and observing how data preprocessing affects the model's performance compared to the given dataset. | 4.2 Project-based system configuration 4.2.0 Computer-based tests on the architectural configuration of AI. The simulated operation can be evaluated using the following criteria: assessment and selection of frameworks for Al architectures; evaluation and choice of solutions for the layers and components of the AI architecture; the configuration of a prototype architecture; and the presentation of the configuration. 4.2.1 Explain how opensource datasets and libraries of Al programming can be leveraged to build an AI system including locally accessible cloud computing platforms or operating systems, and software needed by the training of machine-learning models. 4.2.2 Explain what criteria should be considered to optimize for efficiency and minimize computational resource waste when configuring Al architecture. 4.2.3 Calculate the selected Al model's consumption of computing resources, and design strategies for improving the efficiency of Al methods to reduce its environmental impact. | 4.3 Project-based iteration 4.3.0 Computer-based simulated optimization of a simple Al model, including operational optimization of the datasets, algorithms and parameter adjustment, and the design of functionalities and interfaces; and/or reconfiguration of the architectures, including modifying the problem-scoping. 4.3.1 Design a set of metrics for the performance-testing of an exemplar AI system. Explain what metrics can be designed or adapted to support the measurement of the system's performance and to collect feedback from end users on the societal implications and environmental impact. Exemplify open-source tools that can conduct and report on the performance-testing of an AI system. 4.3.2 Draft a report to explain what decision should be taken on an Al system and why, based on the findings of simulated performance tests and user feedback. Include explanations of decisions to optimize, reconfigure and shut down the system; present the plan for optimization or reconfiguration, or for mitigation strategies if the AI system has the potential to cause harm. 4.3.3 Exemplify locally accessible online communities of Al cocreators; explain what a student can do in these communities. |

Agile formats of concrete assessments and corresponding grading scales that fit neatly into different assessment items and objectives should be designed, tested and optimized. These may include formative and peer assessments in the form of reflective essays, oral presentations or reports of users’ tests of Al tools; and summative examinations on paper and/or via computer-based or unplugged design, including prototypes of AI tools or drawing of algorithms, essays about case studies on Al’s ethical issues, technical reports on the design and development of Al tools or systems, the fine-tuning or simulated training of Al models, and the assembling or creation of hardware.
应设计、测试和优化适合不同评估项目和目标的具体评估的敏捷格式及相应的评分标准。这些可能包括以反思性论文、口头报告或用户对人工智能工具测试的报告形式进行的形成性和同行评估；以及纸质和/或通过计算机基础或非计算机设计的总结性考试，包括人工智能工具的原型或算法绘图、关于人工智能伦理问题的案例研究论文、关于人工智能工具或系统的设计和开发的技术报告、人工智能模型的微调或模拟训练，以及硬件的组装或创建。

This large array of concrete methods should be examined in a nuanced manner against the specific needs of aspects and infused flexibly in the implementation of the AI CFS.
这一大批具体方法应根据各方面的具体需求进行细致审查，并灵活地融入 AI 能力框架的实施中。

The use of AI tools for assessments also emerges as a new supplementary method of assessment, for example automating the collection of data on learning processes and formative mastery directly from students or learning management systems, personalizing assessments for students according to their ability or linguistic and cultural background, or facilitating teachers’ decision-making on teaching strategies. While the opportunities being enabled by Al to enhance assessments should be dynamically reviewed and properly leveraged, it is critical to examine and regulate ethical issues concerning the collection and use of students’ data; the risks of using AI recommendations and predictions in assessments, especially those with high stakes; and the reduction of teachers’ agency in assessments, particularly the opportunities for teachers to gain insights from analysing learning processes.
使用 AI 工具进行评估也成为了一种新的补充评估方法，例如自动收集学生或学习管理系统中的学习过程和形成性掌握的数据，根据学生的能力或语言和文化背景个性化评估，或促进教师在教学策略上的决策。虽然 AI 所带来的增强评估的机会应动态审查并妥善利用，但关键是要审查和规范有关学生数据收集和使用的伦理问题；在评估中使用 AI 推荐和预测的风险，尤其是那些高风险的评估；以及在评估中减少教师的自主权，特别是教师从分析学习过程获得见解的机会。

