Computers & Education最新文献_第7页

A generative artificial intelligence-enhanced multiagent approach to empowering collaborative problem solving across different learning domains 生成人工智能增强的多智能体方法，支持跨不同学习领域的协作问题解决

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-02-01 Epub Date: 2025-10-19 DOI: 10.1016/j.compedu.2025.105489

Lanqin Zheng, Zhe Shi, Lei Gao

Collaborative problem-solving skills are among the most important skills in the 21st century. However, learners exhibit significant deficiencies in terms of their collaborative problem-solving skills. Emerging artificial intelligence (AI) technologies have given rise to transformative opportunities to facilitate collaborative problem solving through the introduction of adaptive learning mechanisms in educational settings. This study proposes a generative artificial intelligence (GenAI)-enhanced multiagent approach that aims to promote collaborative problem solving across different learning domains. The study also examines the effectiveness of this GenAI-enhanced multiagent approach to collaborative problem solving. In total, 234 college students participated in two empirical studies that focused on different tasks but had the same purpose and procedure. Experimental group 1 engaged in collaborative problem-solving with the assistance of a GenAI-enhanced multiagent approach. Experimental group 2 engaged in collaborative problem solving via a chatbot-based approach. The control group engaged in traditional collaborative problem-solving without any support. Data were collected through the pretest, posttest, and collaborative problem-solving process records and interview records. Both quantitative and qualitative methods were employed to analyze the data. The results indicated that compared with chatbot-based and traditional approaches, the GenAI-enhanced multiagent approach had more significant effects on learning achievements, knowledge elaboration, and collaborative problem-solving performance and skills. The implications of these findings are discussed in depth with the goal of advancing the use of GenAI to empower collaborative problem solving.

协作解决问题的能力是21世纪最重要的技能之一。然而，学习者在协作解决问题的能力方面表现出明显的不足。新兴的人工智能（AI）技术带来了变革性的机会，通过在教育环境中引入自适应学习机制，促进协作解决问题。本研究提出了一种生成人工智能（GenAI）增强的多智能体方法，旨在促进跨不同学习领域的协作问题解决。该研究还检验了这种genai增强的多智能体协作解决问题方法的有效性。共有234名大学生参与了两项实证研究，这两项研究的重点不同，但目的和程序相同。实验组1在genai增强型多智能体方法的帮助下进行协作解决问题。实验2组通过基于聊天机器人的方法进行协作解决问题。控制组在没有任何支持的情况下进行传统的协作解决问题。通过前测、后测、协作解决问题过程记录和访谈记录收集数据。采用定量和定性相结合的方法对数据进行分析。结果表明，与基于聊天机器人和传统方法相比，genai增强的多智能体方法在学习成绩、知识阐述、协作解决问题的表现和技能方面具有更显著的效果。这些发现的含义进行了深入的讨论，目标是推进GenAI的使用，以增强协作解决问题的能力。

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引用次数: 0

Adaptive vs. planned metacognitive scaffolding for computational thinking: Evidence from generative AI-supported programming in elementary education 计算思维的自适应与计划元认知支架：来自基础教育中生成式人工智能支持的编程的证据

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-02-01 Epub Date: 2025-10-02 DOI: 10.1016/j.compedu.2025.105473

Jinfang Liu , Yi Zhang , Wei Li , Qiyun Wang , Pingxiu Niu , Xue Zhang

Metacognitive scaffolding plays a pivotal role in supporting the development of computational thinking (CT) among elementary students. Compared to planned scaffolding, adaptive scaffolding supported by generative AI (GAI) agents offers a promising option by providing real–time, adaptive and personalized response. This study investigated how adaptive metacognitive scaffolding (AMS) and planned metacognitive scaffolding (PMS) influenced elementary students’ CT in a GAI–supported programming environment. 74 students were assigned to AMS, PMS, or control conditions. Using a mixed-methods approach, we examined CT task performance, CT skill use, metacognitive regulation, and cognitive load through standardized assessments, programming artifacts, discourse logs, video-coded behaviors, classroom observation records, cognitive load ratings. Results showed that AMS significantly enhanced contextualized CT performance and promoted more complex, diverse skill use compared to PMS and control groups. AMS also fostered more recursive metacognitive behaviors and reduced mental efforts, while PMS supported higher meaningful engagement. These findings highlight the potential of adaptive scaffolding powered by GAI to support CT development in elementary programming education.

