Introduction 导言
The advent of artificial intelligence (AI) has ushered in an era of profound transformation across various sectors, including education. Generative AI is characterized by its capacity to generate content, including texts, audios, pictures, videos, and programming codes [1]. Despite its vast potential, generative AI has not been fully integrated into educational contexts [2], [3], [4], and this lag in integration is especially evident when compared with its rapid adoption in fields, such as healthcare, business operations, and software engineering [5], [6]. This lag is due to the insufficient attention given to the role of teachers in the deployment and orchestration of AI tools [2], [7].
人工智能(AI)的出现为包括教育在内的各行各业带来了一个深刻变革的时代。生成式人工智能的特点是能够生成内容,包括文本、音频、图片、视频和编程代码 [1] 。尽管生成式人工智能具有巨大的潜力,但它尚未完全融入教育环境 [2] 、 [3] 、 [4] ,与它在医疗保健、业务运营和软件工程等领域的快速应用相比,这种融入的滞后尤为明显 [5] 、 [6] 。这种滞后是由于教师在人工智能工具的部署和协调中的作用没有得到足够重视 [2] , [7] 。
The integration of generative AI tools into educational practices requires a robust framework that supports learning and teaching. Existing frameworks, such as those advocated by the United Nations Educational, Scientific and Cultural Organization (UNESCO) [8] and initiatives by the Australian government [9], have laid foundational guidelines for incorporating generative AI in education. However, there is a noticeable gap in the use of these frameworks to implement generative AI practically in K–12 educational settings. Specifically, concrete, classroom-level guidance is needed to empower teachers and facilitate learners in harnessing the full potential of generative AI tools.
将人工智能生成工具融入教育实践需要一个支持学习和教学的强大框架。现有的框架,如联合国教育、科学及文化组织(UNESCO)倡导的框架 [8] 和澳大利亚政府的倡议 [9] ,为将生成式人工智能纳入教育奠定了基础性的指导方针。然而,在利用这些框架在 K-12 教育环境中实际应用生成式人工智能方面还存在明显差距。具体来说,我们需要具体的课堂指导,以增强教师的能力,促进学习者充分发挥生成式人工智能工具的潜力。
In K–12 education settings, generative AI presents opportunities to enrich the educational experience by facilitating self-regulated learning (SRL). SRL is essential for cultivating lifelong learners who can adeptly navigate the evolving challenges of the 21st century [10]. It equips students with the abilities to thoughtfully engage in their academic journey, set goals, strategically approach learning tasks, and reflect critically on their learning experiences. Once integrated into K–12 education, generative AI can engage students in learning by writing interactive prompts to acquire domain-specific knowledge. Generative AI tools (e.g., ChatGPT) can provide tailored learning experiences, and real-time feedback to meet the needs of individual students [4], [7].
在 K-12 教育环境中,生成式人工智能提供了通过促进自我调节学习(SRL)来丰富教育体验的机会。自我调节学习对于培养能够应对 21 世纪不断变化的挑战的终身学习者 [10] 至关重要。它使学生具备深思熟虑地参与学习、设定目标、有策略地完成学习任务以及批判性地反思学习经历的能力。一旦融入 K-12 教育,生成式人工智能就能通过编写互动提示让学生参与学习,从而获得特定领域的知识。生成式人工智能工具(如 ChatGPT)可以提供量身定制的学习体验和实时反馈,以满足不同学生的需求 [4] , [7] 。
Therefore, it is imperative to address the pedagogical needs in K–12 education settings. Increasingly, traditional models of teaching and learning are perceived to inadequately prepare students for the complexities of an AI-infused society [2]. There is a pressing need to propose pedagogical models that can effectively integrate generative AI tools in K–12 settings for the betterment of students to meet the demands of contemporary and future AI-permeated societies [11].
因此,解决 K-12 教育环境中的教学需求势在必行。越来越多的人认为,传统的教学模式不足以让学生为人工智能社会的复杂性做好准备 [2] 。为了让学生更好地适应当代和未来人工智能渗透社会的需求,迫切需要提出能够在 K-12 教育环境中有效整合生成式人工智能工具的教学模式 [11] 。
This article introduces a comprehensive framework for learning and teaching through the integration of generative AI tools into K–12 education. The framework was developed collaboratively by researchers and practicing teachers. The purpose of the framework is to equip students with the necessary skills to excel in an era deeply influenced by AI. In addition, the framework seeks to redefine the roles of both teachers and students. Specifically, teachers assume the roles of facilitator and guide in a learning process that is enriched by generative AI tools. Their students are then encouraged to take ownership of their learning and to engage deeply with problem-solving tasks and domain knowledge acquisition. These interactions between students and generative AI tools are set to occur in a supportive environment, where teachers offer guidance and generative AI tools function as engaging partners [12].
本文介绍了一个通过将生成式人工智能工具整合到 K-12 教育中进行学习和教学的综合框架。该框架由研究人员和实践教师合作开发。该框架的目的是让学生掌握必要的技能,以便在深受人工智能影响的时代脱颖而出。此外,该框架力求重新定义教师和学生的角色。具体来说,教师在人工智能生成工具丰富的学习过程中扮演促进者和引导者的角色。然后,鼓励学生自主学习,深入参与问题解决任务和领域知识的获取。学生与生成式人工智能工具之间的这些互动将在一个支持性环境中进行,教师提供指导,生成式人工智能工具则充当参与伙伴 [12] 。
In this study, the effects of a 60-h teaching professional programme under this framework were also evaluated with respect to in-service teachers’ understanding of how to integrate generative AI into teaching and learning. In addition, the teachers’ perceptions of the domains of content knowledge (CK), technological CK (TCK), pedagogical CK (PCK), and technological, PCK (TPACK) were evaluated, along with their perceived ability to design courses using generative AI tools to support students’ SRL.
本研究还评估了在此框架下开展的 60 小时教学专业课程的效果,以了解在职教师对如何将生成式人工智能融入教学的理解。此外,还评估了教师对内容知识(CK)、技术知识(TCK)、教学知识(PCK)和技术、PCK(TPACK)等领域的看法,以及他们认为利用生成式人工智能工具设计课程以支持学生自学能力的能力。
Literature Review 文献综述
A. Current Frameworks for Generative AI in Education
A.教育领域生成式人工智能的现有框架
In recent years, research and practice have increasingly focused on the integration of generative AI in educational settings [4], [7]. Various frameworks have been developed to guide educators and policymakers in harnessing the potential of generative AI tools.
近年来,研究和实践越来越关注在教育环境中整合生成式人工智能 [4] , [7] 。人们制定了各种框架,以指导教育工作者和决策者利用生成式人工智能工具的潜力。
UNESCO has led the way in creating AI competency frameworks for both teachers and students, with the goal of fostering a thorough understanding of AI's importance in education. Both frameworks designed for teachers and for students emphasize adherence to human rights, the protection of human dignity and privacy, and the reinforcement of human agency [8]. However, the practical application of these frameworks in everyday educational settings remains challenging. The UNESCO guidelines do not provide detailed, actionable instructions that teachers can readily apply within varied classroom environments.
教科文组织率先为教师和学生制定了人工智能能力框架,目的是促进人们全面了解人工智能在教育中的重要性。为教师和学生设计的这两个框架都强调遵守人权、保护人的尊严和隐私,以及加强人的能动性 [8] 。 然而,在日常教育环境中实际应用这些框架仍然具有挑战性。教科文组织的指导方针没有提供详细的、可操作的指示,教师无法在不同的课堂环境中随时应用。
The Australian Framework for Generative Artificial Intelligence in Schools [13] outlines a set of guidelines tailored to the educational context, namely, teaching and learning; human and social well-being; transparency; fairness; accountability; and privacy, security, and safety. Regarding teaching and learning, the framework's holistic approach is commendable as it addresses several aspects:
《澳大利亚学校人工智能生成框架》 [13] 概述了一套针对教育背景的指导方针,即教学;人类和社会福祉;透明度;公平性;问责制;以及隐私、安保和安全。在教学方面,该框架的整体方法值得称赞,因为它涉及多个方面:
the reinforcement of educational practices through impactful integration;
通过有影响力的整合加强教育实践;instructional methods that cultivate a critical understanding of AI;
培养对人工智能的批判性理解的教学方法;teacher expertise to augment rather than replace human teaching;
教师的专业知识是对人类教学的补充,而不是替代;the development of critical thinking skills to foster intellectual growth;
培养批判性思维能力,促进智力发展;learning designs that prioritize students’ needs;
优先考虑学生需求的学习设计;the maintenance of academic integrity to guarantee the ethical application of AI in educational settings.