Conclusion 结论

The AI competency framework for students charts an action-oriented programme based on three basic assumptions about the role of education in responding to the pervasive adoption of Al in today’s world. The first is that the education sector, rather than merely adapting to Al systems and tools, must be proactive in developing the competencies required to shape ethical and environmentally-friendly AI. Second, that students should be equipped with the competencies to act both as critical and responsible users and co-creators of Al , as well as leaders in defining and designing the next generation of Al technologies. The third assumption is that students’ AI competencies are to be constructed around the convergence of a human-centred mindset and attitudes, internalized ethics of AI, transferable conceptual knowledge and skills on AI, as well as future-proof thinking relative to Al system design. As Al competency development goes far beyond mere technical skills associated with learning to code or to operate AI tools, the integration of AI-related learning
《AI 能力框架-学生》为学生制定了一个以行动为导向的计划，基于三个关于教育在应对当今世界广泛采用 AI 方面的基本假设。第一个假设是，教育部门不仅要适应 AI 系统和工具，还必须积极主动地发展塑造伦理和环保 AI 所需的能力。第二，学生应具备作为批判性和负责任的用户及 AI 的共同创造者的能力，同时在定义和设计下一代 AI 技术方面担任领导者。第三个假设是，学生的 AI 能力应围绕以人为本的思维方式和态度、内化的 AI 伦理、可转移的 AI 概念知识和技能，以及与 AI 系统设计相关的未来思维进行构建。由于 AI 能力的发展远远超出了学习编码或操作 AI 工具所需的单纯技术技能，因此 AI 相关学习的整合
requires an interdisciplinary approach to curricular integration spanning subjects related to science, technology, engineering, art and mathematics, to social studies and citizenship education.
需要一种跨学科的方法，将与科学、技术、工程、艺术和数学相关的课程整合，扩展到社会研究和公民教育。

This AI competency framework for students is the first attempt to provide a global blueprint to steer a humancentred integration of AI-related learning in curriculum. Informed by expertise and consultations at the international level, the framework serves as a global reference to be adapted across diverse local educational contexts. It is only through adapting and testing the framework among teachers and teacher educators in diverse settings, and surfacing insights from their contextualized practice, that the global framework can be further refined. As such, the framework is a living document which will need to be continuously reviewed on the basis of analysis of practice in a diversity of contexts, as well as in response to new iterations of Al technologies that will emerge.
这个针对学生的人工智能能力框架是首次尝试提供一个全球蓝图，以引导以人为本的人工智能相关学习在课程中的整合。该框架基于国际层面的专业知识和咨询，作为一个全球参考，适用于不同地方的教育背景。只有通过在不同环境中对教师和教师教育者进行适应和测试，并从他们的情境实践中提取见解，全球框架才能进一步完善。因此，该框架是一个活文档，需要根据多样化背景下的实践分析以及对新出现的人工智能技术的响应不断进行审查。

References 参考文献

IEA. 2022. World Energy Statistics and Balances. Paris, International Energy Agency (IEA). Available at: https://www.iea.org/ data-and-statistics/data-product/world-en-ergy-statistics-and-balances (Accessed 26 July 2024.)
IEA. 2022. 世界能源统计与平衡。巴黎，国际能源署（IEA）。可在以下网址获取：https://www.iea.org/data-and-statistics/data-product/world-energy-statistics-and-balances（访问日期：2024 年 7 月 26 日。）
-. 2024. Electricity 2024. Paris, International Energy Agency (IEA). Available at: https:// www.iea.org/reports/electricity-2024 (Accessed 26 July 2024.)
-. 2024. 电力 2024。巴黎，国际能源署（IEA）。可在以下网址获取：https://www.iea.org/reports/electricity-2024（访问日期：2024 年 7 月 26 日。）
Ministry of Science and ICT, Republic of Korea. 2019. “IT 강국을 넘어 AI 강국으로!” 범 정부 역량을 결집하여 Al 시대 미래 비전 과 전략을 담은 'AI 국가전략 발표 [“Beyond an IT powerhouse, to an Al powerhouse!” Announcement of the ‘Al National Strategy’ containing the vision and strategy for the future of the AI era by consolidating the capabilities of the entire government]. Sejong-si, Ministry of Science and ICT, Republic of Korea. (In Korean.) Available at: https:// doc.msit.go.kr/SynapDocViewServer/ viewer/doc.html?key=3035e1e0a5df4f1a9395b5284512a908 (Accessed 17 July 2024.)
韩国科学技术信息部。2019。“超越 IT 强国，迈向 AI 强国！”通过整合全政府的能力，发布包含 AI 时代未来愿景和战略的《AI 国家战略》。世宗市，韩国科学技术信息部。（韩文。）可在以下网址获取：https://doc.msit.go.kr/SynapDocViewServer/viewer/doc.html?key=3035e1e0a5df4f1a9395b5284512a908（访问日期：2024 年 7 月 17 日。）