元认知支架在小学生计算思维发展中起着举足轻重的作用。与计划脚手架相比，由生成式人工智能（GAI）代理支持的自适应脚手架通过提供实时、自适应和个性化的响应，提供了一个很有前途的选择。本研究探讨了在人工智能支持的编程环境下，适应性元认知支架（AMS）和计划性元认知支架（PMS）对小学生CT的影响。74名学生被分配到AMS， PMS或对照条件。采用混合方法，我们通过标准化评估、编程工件、话语日志、视频编码行为、课堂观察记录、认知负荷评级，检查了CT任务表现、CT技能使用、元认知调节和认知负荷。结果显示，与PMS和对照组相比，AMS显著提高了情境化CT表现，促进了更复杂、更多样化的技能使用。AMS还培养了更多的递归元认知行为，减少了心理努力，而PMS支持更高的有意义的投入。这些发现强调了由GAI驱动的自适应脚手架在支持初级编程教育中的CT开发方面的潜力。

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引用次数: 0

Ex-Edchat: Historic retrospective of X/Twitter #Edchat Ex-Edchat: X/Twitter #Edchat的历史回顾

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-02-01 Epub Date: 2025-10-17 DOI: 10.1016/j.compedu.2025.105476

K. Bret Staudt Willet , Jeffrey P. Carpenter , Hunhui Na

For more than a decade, education-related X/Twitter hashtags facilitated networking and resource-sharing among educators with related interests and needs, resulting in self-reported impacts on practice. #Edchat was one of the first such hashtags and attracted substantial attention as an affinity space for educators. This study retrospectively explores long-term and large-scale digital trace X/Twitter data associated with #Edchat from October 2008 to May 2023, analyzing more than 15 million tweets in terms of changes in volume (e.g., daily tweets) and content (e.g., questions, replies, hyperlinks, co-occurring hashtags, language features). Findings suggest that #Edchat’s initial success led to impressive growth, followed by change in the nature of content and a long period of steady decline. Specific social dynamics associated with the hashtag, such as the decline of its associated synchronous chat, as well as technical factors (e.g., platform updates, policy changes) appear to have influenced #Edchat’s volume and content. Quantifying the shifting nature of this long-standing affinity space contributes to understanding the opportunities and challenges educators may encounter on social media broadly and highlights the importance of supporting and developing educators’ digital literacy.

十多年来，与教育相关的X/Twitter标签促进了具有相关兴趣和需求的教育工作者之间的网络和资源共享，从而对实践产生了自我报告的影响。#Edchat是第一个这样的标签之一，作为教育工作者的亲密空间吸引了大量关注。本研究回顾性地研究了2008年10月至2023年5月期间与#Edchat相关的长期大规模数字跟踪X/Twitter数据，分析了超过1500万条推文的数量变化（如每日推文）和内容变化（如问题、回复、超链接、共同出现的标签、语言特征）。研究结果表明，#Edchat最初的成功带来了令人印象深刻的增长，随后内容性质发生了变化，并经历了长时间的稳步下滑。与该标签相关的特定社会动态，如与之相关的同步聊天的衰落，以及技术因素（如平台更新、政策变化）似乎影响了#Edchat的数量和内容。量化这一长期存在的亲和力空间的变化性质有助于理解教育工作者在社交媒体上可能遇到的机遇和挑战，并强调支持和发展教育工作者数字素养的重要性。

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引用次数: 0

Incremental project-based robot programming: Effects on young children's computational thinking, executive function, and learning behavioral patterns 基于增量项目的机器人编程：对幼儿计算思维、执行功能和学习行为模式的影响

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-02-01 Epub Date: 2025-10-03 DOI: 10.1016/j.compedu.2025.105471