维护学术诚信,保证人工智能在教育环境中的应用符合道德规范。
However, this framework may lack the specificity needed for practical application, specifically clear-cut guidance on how to implement AI tools in varied classroom situations. The effective integration of AI into curricula and teaching practices will require detailed strategies [14], which are not provided sufficiently by the current framework.
然而,这一框架可能缺乏实际应用所需的具体性,特别是缺乏关于如何在不同课堂情境中实施人工智能工具的明确指导。要将人工智能有效地融入课程和教学实践,就必须制定详细的策略 [14] ,而目前的框架并没有充分提供这些策略。
In addition to these official guidelines, several scholars have proposed frameworks. For example, Su and Yang [15] proposed a theoretical framework, “IDEE,” for generative AI in education. Their framework includes steps such as identifying desired outcomes, determining the level of automation, considering ethical impacts, and assessing effectiveness. Yet, this framework does not offer concrete steps for practical implementation in diverse educational settings. Chan [16] developed an AI education policy for higher education and indicated that students in higher education should take an active role in policy. Furthermore, Kong et al. [17] proposed a 6-P pedagogy, comprising a plan, prompt, preview, produce, peer-review, and portfolio-tracking framework, to guide university students’ academic writing.
除了这些官方指南,还有一些学者提出了一些框架。例如,Su 和 Yang [15] 为教育领域的生成式人工智能提出了一个理论框架 "IDEE"。他们的框架包括确定预期结果、确定自动化程度、考虑伦理影响和评估有效性等步骤。然而,该框架并未提供在不同教育环境中实际实施的具体步骤。Chan [16] 为高等教育制定了一项人工智能教育政策,并指出高等教育中的学生应在政策中发挥积极作用。此外,Kong 等人 [17] 提出了一种 6-P 教学法,包括计划、提示、预览、制作、同行评审和作品集跟踪框架,以指导大学生的学术写作。
These endeavors reveal a gap in research and an essential need for a K–12-specific framework that aligns generative AI's innovative capabilities with the developmental requirements of younger students. Such a framework should offer clear guidance for implementation across different educational environments, with a focus on centering human needs and perspectives.
这些努力揭示了研究方面的差距,也表明我们亟需一个针对 K-12 阶段的框架,将生成式人工智能的创新能力与低年级学生的发展要求结合起来。这种框架应为在不同的教育环境中实施提供明确的指导,重点是以人的需求和视角为中心。
B. Generative AI: Empowering Lifelong Learning
B.生成式人工智能:增强终身学习能力
Generative AI has catalyzed individualized learning [2], [3], [17]. By ensuring that learning experiences are tailored, generative AI provides a foundation for the continuous engagement and skill development that are essential for sustained educational growth throughout one's life [7], [9]. Chiu's [7] research delves into AI's diverse impacts on education, highlighting four pivotal roles of AI that support the development of lifelong learners. The first role involves using generative AI for personalization, such that tasks are customized to each learner's skill level. However, personalization may cause students to become too dependent on generative AI and take a passive approach to learning [18]. In K–12 education, therefore, it is vital to not only implement generative AI tools but also educate students about the foundational AI technologies, including the concepts of tokens, transformer algorithms, self-attention mechanisms, and embeddings. Students must comprehend these concepts to appreciate AI's capabilities and constraints and thus avoid an excessive dependence on technology.
生成式人工智能催化了个性化学习 [2] , [3] , [17] 。通过确保量身定制的学习体验,生成式人工智能为持续参与和技能发展奠定了基础,而持续参与和技能发展对于人一生的持续教育成长至关重要 [7] , [9] 。Chiu 的 [7] 研究深入探讨了人工智能对教育的各种影响,强调了人工智能在支持终身学习者发展方面的四个关键作用。第一个作用是利用生成式人工智能实现个性化,从而根据每个学习者的技能水平定制任务。然而,个性化可能会导致学生过于依赖人工智能生成器,从而采取被动的学习方式 [18] 。因此,在 K-12 教育中,至关重要的是不仅要实施生成式人工智能工具,还要教育学生了解人工智能的基础技术,包括标记、变换器算法、自我关注机制和嵌入等概念。学生必须了解这些概念,才能理解人工智能的能力和限制,从而避免过度依赖技术。
The second role of AI is its ability to enhance human–computer interactions. Michel-Villarreal et al. [19] found that using AI chatbots can improve students’ communication skills [20]. However, it is crucial to supplement human–computer interactions with teacher-led discussions to nurture empathy, emotional intelligence, and understanding, which AI currently cannot provide.
人工智能的第二个作用是增强人机互动的能力。Michel-Villarreal等人 [19] 发现,使用人工智能聊天机器人可以提高学生的交流技能 [20] 。 然而,关键是要通过教师主导的讨论来补充人机互动,以培养同理心、情商和理解力,而这正是人工智能目前无法提供的。
The third role of AI involves providing feedback on students’ work. In K–12 education, immediate feedback can help students to quickly identify areas for improvement and understand complex concepts [21]. Nevertheless, teachers should offer in-depth feedback that encompasses the more qualitative facets of students’ work, such as creativity and critical analysis. In addition, it is important to recognize that the content generated by AI may lack the nuance and depth of human insights [6], [22].
人工智能的第三个作用是为学生的作业提供反馈。在 K-12 教育中,即时反馈可以帮助学生快速确定需要改进的地方并理解复杂的概念 [21] 。不过,教师应提供深入的反馈,包括学生作业中更多的定性方面,如创造性和批判性分析。此外,必须认识到,人工智能生成的内容可能缺乏人类见解的细微差别和深度 [6] , [22] 。
In its fourth role, AI can increase the engagement of students through prompt-based interactions with generative AI [23]. For K–12 students, continued engagement is vital for nurturing a lasting enthusiasm for learning. Thus, teachers should develop AI-assisted learning scenarios that encourage exploration, spark curiosity, and facilitate interactive learning [20], [24]. Teachers should address specific AI mechanisms, such as tokens, transformer algorithms, and embeddings, in their curricula to improve students’ understanding of generative AI.
人工智能的第四个作用是,通过与生成性人工智能 [23] 进行基于提示的互动,提高学生的参与度。对于 K-12 年级的学生来说,持续的参与对于培养持久的学习热情至关重要。因此,教师应开发人工智能辅助学习场景,鼓励探索,激发好奇心,促进互动学习 [20] , [24] 。教师应在课程中涉及具体的人工智能机制,如代币、变换算法和嵌入,以提高学生对生成式人工智能的理解。
C. TPACK Framework and Generative AI in Education
C.TPACK 框架和教育领域的生成式人工智能
The TPACK framework is a vital model for understanding the integration of technology into educational practices [25], [26], [27]. Shulman [25] introduced the concept of PCK, highlighting the importance of teachers’ integrated understanding of both the content they teach and the pedagogical methods best suited for delivering this content. Mishra and Koehler [26] introduced technology knowledge (TK), and proposed that effective teaching in the digital age requires an understanding of how technology can be combined with pedagogical methods and CK. The TPACK framework thus consists of three primary forms of knowledge: PK, CK, and TK. It further includes four hybrid forms of knowledge that emerge at the intersections of the primary forms: PCK, technological pedagogical knowledge, TCK, and TPACK [26, p. 1025].
TPACK 框架是理解将技术融入教育实践的一个重要模型 [25] , [26] , [27] 。舒尔曼 [25] 提出了 PCK 的概念,强调了教师对所教内容和最适合传授这些内容的教学方法的综合理解的重要性。Mishra 和 Koehler [26] 介绍了技术知识(TK),并提出数字时代的有效教学需要了解如何将技术与教学方法和 CK 结合起来。因此,TPACK 框架包括三种主要的知识形式:PK、CK 和 TK。它还包括四种混合形式的知识,这些知识出现在主要形式的交叉点上:PCK、技术教学知识、TCK 和 TPACK [26,第 1025 页]。
The TPACK framework guides teachers in creating and implementing technology-enhanced learning experiences that are content-specific and pedagogically sound [27], [28]. TPACK reflects the dynamic integration of the technology, content, and instruction knowledge domains. It plays a pivotal role in guiding the contextual application of technology in instructional contexts [26] and is instrumental in cultivating teachers’ competency in integrating technology seamlessly into curriculum-specific teaching [27], [29].