Patrick, S. and Sturgis, C. 2017. An Introduction to the National Summit on K-12 CompetencyBased Education. 2017. Arlington, Aurora Institute. Available at: https://auro-ra-institute.org/wp-content/uploads/ CompetencyWorks-AnIntroductionToTheNa tionalSummitOnK12CompetencyBasedEducation.pdf (Accessed 26 July 2024.)
Patrick, S. 和 Sturgis, C. 2017. 《K-12 基于能力教育国家峰会简介》。2017 年，阿灵顿，Aurora Institute。可在以下网址获取：https://auro-ra-institute.org/wp-content/uploads/CompetencyWorks-AnIntroductionToTheNationalSummitOnK12CompetencyBasedEducation.pdf（访问日期：2024 年 7 月 26 日。）
Payne, B. H. 2019. An Ethics of Artificial Intelligence Curriculum for Middle School Students. Cambridge, MIT Media Lab. Available at: https://thecenter.mit.edu/ wp-content/uploads/2020/07/MIT-AI-Ethics-Education-Curriculum.pdf (Accessed 26 July 2024.)
Payne, B. H. 2019. 《中学生人工智能伦理课程》。剑桥，麻省理工学院媒体实验室。可在以下网址获取：https://thecenter.mit.edu/wp-content/uploads/2020/07/MIT-AI-Ethics-Education-Curriculum.pdf（访问日期：2024 年 7 月 26 日。）
UNESCO. 2019. Beijing Consensus on Artificial Intelligence and Education. Paris, UNESCO. Available at: https://unesdoc.unesco.org/ ark:/48223/pf0000368303 (Accessed 26 July 2024.)
联合国教科文组织。2019. 《北京共识：人工智能与教育》。巴黎，联合国教科文组织。可在以下网址获取：https://unesdoc.unesco.org/ark:/48223/pf0000368303（访问日期：2024 年 7 月 26 日。）
-. 2021. Reimagining our futures together: a new social contract for education. Paris, UNESCO. Available at: https://unesdoc. unesco.org/ark:/48223/pf0000379707 (Accessed 16 July 2024.)
-. 2021. 《共同重新想象我们的未来：教育的新社会契约》。巴黎，联合国教科文组织。可在以下网址获取：https://unesdoc.unesco.org/ark:/48223/pf0000379707（访问日期：2024 年 7 月 16 日。）
-. 2022a. Recommendation on the Ethics of Artificial Intelligence. Paris, UNESCO. Available at: https://unesdoc.unesco.org/ ark:/48223/pf0000381137 (Accessed 16 July 2024.)
-. 2022a. 关于人工智能伦理的建议。巴黎，联合国教科文组织。可在以下网址获取：https://unesdoc.unesco.org/ark:/48223/pf0000381137（访问日期：2024 年 7 月 16 日。）
-. 2022b. K-12 AI curricula: a mapping of government-endorsed AI curricula. Paris, UNESCO. Available at: https://unesdoc. unesco.org/ark:/48223/pf0000380602 (Accessed 26 July 2024.)
-. 2022b. K-12 人工智能课程：政府认可的人工智能课程的映射。巴黎，联合国教科文组织。可在以下网址获取：https://unesdoc.unesco.org/ark:/48223/pf0000380602（访问日期：2024 年 7 月 26 日。）
-. 2024. Al in the United Arab Emirates’ computing, creative design and innovation K-12 curriculum: a case study. Paris, UNESCO. Available at: https://unesdoc.unesco.org/ ark:/48223/pf0000388652 (Accessed 26 July 2024.)
-. 2024. 阿联酋的计算、创意设计和创新 K-12 课程中的人工智能：案例研究。巴黎，联合国教科文组织。可在以下网址获取：https://unesdoc.unesco.org/ark:/48223/pf0000388652（访问日期：2024 年 7 月 26 日。）