Yuchen Chen , Xinli Zhang , Lailin Hu , Gwo-Jen Hwang , Haoran Xie , Yun-Fang Tu

Robot programming is an age-appropriate means to cultivate children's computational thinking (CT) and executive function (EF). Project-based learning (PBL) is commonly applied to teach robot programming. PBL involves complex, large-scale projects consisting of multiple sub-projects. Each sub-project introduces new knowledge but offers few opportunities to connect it with previously learned knowledge, thus misaligning with children's cognitive development. In comparison, incremental PBL (IPBL) is a tailored design of PBL that begins with basic knowledge, with each new sub-project building on all learned knowledge from previous sub-projects and progressively adding new knowledge to achieve complete project learning. Hence, this research integrated IPBL into robot programming to develop an incremental project-based robot programming (I-PBRP) approach, and evaluated its effect on young children's CT, EF, and learning behavioral patterns. Ninety-five children aged 5–6 engaged in 12-week interventions. They were randomly assigned to the I-PBRP group and the conventional project-based robot programming (C-PBRP) group. Results manifested that the I-PBRP group achieved better performance than the C-PBRP group in CT and in the three components of EF (inhibition, working memory, and cognitive flexibility) over time. The progressive behavioral analysis manifested that the I-PBRP approach promoted superior performance in robot programming activities and more positive learning behaviors for children. This research has implications for robot programming teaching approaches for young children's CT and EF development.

机器人编程是培养儿童计算思维（CT）和执行功能（EF）的一种适龄手段。基于项目的学习（PBL）是机器人编程教学的常用方法。PBL涉及由多个子项目组成的复杂的大型项目。每个子项目都引入了新知识，但很少有机会将其与以前所学的知识联系起来，从而与儿童的认知发展不一致。相比之下，增量式PBL （IPBL）是一种定制化的PBL设计，从基础知识开始，每个新的子项目都建立在以前子项目的所有知识基础上，并逐步增加新的知识，以实现完整的项目学习。因此，本研究将IPBL整合到机器人编程中，开发了一种基于增量项目的机器人编程（I-PBRP）方法，并评估了其对幼儿CT、EF和学习行为模式的影响。95名5-6岁的儿童参与了为期12周的干预。他们被随机分配到I-PBRP组和传统的基于项目的机器人编程（C-PBRP）组。结果表明，随着时间的推移，I-PBRP组在CT和EF的三个组成部分（抑制、工作记忆和认知灵活性）方面的表现优于C-PBRP组。渐进式行为分析表明，I-PBRP方法促进了儿童在机器人编程活动中的优异表现和更积极的学习行为。本研究对幼儿CT和EF发展的机器人编程教学方法具有启示意义。

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引用次数: 0

Mapping student-AI interaction dynamics in multi-agent learning environments: Supporting personalized learning and reducing performance gaps 在多智能体学习环境中映射学生与人工智能交互动态：支持个性化学习并减少绩效差距

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-02-01 Epub Date: 2025-10-08 DOI: 10.1016/j.compedu.2025.105472

Zhanxin Hao , Jie Cao , Ruimiao Li , Jifan Yu , Zhiyuan Liu , Yu Zhang

Multi-agent AI systems, which simulate diverse instructional roles such as teachers and peers, offer new possibilities for personalized and interactive learning. Yet, the specific patterns of student-AI interaction and their pedagogical implications remain unclear. This study explores how university students engaged with multiple AI agents during a six-module course, and how these interactions influenced cognitive outcomes (learning gains) and non-cognitive factors (motivation, technology acceptance). Through the analysis of dialogue data, two core engagement patterns were identified: co-construction of knowledge and co-regulation. Students with lower prior knowledge relied more on co-construction of knowledge sequences and showed higher learning gains and post-course motivation. In contrast, students with higher prior knowledge engaged more in co-regulation behaviors but demonstrated limited learning improvement. Technology acceptance increased across all groups. These findings suggest that multi-agent systems can effectively support differentiated engagement and help reduce performance gaps by adapting to students' varying needs. This study makes three innovative contributions to existing research: it is based on a long-term formal curriculum, moving beyond fragmented, short-term interactions; it investigates a multi-agent collaborative mechanism that simulates diverse pedagogical roles (e.g., AI teacher, AI teaching assistance, AI classmates), distinguishing this work from single-agent systems; and it explores differentiated interaction modes based on prior knowledge, providing critical teaching implications for personalized learning.