TPACK 框架指导教师创建和实施技术强化学习体验,这些体验既针对具体内容,又符合教学规律 [27] , [28] 。TPACK 反映了技术、内容和教学知识领域的动态整合。它在指导技术在教学情境中的应用方面起着举足轻重的作用 [26] ,并有助于培养教师将技术无缝整合到特定课程教学中的能力 [27] , [29] 。
Several recent studies have explored teachers’ acceptance of generative AI in education using the technology acceptance model [30], [31], [32]. However, teacher development programmes in which TPACK is aligned with generative AI are scarce. Our study concentrates on four dimensions of CK: CK, TCK, PCK, and TPACK. CK underpins students’ learning and SRL objectives, TCK explores the affordances of generative AI for SRL, and PCK encompasses lesson planning strategies in which generative AI is integrated to support SRL. TPACK synthesizes all three dimensions into a cohesive strategy for designing student-centered courses that use generative AI to promote SRL in a unit teaching and learning.
最近有几项研究利用技术接受模型 [30] 、 [31] 、 [32] 探讨了教师对教育领域生成式人工智能的接受程度。 然而,将TPACK与生成式人工智能相结合的教师发展计划却很少见。我们的研究集中于四个维度的 CK:CK、TCK、PCK 和 TPACK。CK是学生学习和自学能力目标的基础,TCK探讨了生成式人工智能对自学能力的影响,PCK则包含了将生成式人工智能融入其中以支持自学能力的备课策略。TPACK 将所有三个维度综合成一个有凝聚力的策略,用于设计以学生为中心的课程,在单元教学中使用生成式人工智能促进 SRL。
To address the noted gap in research, the current study was conducted to support a teacher development programme tailored to integrate generative AI with teaching and learning. The attention, relevance, confidence, and satisfaction (ARCS) model of motivational design principles, developed by Keller [33], was adopted to guide the programme. The four components of ARCS are also crucial to the design of teaching and learning activities in using generative AI for engaging students for SRL development, drawing their attention, making the learning activities relevant to their experience, fostering confident development in using generative AI, and ultimately, enabling satisfaction after the learning process.
为了弥补研究方面的不足,本研究开展了一项教师发展计划,旨在支持将生成式人工智能与教学相结合的教师发展计划。该计划采用了凯勒 [33] 提出的动机设计原则--注意力、相关性、信心和满意度(ARCS)模型作为指导。ARCS 的四个要素对于设计使用生成式人工智能的教与学活动也至关重要,它们能吸引学生参与,促进他们的自学能力发展,吸引他们的注意力,使学习活动与他们的经验相关,培养他们使用生成式人工智能的自信心,并最终使他们在学习过程后获得满足感。
D. SRL via Generative AI
D.通过生成式人工智能实现 SRL
Zimmerman's three phases of SRL, namely, forethought, performance, and self-reflection, are essential for guiding students through domain-specific learning processes [10], [34]. The integration of generative AI into a curriculum can enhance each phase of SRL by providing individualized learning materials and immediate feedback that facilitates reflection on learning strategies and outcomes. However, current K–12 educational frameworks lack a systematic approach to integrate TPACK with generative AI and thus foster SRL.
齐默尔曼(Zimmerman)提出的自学学习(SRL)的三个阶段,即前思、表现和自省,对于指导学生完成特定领域的学习过程 [10] , [34] 至关重要。将生成式人工智能整合到课程中,可以通过提供个性化的学习材料和即时反馈,促进对学习策略和结果的反思,从而增强自学能力的每个阶段。然而,目前的 K-12 教育框架缺乏将传统知识包(TPACK)与生成式人工智能相结合,从而促进 SRL 的系统方法。
In the forethought phase of SRL, generative AI can be used to analyze students’ existing knowledge and learning preferences, enabling the provision of tailored learning goals and resources [35], [36]. This individualized learning aids in establishing intrinsic motivation and strategic planning [19].
在 SRL 的前瞻阶段,生成式人工智能可用于分析学生的现有知识和学习偏好,从而提供量身定制的学习目标和资源 [35] , [36] 。这种个性化学习有助于建立内在动力和战略规划 [19] 。
During the performance phase, generative AI can provide immediate feedback to empower students to monitor their understanding and quickly adjust their learning strategies [14]. For example, writing prompts can facilitate dialogue with generative AI. However, young learners are still developing their metacognitive and cognitive abilities [37], [38]; accordingly, they may experience frustration or confusion when working with AI. Thus, teachers play a vital role in providing emotional support and motivation to help students to overcome challenges [39].
在表现阶段,生成式人工智能可以提供即时反馈,使学生能够监控自己的理解并迅速调整学习策略 [14] 。例如,写作提示可以促进与生成式人工智能的对话。然而,青少年学生的元认知和认知能力仍处于发展阶段 [37] , [38] ;因此,他们在使用人工智能时可能会遇到挫折或困惑。因此,教师在提供情感支持和激励以帮助学生克服困难方面起着至关重要的作用 [39] 。
In the self-reflection phase, students engage in self-evaluation to assess their own performance and reflect on their interactions with generative AI, enabling them to better plan for subsequent learning tasks [40]. This reflection is a key step toward consolidating learning and preparing for the future.
在自我反思阶段,学生进行自我评价,评估自己的表现,并反思自己与生成式人工智能的互动,从而更好地规划后续学习任务 [40] 。这种反思是巩固学习成果和为未来做好准备的关键一步。
However, the absence of an integrated TPACK and generative AI framework for SRL in K–12 education has left teachers without a clear strategy to harness AI's full potential in promoting SRL. A dedicated framework could guide teachers in effectively combining their pedagogical expertise and CK with generative AI to foster and support SRL. Such a framework would ensure a human-centered pedagogical approach and equip students with the necessary skills and mindset for lifelong learning.
然而,在 K-12 教育中,由于缺乏针对 SRL 的综合 TPACK 和生成式人工智能框架,教师们没有明确的策略来利用人工智能的全部潜力来促进 SRL。一个专门的框架可以指导教师有效地将他们的教学专业知识和 CK 与生成性人工智能结合起来,以促进和支持 SRL。这样一个框架将确保以人为本的教学方法,并使学生具备终身学习所需的技能和心态。
Against this background, a human-centered learning and teaching framework (HCLTF) was proposed in this study. This framework uses generative AI for SRL development through domain knowledge learning in K–12 settings. In addition, a 10-week teacher development programme was introduced to equip in-service primary teachers with conceptual knowledge about generative AI and the skills necessary to use generative AI tools for creating differentiated instructional materials and fostering students’ individualized learning.
在此背景下,本研究提出了一个以人为本的学习与教学框架(HCLTF)。该框架利用生成式人工智能,在 K-12 环境中通过领域知识学习促进自学能力的发展。此外,本研究还引入了一个为期10周的教师发展计划,以帮助在职小学教师掌握生成式人工智能的概念知识,以及使用生成式人工智能工具创建差异化教学材料和促进学生个性化学习所需的技能。
The programme includes an introduction to generative AI (e.g., concepts of generative AI, tokens, self-attention mechanisms, transformer, supervised and unsupervised learning, and reinforcement learning) and the application of generative AI to course design. The TPACK and ARCS models were integrated to guide the design of the programme. The proposed HCLTF was used to guide the pedagogical design of diverse case studies on subjects, such as Chinese language, English language, mathematics, general studies, and programming. The impact of the teacher professional development programme was examined by addressing the following three questions.
该课程包括对生成式人工智能的介绍(如生成式人工智能的概念、代币、自我注意机制、转换器、监督和非监督学习以及强化学习)以及生成式人工智能在课程设计中的应用。TPACK 模型和 ARCS 模型相结合,为课程设计提供指导。拟议的 HCLTF 用于指导不同学科(如中文、英文、数学、通识教育和编程)案例研究的教学设计。教师专业发展计划的影响通过以下三个问题进行了研究。
To what extent did the teacher development programme improve teachers’ comprehension of generative AI concepts?
教师发展计划在多大程度上提高了教师对生成式人工智能概念的理解?To what extent did the teacher development programme enhance teachers’ perceptions of the domains of CK, TCK, PCK, and TPACK?
教师发展计划在多大程度上提高了教师对知识、能力和社区知识、个人成长知识和知识包领域的认识?To what extent did the teacher development programme enhance teachers’ perceived ability to design courses that would increase students’ attention, relevance, confidence, and satisfaction?
教师发展计划在多大程度上提高了教师设计课程的能力,从而提高学生的注意力、相关性、自信心和满意度?
Proposed Framework 拟议框架
A. Introduction to the Proposed Framework
A.拟议框架简介
Fig. 1 depicts the proposed HCLTF. The framework is visually articulated as a Venn diagram composed of three overlapping circles representing the domains of learning, teaching, and generative AI. The following section explains the three domains and the three areas where they intersect.