Williams, R., Kaputsos, S. P. and Breazeal, C. 2021. Teacher Perspectives on How To Train Your Robot: A Middle School AI and Ethics Curriculum. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, No. 17. Washington, DC, Association for the Advancement of Artificial Intelligence (AAAI), pp. 15678-15686. Available at: https://doi.org/10.1609/aaai.v35i17.17847 (Accessed 26 July 2024.)
威廉姆斯，R.，卡普佐斯，S. P. 和布雷泽尔，C. 2021. 教师对如何训练你的机器人的看法：一项中学人工智能与伦理课程。美国人工智能协会会议论文集，第 35 卷，第 17 期。华盛顿特区，美国人工智能促进协会（AAAI），第 15678-15686 页。可在以下网址获取：https://doi.org/10.1609/aaai.v35i17.17847（访问日期：2024 年 7 月 26 日。）
World Bank. 2023. Green Digital Transformation: How to Sustainably Close the Digital Divide and Harness Digital Tools for Climate Action. Climate Change and Development Series. Washington, DC, World Bank. Available at: http://hdl.handle. net/10986/40653 (Accessed 26 July 2024.)
世界银行。2023 年。《绿色数字转型：如何可持续地缩小数字鸿沟并利用数字工具应对气候变化》。气候变化与发展系列。华盛顿特区，世界银行。可在以下网址获取：http://hdl.handle.net/10986/40653（访问日期：2024 年 7 月 26 日。）
World Bank and ITU. 2024. Measuring the Emissions & Energy Footprint of the ICT Sector: Implications for Climate Action. Washington, DC, World Bank and Geneva, International Telecommunication Union (ITU). Available at: https://www.itu.int/hub/ publication/d-ind-clim-2023-01 (Accessed 26 July 2024.)(Accessed 26 July 2024.)
世界银行与国际电信联盟（ITU）。2024 年。《测量信息通信技术（ICT）部门的排放与能源足迹：对气候行动的影响》。华盛顿特区，世界银行与日内瓦，国际电信联盟（ITU）。可在以下网址获取：https://www.itu.int/hub/publication/d-ind-clim-2023-01（访问日期：2024 年 7 月 26 日。）（访问日期：2024 年 7 月 26 日。）