多智能体人工智能系统模拟了教师和同伴等不同的教学角色，为个性化和互动学习提供了新的可能性。然而，学生与人工智能互动的具体模式及其教学意义仍不清楚。本研究探讨了大学生在六模块课程中如何与多个人工智能代理互动，以及这些互动如何影响认知结果（学习收益）和非认知因素（动机、技术接受度）。通过对对话数据的分析，确定了两种核心参与模式：共同构建知识和共同调节。先验知识水平低的学生更依赖于知识序列的共同构建，表现出更高的学习收益和课后动机。相比之下，具有较高先验知识的学生参与了更多的共同调节行为，但表现出有限的学习进步。所有群体对技术的接受程度都有所提高。这些发现表明，多智能体系统可以有效地支持差异化参与，并通过适应学生的不同需求来帮助缩小绩效差距。这项研究对现有研究做出了三个创新贡献：它基于长期的正式课程，超越了碎片化的短期互动；它研究了一种多智能体协作机制，该机制模拟了不同的教学角色（例如，人工智能教师、人工智能教学辅助、人工智能同学），将这项工作与单智能体系统区分开来；它探索了基于先验知识的差异化互动模式，为个性化学习提供了重要的教学启示。

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引用次数: 0

Unpacking self-regulation and social interaction in “Study With Me” videos through large-scale analytics 通过大规模分析分析“与我一起学习”视频中的自我调节和社会互动

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-02-01 Epub Date: 2025-10-17 DOI: 10.1016/j.compedu.2025.105488

Tongxi Liu , Liping Deng , Yujie Zhou

As social media platforms are increasingly used to facilitate informal learning, “Study With Me” (SWM) videos have garnered substantial popularity. Despite their widespread use, empirical research on these videos remains in its infancy. This study investigates the characteristics of SWM videos, providing a comprehensive understanding of their affordances for self-regulated learning and social interaction. Specifically, advanced machine learning techniques were applied to analyze 393 SWM videos and 164,611 associated comments on YouTube. A modified topic modeling approach identified emerging themes and patterns in the comment data, while sentiment analysis assessed emotional tone and examined how specific video features influenced users' self-regulation. The analysis revealed that comments primarily focused on SWM video features, self-regulation, and social interaction. Positive sentiment appeared in about half of the comments, praising elements such as ambient music and visual aesthetics for enhancing emotional engagement and motivation. Various features of SWM videos, such as lighting, music, and in-video text, support learners’ self-regulation across motivational, emotional, and social dimensions. This study highlights the potential of social media as a versatile educational tool and encourages stakeholders to leverage such platforms to expand and enrich learning opportunities.

随着社交媒体平台越来越多地用于促进非正式学习，“与我一起学习”（SWM）视频已经获得了相当大的人气。尽管这些视频被广泛使用，但对它们的实证研究仍处于起步阶段。本研究调查了SWM视频的特点，全面了解了它们对自我调节学习和社会互动的启示。具体来说，先进的机器学习技术被应用于分析YouTube上的393个SWM视频和164,611个相关评论。一种改进的话题建模方法确定了评论数据中的新兴主题和模式，而情绪分析评估了情感基调，并检查了特定视频功能如何影响用户的自我调节。分析显示，评论主要集中在SWM视频功能、自我调节和社会互动上。大约一半的评论中出现了积极的情绪，称赞环境音乐和视觉美学等元素可以增强情感投入和动力。SWM视频的各种功能，如灯光、音乐和视频文本，支持学习者在动机、情感和社会维度上的自我调节。这项研究强调了社交媒体作为一种多功能教育工具的潜力，并鼓励利益相关者利用这些平台来扩大和丰富学习机会。

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引用次数: 0

Self-regulation plus individual interests? A design-based study on the development of a GenAI-empowered platform for self-directed out-of-class reading 自律加个人利益？基于设计的genai自主课外阅读平台开发研究