图 1 描述了拟议的 HCLTF。该框架被形象地表述为一个维恩图,由三个重叠的圆圈组成,分别代表学习、教学和生成式人工智能领域。下文将解释这三个领域以及它们相互交叉的三个方面。
Learning: The learning domain, guided by human-centric principles, positions students at the core of the educational experience. It accentuates the facilitation of the three stages of SRL. In this domain, the goal is to empower students to take ownership of their learning process through setting goals, monitoring learning processes, and reflecting on their learning outcomes.
学习:学习领域遵循以人为本的原则,将学生定位为教育体验的核心。它强调促进自学学习的三个阶段。在这一领域,目标是通过设定目标、监控学习过程和反思学习成果,让学生掌握自己的学习过程。Teaching: The teaching domain is dedicated to pedagogical approaches that seamlessly integrate generative AI into educational settings. This domain involves guiding teachers to integrate generative AI into their teaching strategies, ensuring that the technology is aligned with pedagogical goals and enhances content delivery. Within this framework, the ARCS model is used to assist teachers in contemplating strategies to enhance students’ motivation during the learning process.
教学:教学领域致力于将生成式人工智能无缝融入教育环境的教学方法。这一领域涉及指导教师将人工智能生成技术融入教学策略,确保技术与教学目标相一致,并增强内容的传授。在这一框架内,ARCS 模型被用来帮助教师思考在学习过程中提高学生学习积极性的策略。Generative AI: The generative AI domain is focused on providing technological affordances and immediate feedback. In this context, generative AI is a tool that can be used to offer individualized feedback to students.
生成式人工智能:生成式人工智能领域侧重于提供技术能力和即时反馈。在这种情况下,生成式人工智能是一种可用于向学生提供个性化反馈的工具。Learning–Teaching: At the nexus of the learning and teaching domains, the focus shifts to teachers’ guidance and facilitation roles. Teachers are instrumental in steering students through the SRL process by providing affective and social support to foster a supportive learning environment [41].
学与教:在学与教领域的结合点上,重点转向教师的指导和促进作用。教师通过提供情感和社会支持,营造有利的学习环境,在引导学生完成自学学习过程中发挥着重要作用 [41] 。Learning–Generative AI: At the intersection of the learning and generative AI domains, neuroscience-informed educational implications are used to enhance attention, engagement, error-feedback, and reflection and thus support students’ SRL [42]. Accordingly, students are encouraged to actively find and correct errors in the content generated by AI.
学习-生成式人工智能:在学习和生成式人工智能领域的交叉点上,神经科学的教育意义被用来提高注意力、参与度、错误反馈和反思,从而支持学生的自学能力 [42] 。因此,我们鼓励学生在人工智能生成的内容中主动发现并纠正错误。Central Zone—Learning, Teaching, and Generative AI: The central zone, where all three domains converge, symbolizes the unified essence of teaching and learning. This should be a synergistic space, where the framework domains are combined to create a cohesive and dynamic learning environment that supports SRL and prepares students for the demands of the 21st century.
中心区--学习、教学和生成性人工智能:中心区是所有三个领域的交汇处,象征着教与学的统一本质。这应该是一个协同增效的空间,在这里,各框架领域相互结合,创造出一个具有凝聚力和活力的学习环境,以支持自学能力的培养,并使学生为满足 21 世纪的需求做好准备。
B. Case Study: Generative AI Tools as Partners for Enhancing Primary Students’ Chinese Writing
B.案例研究:生成式人工智能工具作为提高小学生中文写作水平的合作伙伴
This case study demonstrates the application of the HCLTF for designing learning and teaching activities in using generative AI (e.g., ChatGPT) for supporting Grade 5 students’ Chinese writing in Hong Kong, in which students are guided by their teachers to partner with ChatGPT across a five-stage lesson plan comprising topic preparation, spoken to written language revision, structure refinement, word refinement, and reflection and review (see Fig. 2).
本案例研究展示了应用 HCLTF 设计学与教活动,使用生成式人工智能(如 ChatGPT)来支持香港五年级学生的中文写作,其中学生在教师的指导下与 ChatGPT 合作,完成了包括主题准备、口语到书面语修改、结构完善、词语完善和反思与回顾五个阶段的课程计划(见图 2 )。
Stage 1—Topic preparation: In this stage, the teacher assigns essay topics and clearly explains the expectations. Students engage in goal-setting and planning their essays. In this stage, generative AI has not yet been introduced.
第 1 阶段--题目准备:在这一阶段,教师布置作文题目,并明确说明对学生的要求。学生参与目标设定和作文规划。在这一阶段,尚未引入生成式人工智能。Stage 2—Spoken to written language revision: The teacher facilitates discussions and guides students in the process of brainstorming ideas orally [43]. Students create drafts of their essays by verbally articulating their thoughts and converting them to text using tools, such as Google Docs, which support speech-to-text functionality. The students then refine their spoken language drafts to align with written language standards by removing colloquialisms and replacing them with more formal language and adding correct punctuation. Next, the students use ChatGPT to revise their spoken language drafts into a more formal written language (Version 1). They learn to interact with the AI interface through prompts, compare their work with the AI's output, and resolve the differences. A comparison table is created to contrast the student version with the generative AI version 1 (v1) and thus provide immediate feedback on the outcomes. Throughout this stage, teachers’ scaffolding provides crucial support.
第二阶段--从口头到书面的语言修正: [43] 教师主持讨论,引导学生集思广益。学生通过口头表达自己的想法,并使用支持语音到文本功能的工具(如 Google Docs)将其转换为文本,从而创作出作文草稿。然后,学生通过删除口语化用语,代之以更正式的语言,并添加正确的标点符号,完善口语草稿,使其符合书面语言标准。接下来,学生使用 ChatGPT 将口语草稿修改为更正式的书面语言(版本 1)。他们学会通过提示与人工智能界面进行交互,将自己的作品与人工智能的输出进行比较,并解决其中的差异。创建一个对照表,将学生版本与生成式人工智能版本 1(v1)进行对比,从而对结果提供即时反馈。在整个阶段中,教师的支架提供了重要的支持。Stage 3—Structure refinement: At the beginning of this stage, the teacher introduces structural knowledge and organizes further discussions to deepen the students’ understanding of essay structure. The students apply this knowledge to enhance the organization of their essays, considering elements, such as introductions, transitions, and conclusions. After an initial student-led revision, generative AI tools are used to further refine the structure (v2). The students are encouraged to critically and comparatively evaluate the structure proposed by ChatGPT against their own essay, and teacher-led discussions are used to consolidate the learning points from prior revisions.
第三阶段--结构完善:在这一阶段的开始,教师介绍结构知识并组织进一步讨论,以加深学生对作文结构的理解。学生运用这些知识来加强文章的组织,考虑导语、过渡句和结论等要素。在学生主导的初步修改后,使用生成式人工智能工具进一步完善结构(v2)。我们鼓励学生根据自己的文章对 ChatGPT 提出的结构进行批判性的比较评估,并通过教师引导的讨论来巩固之前修改中的学习要点。Stage 4—Word refinement: This stage focuses on linguistic precision. The teacher continues to provide guidance and to facilitate the refinement of language and word choice. The students focus on enhancing the phrases and terminology used in their essays. Subsequently, they can ask ChatGPT to integrate advanced rhetorical devices, such as metaphors or personification, into their writing. Finally, they compare their revisions with ChatGPT's suggestions (v3) to determine the most effective expression.
第 4 阶段--词语精炼:这一阶段的重点是语言的准确性。教师继续提供指导,促进语言和用词的精炼。学生将重点放在加强作文中使用的短语和术语上。随后,他们可以要求 ChatGPT 将比喻或拟人等高级修辞手法融入他们的写作中。最后,他们将自己的修改意见与 ChatGPT 的建议(v3)进行比较,以确定最有效的表达方式。Stage 5—Reflection and review: In this stage, the teacher facilitates a discussion to enable reflection, comments on the students’ essays (v4), highlights areas of strength, and suggests improvements. The students summarize their learning experiences and the knowledge and skills gained throughout the cycles of interaction with ChatGPT.
第五阶段--反思与回顾:在这一阶段,教师主持讨论以促进反思,对学生的论文(v4)进行评论,强调优势领域并提出改进建议。学生总结他们的学习经验以及在与 ChatGPT 互动的整个周期中所获得的知识和技能。
In the proposed framework, generative AI is used as a tool to support and enhance the writing process, from initial topic preparation to the final reflection. In each stage, learners are encouraged to actively engage with domain knowledge. As a result, the learners continuously refine their understanding. This iterative process of learning and application is fundamental for the development of SRL competencies.