Endnotes 结束语

1 The principle of proportionality in Al encompasses the idea that Al systems should be designed and deployed in a manner that appropriately balances risks and benefits, respects human rights, and aligns with societal values and interests. See the Recommendation on the Ethics of AI (UNESCO, 2022a) for more on the proportionality of AI.
1 在人工智能中，比例原则的理念是，人工智能系统的设计和部署应适当地平衡风险和收益，尊重人权，并与社会价值观和利益相一致。有关人工智能比例原则的更多信息，请参见《人工智能伦理建议》（联合国教科文组织，2022 年 a）。
2 RAISE stands for ‘Responsible Al for Social Empowerment and Education’
2 RAISE 代表“负责任的人工智能促进社会赋权和教育”
3 See https://ubuntu.com
3 请访问 https://ubuntu.com
4 See https://machinelearningforkids.co.uk
4 请访问 https://machinelearningforkids.co.uk
5 See https://teachablemachine.withgoogle.com
5 请访问 https://teachablemachine.withgoogle.com
6 See https://www.tensorflow.org
6 请查看 https://www.tensorflow.org
7 See https://keras.io 7 请查看 https://keras.io
8 See https://everyday-ai.org/resources/search?f[0]=tools%3A201
8 请查看 https://everyday-ai.org/resources/search?f[0]=tools%3A201
9 See https://www.aiunplugged.org
9 请查看 https://www.aiunplugged.org
10 See https://iste.org/blog/3-unplugged-activities-for-teaching-about-ai
10 请参见 https://iste.org/blog/3-unplugged-activities-for-teaching-about-ai
11 See https://raise.mit.edu/daily
11 请参见 https://raise.mit.edu/daily
12 See http://yann.lecun.com/exdb/mnist
12 请参见 http://yann.lecun.com/exdb/mnist
13 See https://www.cs.toronto.edu/~kriz/cifar.html
13 请参见 https://www.cs.toronto.edu/~kriz/cifar.html
14 See https://www.image-net.org/index.php
14 请参见 https://www.image-net.org/index.php
15 See https://teachablemachine.withgoogle.com
15 请参见 https://teachablemachine.withgoogle.com
16 See https://appinventor.mit.edu
16 请参见 https://appinventor.mit.edu
17 See https://pytorch.org
17 请参见 https://pytorch.org
18 See https://keras.io 18 请参见 https://keras.io
19 See https://pypi.org/project/beautifulsoup4
19 请参见 https://pypi.org/project/beautifulsoup4

Unesco 联合国教科文组织

United Nations 联合国
Educational, Scientific 教育、科学
and Cultural Organization
和文化组织

Al competency framework 人工智能能力框架

for students 针对学生

The Al competency framework for students presented here is based on an ambitious vision that extends well beyond popular notions of AI literacy. It aims to support students to grow towards being not only effective and ethical users of Al tools, but also co-creators in the design of more inclusive and environmentally sustainable Al. The framework defines the values, as well as the foundational knowledge and transferable skills, required to critically understand and use Al systems in a safe, effective and meaningful manner at different levels of mastery. The framework also proposes detailed specifications on what Al topics can be covered and what pedagogical methods may be deployed to facilitate students’ understanding, application, and creation of AI. It further provides guidance for the curricular integration of AI-related learning, the organization of learning sequences, and the design of competence-based assessments. Seen as an integral set of capabilities required for responsible citizenship in the era of AI, the competencies outlined in this framework are based on principles of inclusivity, the centrality of human agency, nondiscrimination, and respect for linguistic and cultural diversity.
这里提出的学生 AI 能力框架基于一个雄心勃勃的愿景，远远超出了对 AI 素养的普遍认识。它旨在支持学生成长为不仅是有效和道德的 AI 工具使用者，而且是更具包容性和环境可持续性 AI 设计的共同创造者。该框架定义了在不同掌握水平上，批判性理解和安全、有效、有意义地使用 AI 系统所需的价值观、基础知识和可转移技能。该框架还提出了关于可以涵盖哪些 AI 主题以及可以采用哪些教学方法来促进学生对 AI 的理解、应用和创造的详细规范。它进一步提供了关于 AI 相关学习的课程整合、学习序列的组织以及基于能力的评估设计的指导。被视为在人工智能时代负责任公民所需的能力的整体能力集，本框架中概述的能力基于包容性原则、人类代理的中心性、非歧视和对语言及文化多样性的尊重。

Source: Adapted from Payne, B. H. 2019. Available under CC BY-NC 4.0
来源：改编自 Payne, B. H. 2019。根据 CC BY-NC 4.0 许可提供

侖领 Unesco 侖领 联合国教科文组织

Al competency framework 人工智能能力框架

UNESCO - a global leader in education联合国教科文组织 - 教育领域的全球领导者

The Global Education 2030 Agenda全球教育 2030 议程

unesco 教科文组织

Education 2030 教育 2030

SHORTSUMMARY 简短摘要

Preparing students to be responsible and creative citizens in the era of AI准备学生在人工智能时代成为负责任和富有创造力的公民