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-02-01 Epub Date: 2025-10-06 DOI: 10.1016/j.compedu.2025.105474

Mengru Pan , Chun Lai , Kai Guo , Jing Huang

Self-directed out-of-class reading contributes to language and general learning, yet many learners struggle with it and need support. Generative Artificial Intelligence (GenAI) holds the potential to provide personalized support that can facilitate self-directed out-of-class reading. This study adopted a design-based research (DBR) methodology to unravel the effective design of GenAI-empowered self-directed out-of-class reading by comparing different ways of integrating a ChatGPT chatbot into an online reading platform in supporting the provision of reading texts and delivering self-regulated learning (SRL) support. The DBR study included three cycles: a conventional approach in Cycle 0 (i.e., a preset pool of reading materials plus human-delivered SRL training), a chatbot-empowered personalized SRL platform in Cycle 1 (i.e., GenAI-recommended reading materials and chatbot-supported personalized SRL training), and a chatbot-empowered interest-based reading with personalized SRL platform in Cycle 2 (i.e., GenAI-recommended interest-based reading materials and chatbot-supported personalized SRL training). This DBR study lasted three semesters, involving one hundred and sixteen English as a foreign language (EFL) students from three classes at a university in China. Qualitative data from open-ended surveys and interviews after each cycle were used to refine the platform design in the subsequent cycle. Quantitative data, including pre- and post-surveys on self-regulated reading strategy use and self-directed reading, along with log data, were analyzed to compare the effects of the platform design on students' self-regulated strategy use and self-directed reading across three cycles. The results indicated that the chatbot-empowered interest-based reading with personalized SRL training showed the greatest potential in enhancing students’ self-regulated reading strategy use and self-directed reading. This study contributes to the existing literature by elucidating the design principles that enhance self-directed out-of-class reading.

自主课外阅读有助于语言和一般的学习，但许多学习者挣扎，需要支持。生成式人工智能（GenAI）具有提供个性化支持的潜力，可以促进自主课外阅读。本研究采用基于设计的研究（DBR）方法，通过比较将ChatGPT聊天机器人集成到在线阅读平台以支持提供阅读文本和提供自我调节学习（SRL）支持的不同方式，揭示了genai授权的自主课外阅读的有效设计。DBR研究包括三个周期：周期0的常规方法（即预设的阅读材料池加上人工交付的SRL训练），周期1的聊天机器人支持的个性化SRL平台（即genai推荐的阅读材料和聊天机器人支持的个性化SRL训练），以及周期2的聊天机器人支持的基于兴趣的阅读和个性化SRL平台（即genai推荐的基于兴趣的阅读材料和聊天机器人支持的个性化SRL训练）。这项DBR研究持续了三个学期，涉及来自中国一所大学三个班的116名英语作为外语（EFL）学生。每个周期后的开放式调查和访谈的定性数据用于改进后续周期的平台设计。定量数据包括自我调节阅读策略使用和自主阅读的前后调查，以及日志数据，分析了平台设计对学生三个周期的自我调节策略使用和自主阅读的影响。结果表明，基于聊天机器人的兴趣阅读与个性化SRL训练在提高学生自主阅读策略使用和自主阅读方面表现出最大的潜力。本研究通过阐明增强自主课外阅读的设计原则，对现有文献有所贡献。

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引用次数: 0

Comparing cognitive load during video versus traditional classroom instruction based on heart rate variability measures 基于心率变异性测量比较视频和传统课堂教学中的认知负荷

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-02-01 Epub Date: 2025-10-16 DOI: 10.1016/j.compedu.2025.105487

Enqi Fan , Matt Bower , Jens Siemon

This pilot study used heart rate variability (HRV) as an indicator of cognitive load to examine student engagement in the learning process. We investigated the dynamics of students' (N = 45, paired sample) cognitive load in classes with and without video tutorials and compared differences in cognitive load between development phases of lessons where students are acquiring new knowledge. The results of the study show that the average cognitive load of students is higher when using video tutorials than in classrooms without them. From the students' behavior, we can see that when using video tutorials, students frequently adjust their viewing strategies or take notes. In classrooms without videos, students are more easily distracted. This means that students mobilized more cognitive resources for effective learning while using video tutorials. In general, our results suggest that the use of video tutorials in the development phase of classroom can increase student effectiveness in learning new knowledge. This study provides new insights into the application of video tutorials as a form of computer-assisted instruction, highlighting the potential benefits of using dynamic cognitive load monitoring in real classroom environments.