在拟议的框架中,生成式人工智能被用作支持和加强写作过程的工具,从最初的题目准备到最后的反思。在每个阶段,我们都鼓励学习者积极参与领域知识的学习。因此,学习者会不断完善自己的理解。这种学习和应用的迭代过程是培养自学能力的基础。
Research Design 研究设计
This study is part of a larger project that aims to empower in-service K–12 teachers by integrating generative AI into course designs. In this mixed-methods study, both quantitative and qualitative data analyses were adopted.
本研究是一个大型项目的一部分,该项目旨在通过将生成式人工智能融入课程设计,增强 K-12 在职教师的能力。在这项混合方法研究中,采用了定量和定性数据分析。
A. Participants A.与会者
The study recruited a cohort of 31 in-service primary school teachers. A purposeful sampling technique was used. During the recruitment process, the researchers contacted the principals of several local primary schools and extended invitations to teachers who expressed interest in participating in the teacher development programme.
研究招募了 31 名在职小学教师。研究采用了有目的的抽样技术。在招募过程中,研究人员与当地几所小学的校长取得了联系,并向表示有兴趣参加教师发展计划的教师发出了邀请。
The gender distribution of the participants was nearly equal, with 16 female and 15 male teachers. Regarding educational attainment, most of the participants held a bachelor's degree (n = 21); the others possessed a master's degree (n = 10).
参与者的性别分布基本均衡,其中女教师 16 人,男教师 15 人。在学历方面,大多数参与者拥有学士学位(21 人),其他参与者拥有硕士学位(10 人)。
The participating teachers were responsible for teaching two or more subjects. Fig. 3 presents the subject distribution among the participants: computer science was most commonly taught, followed by general studies, mathematics, and languages.
参赛教师负责教授两个或两个以上的学科。图 3 显示了参与者的学科分布情况:教授计算机科学的最多,其次是通识教育、数学和语文。
B. Procedure B.程序
The teachers were invited to participate in a six-week programme that included: 1) a 30-h course intended to a foundational understanding of AI (e.g., what AI is, the five steps of machine learning, supervised learning, and unsupervised learning) and deep learning (e.g., data cleaning, data augmentation, neural networks, computer vision, convolution neural networks, and recurrent neural networks), and generative AI concepts and 2) a 30-h course on integrating AI into course design in areas, such as Chinese language, English language, mathematics, general studies, computer science, and music education.
教师们应邀参加了为期六周的课程,其中包括1) 30 小时的课程,旨在对人工智能(例如,什么是人工智能、机器学习的五个步骤、监督学习和无监督学习)和深度学习(例如,数据清理、数据增强、神经网络、计算机视觉、卷积神经网络和递归神经网络)以及生成式人工智能概念进行基础性了解;以及 2) 30 小时的课程,旨在将人工智能融入中文、英文、数学、通识教育、计算机科学和音乐教育等领域的课程设计中。
Throughout the programme, the teachers completed a series of assessments to gauge their progress and the efficacy of training. These assessments included pr-etest and post-test to measure their understanding of AI concepts and evaluate their knowledge of TPACK before and after the course and their self-perceived competency in designing courses, specifically in terms of increasing students’ attention, relevance, confidence, and satisfaction.
在整个课程期间,教师们完成了一系列评估,以衡量他们的进步和培训效果。这些评估包括前测和后测,以衡量他们对人工智能概念的理解,评估他们在课程前后对 TPACK 的了解,以及他们在设计课程方面的自我认知能力,特别是在提高学生的注意力、相关性、自信心和满意度方面。
In the final week of the programme, the teachers were required to write a self-reflective piece on their experiences and the insights gained from learning about the use of generative AI tools in education.
在课程的最后一周,教师们需要撰写一篇自我反思文章,介绍他们从学习在教育中使用生成式人工智能工具中获得的经验和启示。
C. Data Collection and Analysis
C.数据收集与分析
1) Data Collection 1) 数据收集
The data sources included AI concept tests, questionnaires, and reflective writing.
数据来源包括人工智能概念测试、问卷调查和反思性写作。
a) Pre-AI and post-AI concept tests
a) 人工智能前和人工智能后的概念测试
The test consists of ten multiple-choice items. It was designed by authors to assess teachers’ understanding of tokens, self-attention, embeddings, transformer, prompting engineering, other basic AI concepts, and the implications of generative AI. The Cronbach's alpha value for the test exceeds 0.6, indicating a moderate level of consistency in the measurement of conceptual understanding of AI [46]. An example item from the test is provided below.
测试由十个选择题组成。作者设计该测试的目的是评估教师对标记、自我关注、嵌入、转换器、提示工程、其他人工智能基本概念以及生成式人工智能含义的理解。该测试的 Cronbach's alpha 值超过 0.6,表明在测量对人工智能概念的理解 [46] 方面具有中等程度的一致性。下面是测试的一个示例项目。
Which of the following is correct about tokens in the context of large language models?
关于大型语言模型中的标记,以下哪项是正确的?
Tokens are the smallest units of text that a large language model can process.
代币是大型语言模型可以处理的最小文本单位。The number of tokens in a large language model directly corresponds to the number of words.
大型语言模型中的词块数量与单词数量直接对应。Tokens can represent words, parts of words, or punctuation.
标记可以代表单词、单词的一部分或标点符号。(1) and (2) only 仅(1)和(2)
(1) and (3) only 仅(1)和(3)
(2) and (3) only 仅(2)和(3)
All of the above. 以上皆是。
b) Pre-survey and post-survey on TPACK
b) 关于 TPACK 的事前调查和事后调查
This survey was designed to assess changes in the teachers’ self-evaluated knowledge in the TPACK domain. Specifically, it aims to measure changes in the teachers’ perceived abilities to use text-based generative AI tools for differentiated instructions and to address learner differences. This instrument was adapted from previously validated instruments [27], [28]; the responses are scored on a five-point Likert scale, with options ranging from 1 (strongly disagree) to 5 (strongly agree). The survey encompasses 14 items across four constructs: CK, with three items focusing on prompt engineering knowledge (e.g., “I have various ways and strategies of refining prompts when using generative AI tools.”); PCK, with four items focusing on addressing individual differences using generative AI (e.g., “I can guide my students to use generative AI tools through the problem-solving process.”); TCK, with four items focusing on understanding the technological aspects of generative AI (e.g., “I understand the importance of transformer architecture in determining the effectiveness of generative AI tools.”); and TPACK, with three items focusing on the integration of teaching and technology (e.g., “I can teach lessons that appropriately integrate prompt engineering, generative AI tools, and teaching approaches.”). The reliability of the survey is substantiated by the Cronbach's alpha values for the four constructs of the pretest and posttest, which range from 0.75 to 0.92. These values demonstrate a high level of internal consistency, indicating that the survey items consistently reflect the constructs they are intended to measure [46].
本调查旨在评估教师对 TPACK 领域知识的自我评价变化。具体来说,它旨在测量教师在使用基于文本的生成式人工智能工具进行差异化指导和解决学习者差异方面的感知能力变化。该工具改编自以前经过验证的工具 [27] 、 [28] ;回答采用五点李克特量表,选项从 1(非常不同意)到 5(非常同意)不等。调查包括四个方面的 14 个项目:CK,其中三个项目侧重于提示工程知识(例如,"在使用生成式人工智能工具时,我有各种完善提示的方法和策略");PCK,其中四个项目侧重于使用生成式人工智能解决个体差异问题(例如,"在解决问题的过程中,我可以指导学生使用生成式人工智能工具");TCK,其中四个项目侧重于了解生成式人工智能的技术方面(例如,"我了解变压器的重要性")、"我了解变压器结构在决定生成式人工智能工具有效性方面的重要性。");以及 TPACK,其中三个项目侧重于教学与技术的整合(例如,"我能在教学中适当整合提示工程、生成式人工智能工具和教学方法。")。前测和后测四个构面的 Cronbach's alpha 值从 0.75 到 0.92 不等,证明了调查的可靠性。这些值显示了较高的内部一致性,表明调查项目始终如一地反映了它们所要测量的建构 [46] 。
c) Pre-survey and post-survey on assessing teachers’ ability to use text-based generative AI tools for teaching from the perspective of ARCS
c) 从 ARCS 的角度评估教师在教学中使用文本生成式人工智能工具的能力的前调查和后调查
This survey was developed to measure the changes in teachers’ perceived abilities to create teaching materials and guide students to increase their attention, relevance, confidence, and satisfaction. This instrument, which was adapted from previous studies [44], [45], uses a five-point Likert scale for responses, with options ranging from 1 (strongly disagree) to 5 (strongly agree). It encompasses 12 items across four constructs: attention, with three items assessing teachers’ perceived ability to design engaging learning materials using generative AI tools (e.g., “I can use generative AI tools to design teaching materials and activities to sustain students’ interest.”); relevance, with three items assessing the creation of learning experiences that are personally meaningful to students (e.g., “I can use generative AI tools to create authentic scenario-based learning activities to allow students to relate the learning content to their own life experiences.”); confidence, with three items on helping learners to believe and feel that they will succeed and to improve their confidence in using generative AI (e.g., “I can guide students to use generative AI tools to seek multiple answers and thus enhance their confidence through multi-perspective learning.”); and satisfaction, with three items on guiding students to use generative AI tools to seek multiple answers to enhance their satisfaction through multi-perspective learning (e.g., “I can guide students to use generative AI tools to solve problems and develop their independent thinking, allowing them to experience a sense of achievement in their learning.”). The Cronbach's alpha values for the four constructs of the pretest and posttest exceed 0.85, indicating good internal consistency [46].