IIIII Unesco 联合国教科文组织

Al competency framework 人工智能能力框架

Stefania Giannini 斯特法尼亚·贾尼尼

Acknowledgements 致谢

Table of contents 目录

List of tables 表格列表

List of acronyms and abbreviations缩略语和缩写列表

Chapter 1: Introduction 第 1 章：引言

1.1 Why an Al competency framework for students?1.1 为什么需要学生的人工智能能力框架？

1.2 Purpose and target audience1.2 目的和目标受众

Chapter 2: Key principles第 2 章：关键原则

2.1 Fostering a critical approach to AI2.1 培养对人工智能的批判性思维

2.2 Prioritizing human-centred interaction with AI2.2 优先考虑以人为本的人工智能交互

2.3 Encouraging environmentally sustainable AI2.3 鼓励环境可持续的人工智能

2.4 Promoting inclusivity in AI competency development2.4 促进人工智能能力发展的包容性

2.5 Building core AI competencies for lifelong learning2.5 建立终身学习的核心人工智能能力

Chapter 3: Structure of the Al competency第三章：人工智能能力的结构

framework for students 学生的框架

3.1 The framework 3.1 框架

3.2 Progression levels 3.2 进阶水平

Level 1: Understand 第一层次：理解

Level 2: Apply 第二级：应用

Level 3: Create 第三层次：创造

3.3 Aspects 3.3 方面

Human-centred mindset 以人为本的思维方式

Ethics of AI 人工智能的伦理

Al techniques and applications人工智能技术和应用

Al system design 人工智能系统设计

Chapter 4: Specifications of AI competencies第四章：AI 能力规范

for students 针对学生

4.1 Level 1: Understand4.1 级别 1：理解

4.2 Level 2: Apply4.2 第二级：应用

4.3 Level 3: Create4.3 级别 3：创建

Chapter 5: Applying the framework第五章：应用框架

5.1 Aligning Al competencies as the foundation for national Al strategies5.1 将人工智能能力作为国家人工智能战略的基础

Abstract 摘要

Box 1: Recommendation on the Ethics of Artificial Intelligence框架 1：关于人工智能伦理的建议

Box 2: Supporting human resource development: The Republic of Korea's National Strategy for Artificial Intelligence案例 2：支持人力资源发展：韩国的国家人工智能战略

5.2 Building interdisciplinary core and cluster AI curricula for AI competency5.2 建立跨学科核心和集群 AI 课程以提升 AI 能力

Box 3: The United Arab Emirates' interdisciplinary approach to K-12 Al curricula案例 3：阿拉伯联合酋长国对 K-12 人工智能课程的跨学科方法

5.3 Framing future-proofing and locally feasible Al domains as carriers of the curriculum5.3 将未来保障和本地可行的人工智能领域框架作为课程的载体

5.4 Tailoring age-appropriate spiral curricular sequences5.4 定制适合年龄的螺旋课程序列

Box 4: The spiral curricular sequence of 'Day of AI' courses案例 4：'人工智能日'课程的螺旋课程序列

5.5 Building enabling learning environments for Al curricula5.5 为人工智能课程构建支持性学习环境

Box 5: Typical enabling learning environment set up by governments' AI curricula案例 5：政府人工智能课程设立的典型支持学习环境

5.6 Promoting the professionalization of AI teachers and streamlining their support5.6 促进 AI 教师的专业化和简化他们的支持。

Box 6: An Al competency framework for Al subject teachers in China案例 6：针对中国人工智能学科教师的人工智能能力框架

5.7 Guiding the cohort-based design and organization of pedagogical activities5.7 指导基于 cohort 的教学活动设计和组织

Box 7: Pedagogical methodologies in the MIT curriculum on the ethics of AI for middle school students案例 7：麻省理工学院针对中学生的人工智能伦理课程中的教学方法

5.8 Constructing competencybased assessments on the progression of key Al aspects5.8 基于能力的评估构建关键人工智能方面的进展

Frame criterion-referenced assessments to measure the mastery of AI competencies构建标准参照评估以衡量 AI 能力的掌握情况

Adapt performance scenarios to assess overt performance and latent competencies调整表现场景以评估显性表现和潜在能力