本调查旨在测量教师在编写教材和引导学生提高注意力、相关性、自信心和满意度方面的感知能力变化。本调查表改编自先前的研究 [44] , [45] ,采用五点李克特量表进行回答,选项范围从 1(非常不同意)到 5(非常同意)。它包括四个方面的 12 个项目:注意力,其中三个项目评估教师使用生成式人工智能工具设计引人入胜的学习材料的能力(例如,"我可以使用生成式人工智能工具设计教学材料和活动,以保持学生的兴趣");相关性,其中三个项目评估创造对学生个人有意义的学习体验的能力(例如,"我可以使用生成式人工智能工具设计教学材料和活动,以保持学生的兴趣");创造性,其中三个项目评估创造对学生个人有意义的学习体验的能力(例如,"我可以使用生成式人工智能工具设计教学材料和活动,以保持学生的兴趣")、"我能使用生成式人工智能工具创建真实的情景式学习活动,让学生将学习内容与自己的生活经历联系起来。");信心,有三个项目是帮助学习者相信并感觉到他们会成功,并提高他们使用生成式人工智能的信心(例如,"我能指导学生使用生成式人工智能工具,让他们感觉到自己会成功、"我可以引导学生使用生成式人工智能工具寻求多种答案,从而通过多角度学习增强他们的自信心。");以及满意度,其中三个项目涉及引导学生使用生成式人工智能工具寻求多种答案,从而通过多角度学习增强他们的满意度(例如,"我可以引导学生使用生成式人工智能工具解决问题,培养他们的独立思考能力,让他们体验到学习的成就感。")。前测和后测四个构念的 Cronbach's alpha 值均超过 0.85,表明内部一致性良好 [46] 。
d) Reflective writing d) 反思性写作
The teachers were asked to provide written reflections in either English or Chinese throughout framework implementation in the programme. They documented the challenges encountered, the strategies used, and their overall impressions of the usefulness of the generative AI tools for enhancing students’ SRL in practice.
在整个框架实施过程中,教师们被要求用英文或中文提供书面反思。他们记录了在实践中遇到的挑战、使用的策略以及对生成式人工智能工具在提高学生自学能力方面的作用的总体印象。
2) Data Analysis 2) 数据分析
This study employed a mixed-methods approach, integrating both quantitative and qualitative analyses to comprehensively evaluate the effectiveness of the teacher development programme. The Shapiro–Wilk test was applied to assess the normality of the data pertaining to the first and third research questions. The results indicated that the data were normally distributed (p > 0.05). Consequently, the paired sample t-test was applied to analyses of these data, as this test is well suited for comparing the means of two related groups when the data are normally distributed. Furthermore, the self-reflective writings were analyzed to triangulate the quantitative data. The second research question focused on the effect of the programme on teachers’ TPACK. The Shapiro–Wilk test was applied to the data and revealed a nonnormal distribution. Thus, the paired-sample Wilcoxon test was used to evaluate the effect of the programme on teachers’ CK, PCK, TCK, and TPACK. This nonparametric alternative test was selected as the most appropriate method to evaluate the data, as it does not assume a normal distribution and is robust when applied to ordinal data or skewed distributions.
本研究采用了混合方法,综合了定量和定性分析,以全面评估教师发展计划的成效。采用 Shapiro-Wilk 检验法评估了与第一个和第三个研究问题有关的数据的正态性。结果表明,数据呈正态分布(p > 0.05)。因此,在分析这些数据时采用了配对样本 t 检验,因为当数据呈正态分布时,这种检验非常适合比较两个相关群体的平均值。此外,还对自我反思文章进行了分析,以便对定量数据进行三角测量。第二个研究问题的重点是该计划对教师专题知识包的影响。对数据进行了 Shapiro-Wilk 检验,结果显示数据呈非正态分布。因此,采用了配对样本 Wilcoxon 检验来评估该计划对教师的 CK、PCK、TCK 和 TPACK 的影响。选择这种非参数检验作为评估数据的最合适方法,是因为它不假定正态分布,而且在应用于序数数据或偏态分布时也很稳健。
Results 成果
A. Effect of the Teacher Development Programme on Concept Test Scores
A.教师发展计划对概念测试成绩的影响
The mean differences between the preconcept and postconcept tests. The assumption of normality was not violated (Shapiro–Wilk test, p > 0.05). The pretest score (M = 3.581, SD = 1.205) was lower than the posttest (M = 4.871, SD = 1.648; paired-samples t-test), and this difference was significant, t(30) = 3.616, p < 0.001, 95% confidence interval (CI) [0.562, 2.019]. The results show that the teacher development programme had a positive effect on the teachers’ understanding of the AI concepts.
概念前和概念后测试的平均差异。未违反正态性假设(Shapiro-Wilk 检验,P > 0.05)。前测得分(M = 3.581,SD = 1.205)低于后测得分(M = 4.871,SD = 1.648;配对样本 t 检验),且差异显著,t(30) = 3.616,p < 0.001,95% 置信区间 (CI) [0.562, 2.019]。结果表明,教师发展计划对教师理解人工智能概念产生了积极影响。
B. Effect of the Teacher Development Programme on TPACK Evaluation Scores
B.教师发展计划对 TPACK 评估得分的影响
Table I shows the descriptive statistics of teachers' perceptions of TPACK. A Wilcoxon signed-rank test showed that the teacher development programme led to statistically significant increases in the teachers’ CK (Z = –4.562, p < 0.001), PCK (Z = –4.632, p < 0.001), TCK (Z = –4.396, p < 0.001), and TPACK (Z = –4.561, p < 0.001).
表 I 显示了教师对专题知识包看法的描述性统计。Wilcoxon 符号秩检验表明,教师发展计划使教师的 CK(Z = -4.562,p < 0.001)、PCK(Z = -4.632,p < 0.001)、TCK(Z = -4.396,p < 0.001)和 TPACK(Z = -4.561,p < 0.001)都有了显著提高。
These results reveal that the teacher development programme was highly effective in enhancing teachers’ knowledge and skills across the CK, PCK, TCK, and TPACK domains. The statistically significant improvements in the survey scores suggest that the programme increased not only the teachers’ theoretical understanding of the integration of technology with pedagogy and content but also their ability to apply this knowledge in practice.
这些结果表明,教师发展计划在提高教师的知识和技能(CK、PCK、TCK 和 TPACK)方面非常有效。调查得分在统计上的显著提高表明,该计划不仅提高了教师对技术与教学法和内容整合的理论理解,还提高了他们在实践中应用这些知识的能力。
To triangulate the quantitative data, the teachers’ self-reflective writings were analyzed, and some examples were selected.
为了对定量数据进行三角测量,我们对教师的自我反思文章进行了分析,并选取了一些例子。
CK
The course provides information on AI, which can be passed on to students, and emphasises the importance of understanding AI in education. (T1)
该课程提供有关人工智能的信息,可将这些信息传递给学生,并强调在教育中了解人工智能的重要性。(T1)This course has really opened my eyes to the role generative AI can play in our classrooms. It is not just another buzzword; it is a tool we can use to really make a difference in how we teach and how our students learn. I will pass on what I have learnt about AI to my students and help them understand its growing importance in our world. (T17)
这门课程让我真正看到了生成式人工智能在课堂上可以发挥的作用。它不仅仅是另一个流行词,而是我们可以用来真正改变我们的教学方式和学生的学习方式的工具。我将把我所学到的人工智能知识传授给我的学生,帮助他们了解人工智能在我们这个世界上日益增长的重要性。(T17)PCK
There is a need to deepen teachers’ understanding of AI to facilitate the birth and transformation of pedagogy. I learnt a lot from this course. (T23)
有必要加深教师对人工智能的理解,以促进教学法的诞生和变革。我从这门课程中学到了很多。(T23)After taking this programme, I see the power AI has to reshape [how] we teach. I have gained so much insight into how AI can help tailor our teaching methods to each unique student. The framework we have been introduced to is very practical. It underscores the importance of keeping our students at the heart of their learning journey. By harnessing the power of AI, we can create a learning environment that not only recognises but also celebrates each student's individual needs and potential. (T16)
参加这个课程后,我看到了人工智能重塑我们教学方式的力量。我对人工智能如何帮助我们针对每个独特的学生量身定制教学方法有了更深入的了解。向我们介绍的框架非常实用。它强调了让学生在学习过程中处于核心地位的重要性。通过利用人工智能的力量,我们可以创造一个不仅承认而且赞美每个学生的个人需求和潜力的学习环境。(T16)TCK
Words like “tokens” and “transformers” were foreign to me, and I was worried that machines might take our jobs. But this course showed me that is not the case. (T14)
代币 "和 "变压器 "这样的词对我来说很陌生,我担心机器会抢走我们的工作。但这门课程告诉我,事实并非如此。(T14)
TPACK
Integrating AI into our teaching is not just about understanding the technology or the content. I have learnt that using AI tools effectively requires a careful balance. This course has given me the confidence to strategically use AI in my lesson plans. (T29)
将人工智能融入我们的教学,不仅仅是要了解技术或内容。我了解到,有效使用人工智能工具需要谨慎平衡。这门课程让我有信心在教学计划中战略性地使用人工智能。(T29)AI can give me insights into where a student might be struggling, allowing me to intervene with the right kind of support at the right time. The TPACK framework reminds me that technology is a tool to facilitate this journey, not the journey itself. My role is to use that tool to build a bridge between students’ current abilities and their potential, guiding them towards becoming lifelong, self-motivated learners. (T15)
人工智能可以让我深入了解学生可能在哪些方面遇到困难,让我能够在适当的时候提供适当的支持。TPACK 框架提醒我,技术是促进这一旅程的工具,而不是旅程本身。我的职责是利用这一工具,在学生的现有能力和潜能之间架起一座桥梁,引导他们成为终身的、自我激励的学习者。(T15)
C. Effect of the Teacher Development Programme on Perceived Ability to Teach With Generative AI Under the ARCS
C.教师发展计划对在 ARCS 下使用生成式人工智能教学的认知能力的影响
Statistically significant mean differences in attention, relevance, confidence, and satisfaction between the presurvey and postsurvey were assessed (paired samples t-test). The assumption of normality was not violated (Shapiro–Wilk's test, p > 0.05). Table II tabulates the descriptive data on teachers’ perceived ability to use generative AI to design courses that can increase students’ attention, relevance, confidence, and satisfaction.
对调查前和调查后在注意力、相关性、信心和满意度方面的平均差异进行了统计评估(配对样本 t 检验)。没有违反正态性假设(Shapiro-Wilk 检验,p > 0.05)。表 II 列出了教师对使用生成式人工智能设计课程以提高学生注意力、相关性、自信心和满意度的感知能力的描述性数据。
For attention, a statistically significant increase was observed from the presurvey (M = 3.151, SD = 0.950) to the postsurvey (M = 4.366, SD = 0.598), t(df) = 7.762, p < 0.001. The effect size was large (Cohen's d = 0.87). For relevance, the participants reported a significant improvement from the presurvey (M = 3.000, SD = 0.878) to post-survey (M = 4.226, SD = 0.669), t(df) = 8.161, p < 0.001. The effect size was large (Cohen's d = 0.84). For confidence, there was a significant increase from the presurvey (M = 3.108, SD = 0.900) to the post-survey (M = 4.333, SD = 0.644), t(df) = 7.652, p < 0.001. The effect size was large (Cohen's d = 0.89). For satisfaction, there was a significant increase from the presurvey (M = 3.290, SD = 0.811) to the post-survey (M = 4.366, SD = 0.663), t(df) = 6.966, p < 0.001. The effect size was large (Cohen's d = 0.86).
在注意力方面,从调查前(M = 3.151,SD = 0.950)到调查后(M = 4.366,SD = 0.598),观察到了统计学上的显著增长,t(df) = 7.762,p < 0.001。效应大小很大(Cohen's d = 0.87)。在相关性方面,参与者报告说,从调查前(M = 3.000,SD = 0.878)到调查后(M = 4.226,SD = 0.669),他们的相关性有了显著提高,t(df)= 8.161,p < 0.001。效应大小较大(Cohen's d = 0.84)。在自信心方面,从调查前(M = 3.108,SD = 0.900)到调查后(M = 4.333,SD = 0.644),自信心明显增加,t(df)= 7.652,p < 0.001。效应大小较大(Cohen's d = 0.89)。在满意度方面,从调查前(M = 3.290,SD = 0.811)到调查后(M = 4.366,SD = 0.663),满意度有了显著提高,t(df)= 6.966,p < 0.001。效应大小较大(Cohen's d = 0.86)。
To add depth to the quantitative data, the teachers’ self-reflective narratives were carefully reviewed.
为了增加定量数据的深度,我们仔细审查了教师的自我反思叙述。
Attention 请注意
The training transformed my lesson kick-offs. I now use targeted questions that tap into students’ curiosity, leading to a noticeable boost in their participation from the start. (T16)
培训改变了我的开课方式。现在,我使用有针对性的问题来激发学生的好奇心,使他们从一开始就明显提高了参与度。(T16)After completing the programme, I have noticed a marked change in the way I approach the start of my lessons. This shift became evident when I observed my students more eagerly participating in discussions and activities right from the beginning of class. Generative AI can give me many amazing ideas. (T24)
课程结束后,我发现自己上课的方式发生了明显的变化。当我观察到我的学生从课堂一开始就更热衷于参与讨论和活动时,这种转变就显而易见了。生成式人工智能能给我带来很多奇妙的想法。(T24)Relevance 相关性
I am always striving to make my lessons relevant, but the programme gave me a new perspective on how to integrate real-world problems into my curriculum. By using AI to brainstorm real-time scenarios, I am able to bring context to theoretical concepts. (T29)
我一直在努力使我的课程具有相关性,但该计划给了我一个新的视角,让我知道如何将现实世界的问题融入我的课程。通过使用人工智能对实时场景进行头脑风暴,我能够为理论概念提供背景。(T29)
Confidence 信心
After the first day of this programme, I encouraged students to converse with generative AI and critically evaluate its responses. They were developing critical thinking skills by identifying areas where AI's responses can be improved. (T8)
第一天课程结束后,我鼓励学生与生成式人工智能对话,并对其反应进行批判性评估。他们通过找出人工智能反应可以改进的地方,培养了批判性思维能力。(T8)
Satisfaction 满意度
Integrating generative AI into my daily teaching practices has brought a new level of joy as I watch students take ownership of their learning. The changes I have implemented in the course, inspired by what I've learnt from this teacher development programme, have been met with enthusiastic feedback from students, affirming the value of these innovative tools in enhancing their educational experience. (T23)
将生成式人工智能融入我的日常教学实践,给我带来了新的快乐,因为我看到学生们掌握了学习的主动权。受教师发展项目的启发,我在课程中实施了一些变革,并得到了学生们的热烈反馈,他们肯定了这些创新工具在提升教育体验方面的价值。(T23)
Discussion 讨论
In this study, a framework was proposed to integrate generative AI tools into the HCLTF to guide in-service teachers’ practices. The results of the study show that teacher development programme significantly increased the in-service teachers’ conceptual understanding of generative AI, improved their pedagogical strategies, and enhanced their perceived ability to integrate the AI tools into curriculum design. These findings align with the emerging consensus on the importance of providing professional training for in-service teachers in the age of generative AI in previous research [31], [48], [49].