1. Human-centred mindset:1. 以人为本的思维方式：

Conclusion 结论

References 参考文献

Endnotes 结束语

Unesco 联合国教科文组织

Al competency framework 人工智能能力框架

for students 针对学生

侖领 Unesco 侖领联合国教科文组织

UNESCO - a global leader in education
联合国教科文组织 - 教育领域的全球领导者

The Global Education 2030 Agenda
全球教育 2030 议程

Education
2030 教育 2030

Preparing students to be responsible and creative citizens in the era of AI
准备学生在人工智能时代成为负责任和富有创造力的公民

List of acronyms and abbreviations
缩略语和缩写列表

1.1 Why an Al competency framework for students?
1.1 为什么需要学生的人工智能能力框架？

1.2 Purpose and target audience
1.2 目的和目标受众

Chapter 2: Key principles
第 2 章：关键原则

2.1 Fostering a critical approach to AI
2.1 培养对人工智能的批判性思维

2.2 Prioritizing human-centred interaction with AI
2.2 优先考虑以人为本的人工智能交互

2.3 Encouraging environmentally sustainable AI
2.3 鼓励环境可持续的人工智能

2.4 Promoting inclusivity in AI competency development
2.4 促进人工智能能力发展的包容性

2.5 Building core AI competencies for lifelong learning
2.5 建立终身学习的核心人工智能能力

Chapter 3: Structure of the Al competency
第三章：人工智能能力的结构

Al techniques and applications
人工智能技术和应用

Chapter 4: Specifications of AI competencies
第四章：AI 能力规范

4.1 Level 1: Understand
4.1 级别 1：理解

4.2 Level 2: Apply
4.2 第二级：应用

4.3 Level 3: Create
4.3 级别 3：创建

Chapter 5: Applying the framework
第五章：应用框架

5.1 Aligning Al competencies as the foundation for national Al strategies
5.1 将人工智能能力作为国家人工智能战略的基础

Box 1: Recommendation on the Ethics of Artificial Intelligence
框架 1：关于人工智能伦理的建议

Box 2: Supporting human resource development: The Republic of Korea's National Strategy for Artificial Intelligence
案例 2：支持人力资源发展：韩国的国家人工智能战略

5.2 Building interdisciplinary core and cluster AI curricula for AI competency
5.2 建立跨学科核心和集群 AI 课程以提升 AI 能力

Box 3: The United Arab Emirates' interdisciplinary approach to K-12 Al curricula
案例 3：阿拉伯联合酋长国对 K-12 人工智能课程的跨学科方法

5.3 Framing future-proofing and locally feasible Al domains as carriers of the curriculum
5.3 将未来保障和本地可行的人工智能领域框架作为课程的载体

5.4 Tailoring age-appropriate spiral curricular sequences
5.4 定制适合年龄的螺旋课程序列

Box 4: The spiral curricular sequence of 'Day of AI' courses
案例 4：'人工智能日'课程的螺旋课程序列

5.5 Building enabling learning environments for Al curricula
5.5 为人工智能课程构建支持性学习环境

Box 5: Typical enabling learning environment set up by governments' AI curricula
案例 5：政府人工智能课程设立的典型支持学习环境

5.6 Promoting the professionalization of AI teachers and streamlining their support
5.6 促进 AI 教师的专业化和简化他们的支持。

Box 6: An Al competency framework for Al subject teachers in China
案例 6：针对中国人工智能学科教师的人工智能能力框架

5.7 Guiding the cohort-based design and organization of pedagogical activities
5.7 指导基于 cohort 的教学活动设计和组织

Box 7: Pedagogical methodologies in the MIT curriculum on the ethics of AI for middle school students
案例 7：麻省理工学院针对中学生的人工智能伦理课程中的教学方法

5.8 Constructing competencybased assessments on the progression of key Al aspects
5.8 基于能力的评估构建关键人工智能方面的进展

Frame criterion-referenced assessments to measure the mastery of AI competencies
构建标准参照评估以衡量 AI 能力的掌握情况

Adapt performance scenarios to assess overt performance and latent competencies
调整表现场景以评估显性表现和潜在能力

1. Human-centred mindset:
1. 以人为本的思维方式：