本研究提出了一个框架,将生成式人工智能工具整合到 "高中课程与教学法 "中,以指导在职教师的实践。研究结果表明,教师发展计划显著提高了在职教师对生成式人工智能的概念理解,改善了他们的教学策略,并增强了他们将人工智能工具融入课程设计的感知能力。这些研究结果与之前的研究 [31] , [48] , [49] 中关于在生成式人工智能时代为在职教师提供专业培训的重要性的新共识相一致。
First, by learning the theoretical underpinnings and practical uses of generative AI, teachers gain deeper insights into the principles and the limitations of large language models. For instance, the concept of embeddings, which are the high-dimensional vector spaces where lexical items are mapped to capture semantic significance, is pivotal to the operational capacity of generative AI models to process natural language. Thus, ChatGPT demonstrates proficiency within the domain of linguistic tasks, but its ability to handle abstract reasoning tasks, such as mathematical problem-solving, is constrained. In addition, techniques, such as few shots, enable teachers to produce a variety of similar questions. This capability can be particularly useful when constructing assessments.
首先,通过学习生成式人工智能的理论基础和实际应用,教师可以深入了解大型语言模型的原理和局限性。例如,嵌入(embeddings)的概念,即词汇项目映射到其中以捕捉语义意义的高维向量空间,对于生成式人工智能模型处理自然语言的操作能力至关重要。因此,ChatGPT 在语言任务领域表现出了很强的能力,但在处理抽象推理任务(如数学问题解决)方面却受到了限制。此外,"少量拍摄 "等技术还能让教师提出各种类似的问题。这种能力在构建评估时特别有用。
Second, the programme helped the teachers to shift from traditional educational paradigms to new models that use AI for differentiated instruction. This enabled them to become facilitators of knowledge and of students’ SRL [50].
其次,该计划帮助教师从传统的教育模式转向利用人工智能进行差异化教学的新模式。这使他们成为知识和学生自学能力的促进者 [50] 。
Finally, the teachers noted an improved capacity to design courses that can enhance students’ attention, relevance, confidence, and satisfaction, suggesting that the teacher development programme equipped them with strategies and tools to make learning more engaging and meaningful for their students. By incorporating generative AI into their teaching, teachers can enhance students’ motivation, which in turn can foster SRL.
最后,教师们指出,他们设计课程的能力得到了提高,可以增强学生的注意力、相关性、自信心和满意度,这表明教师发展计划为他们提供了策略和工具,让学生学得更投入、更有意义。通过将生成式人工智能融入教学,教师可以提高学生的学习积极性,进而促进自学能力的提高。
The results of the study contribute to the ongoing dialogue between researchers and in-service teachers on educational innovation. The proposed HCLTF can guide future K–12 practitioners. The HCLTF, which was inspired by TPACK, provides an integrated understanding of how generative AI, pedagogy, and content can interact to create student-centered learning experiences.
研究结果有助于研究人员与在职教师就教育创新问题进行持续对话。拟议的 HCLTF 可以为未来的 K-12 从业人员提供指导。HCLTF 受到 TPACK 的启发,提供了对生成式人工智能、教学法和内容如何相互作用以创造以学生为中心的学习体验的综合理解。
The findings of this study have three implications for research and practice in the area of integrating generative AI into K–12 education settings.
本研究的结果对将生成式人工智能融入 K-12 教育领域的研究和实践有三方面的启示。
First, the results of this study can inform researchers and designers about the need for practical and actionable frameworks to guide the integration of generative AI into teaching practices [15], [47]. This framework contributes to the ongoing dialogue on educational innovation and highlights the importance of developing adaptable models that can guide K–12 practitioners in the future [49], [51].
首先,本研究的结果可以让研究人员和设计人员了解到,有必要制定切实可行的框架,以指导将生成式人工智能融入教学实践 [15] , [47] 。本框架有助于正在进行的教育创新对话,并强调了开发可适应的模型的重要性,这些模型可在未来指导 K-12 从业人员 [49] , [51] 。
Second, this study underscores the importance of human-centered principles when integrating generative AI into the classroom [9], [13]. The results suggest caution against an over-reliance on AI technologies and support the teacher's role as a social and emotional anchor in the learning process. This human-centered approach ensures that the essential human aspects of learning and teaching, such as empathy, ethical considerations, and social interactions, are maintained and strengthened.
其次,本研究强调了在将生成式人工智能融入课堂 [9] , [13] 时,以人为本原则的重要性。研究结果表明,要警惕对人工智能技术的过度依赖,并支持教师在学习过程中扮演社会和情感支柱的角色。这种以人为本的方法可确保学习和教学中人的基本方面(如同理心、道德考虑和社会互动)得到保持和加强。
Finally, for curriculum developers and EdTech stakeholders, this study highlights the potential of generative AI when it is integrated thoughtfully and ethically into K–12 education settings. The results suggest an opportunity to design curricula that use AI not only to assist teachers in creating differentiated instruction but also to cultivate students’ SRL capabilities. Furthermore, EdTech stakeholders are encouraged to collaborate with teachers to create tools that align with the HCLTF, thus ensuring that technology supports educational goals without overshadowing the human elements of teaching and learning.
最后,对于课程开发人员和教育技术相关人员来说,本研究强调了生成性人工智能的潜力,即当人工智能被深思熟虑、合乎道德地整合到 K-12 教育环境中时。研究结果表明,我们有机会设计课程,利用人工智能不仅协助教师创建差异化教学,而且培养学生的自学能力。此外,我们鼓励教育技术相关人员与教师合作,创建符合 HCLTF 的工具,从而确保技术在支持教育目标的同时,不会掩盖教学中的人为因素。
Conclusion 结论
Currently, a transformative shift is occurring in education: generative AI tools and enhanced AI literacy are empowering educators to refine their pedagogical strategies. The results of this study contribute to the ongoing dialogue between researchers and in-service teachers on educational innovation. The proposed HCLTF can guide future K–12 practitioners. The findings of this study call for continued research and dialogue among researchers, practitioners, curriculum developers, and EdTech stakeholders to ensure that generative AI is incorporated in a way that enhances learning while remaining grounded in human-centered educational values.
目前,教育领域正在发生变革性的转变:生成式人工智能工具和增强的人工智能素养使教育工作者有能力完善他们的教学策略。本研究的结果有助于研究人员与在职教师就教育创新问题进行持续对话。拟议的 HCLTF 可以为未来的 K-12 从业人员提供指导。本研究的结果呼吁研究人员、从业人员、课程开发人员和教育技术利益相关者继续开展研究和对话,以确保生成式人工智能的融入方式既能促进学习,又能保持以人为本的教育价值观。
The study also has three notable limitations. First, although the HCLTF was co-designed by researchers and in-service teachers and has been applied to the design of courses in Chinese language, English language, mathematics, general studies, computer science, and music education, its applicability to other subjects and the effects on students remain unknown. As such, the framework may not be fully representative of or adaptable to the diverse range of scenarios encountered in primary and secondary education settings. Further iterations and validation across a variety of educational environments and disciplines are necessary to ensure the HCLTF's robustness and generalizability.
这项研究还有三个明显的局限性。首先,尽管 "HCLTF "由研究人员和在职教师共同设计,并已应用于中文、英文、数学、通识教育、计算机科学和音乐教育等课程的设计,但其对其他学科的适用性以及对学生的影响仍是未知数。因此,该框架可能无法完全代表或适应中小学教育环境中遇到的各种情况。为了确保 HCLTF 的稳健性和通用性,有必要在各种教育环境和学科中进行进一步的迭代和验证。
Second, the participants in this study were limited to in-service primary teachers. A more diverse group of teachers with different levels of AI experience and expertise in various subjects will be invited to participate in future studies. In the future, research should consider how school culture influences the adoption and effectiveness of AI in teaching. A three-level analysis could be adopted to assess the dynamics between teacher–student interactions, teacher–teacher interactions, and the overall school environment. By expanding the participant pool and examining additional factors, future research may provide a generalizable understanding of the proposed HCLTF.
其次,本研究的参与者仅限于在职小学教师。在今后的研究中,将邀请具有不同程度人工智能经验和不同学科专业知识的更多样化的教师群体参与。今后的研究应考虑学校文化如何影响人工智能在教学中的应用和效果。可以采用三层次分析法来评估师生互动、师师互动和学校整体环境之间的动态关系。通过扩大参与者库和研究更多因素,未来的研究可能会对拟议的 HCLTF 提供可推广的理解。
Finally, the study primarily focused on the perspective of teachers, with less emphasis on the student experience. Future research should include students’ voices to understand how the integration of generative AI affects their learning experience, engagement, and outcomes [16]. Students’ feedback should be used to iterate and improve the framework for integrating AI into learning and teaching practices in K–12 settings.
最后,本研究主要侧重于教师的视角,而较少关注学生的体验。未来的研究应包括学生的声音,以了解生成式人工智能的整合如何影响他们的学习体验、参与度和成果 [16] 。应利用学生的反馈意见,不断改进和完善将人工智能融入 K-12 环境中的学习和教学实践的框架。