这项初步研究使用心率变异性（HRV）作为认知负荷的指标来检查学生在学习过程中的参与度。我们调查了学生（N = 45，配对样本）在有和没有视频教程的课堂上的认知负荷动态，并比较了学生在学习新知识的课程发展阶段之间的认知负荷差异。研究结果表明，使用视频教程的学生的平均认知负荷高于没有视频教程的教室。从学生的行为可以看出，在使用视频教程时，学生经常调整观看策略或做笔记。在没有视频的教室里，学生更容易分心。这意味着学生在使用视频教程时调动了更多的认知资源来进行有效的学习。总的来说，我们的研究结果表明，在课堂开发阶段使用视频教程可以提高学生学习新知识的有效性。本研究为视频教程作为一种计算机辅助教学形式的应用提供了新的见解，强调了在真实课堂环境中使用动态认知负荷监测的潜在好处。

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引用次数: 0

Co-constructing adaptive lesson plans with GenAI: Pre-service teachers' Intelligent-TPACK and prompt engineering strategies 与GenAI共同建构适应性教案：职前教师的智能tpack与即时工程策略

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-02-01 Epub Date: 2025-10-13 DOI: 10.1016/j.compedu.2025.105485

Ismail Celik , Sini Kontkanen , Jari Laru , Alanur Ahsen Dalyanci

Generative Artificial Intelligence (GenAI) technologies present new opportunities for teachers to design adaptive and student-centered instruction. However, the educational value of GenAI depends not only on technical usage but also on teachers' ability to formulate pedagogically meaningful prompts. Prompting strategies are not isolated from teachers′ prior knowledge and skills. Less is known about how pre-service teachers′ AI-related knowledge influences prompt engineering strategies, in turn leading to meaningful adaptive lesson plans. Considering this gap, we design an instructional task for pre-service teachers to generate adaptive lesson plans with the help of GenAI. Prior to the task, we collected data about their AI-related skills, namely AI literacy and Intelligent-TPACK. The prompts were qualitatively analyzed based on the phases of the Knowledge Construction (KC) Framework. Then, we explored the pedagogical value of adaptive lesson plans through a rubric in terms of three indicators: student agency, adaptive strategies, and flexible tools. PLS-SEM analysis revealed that as long as pre-service teachers have AI-specific technological and pedagogical knowledge, they formulate higher phases of prompts based on the KC framework. Our analysis showed that prompts from higher phases generated more adaptive lesson plans in terms of student agency, adaptive strategies, and flexible tools. We also found an indirect effect of Intelligent-TPK on adaptive lesson plans. This study highlights that effective prompt engineering is a pedagogical act shaped by teachers’ knowledge, not merely a technical command. It also underscores the importance of embedding AI-specific pedagogical training in teacher education. By conceptualizing prompts as epistemic moves, we offer new insights into how teachers and GenAI can collaborate to produce responsive and inclusive learning experiences.

生成式人工智能（GenAI）技术为教师设计适应性强、以学生为中心的教学提供了新的机会。然而，GenAI的教育价值不仅取决于技术使用，还取决于教师制定有教学意义的提示的能力。提示策略并非孤立于教师的先验知识和技能之外。对于职前教师的人工智能相关知识如何影响提示工程策略，进而导致有意义的适应性课程计划，人们知之甚少。考虑到这一差距，我们为职前教师设计了一个教学任务，让他们在GenAI的帮助下生成适应性课程计划。在任务之前，我们收集了他们的人工智能相关技能数据，即人工智能素养和智能tpack。根据知识构建（KC）框架的阶段对提示进行定性分析。然后，我们通过三个指标来探讨适应性教案的教学价值：学生代理、适应性策略和灵活的工具。PLS-SEM分析显示，只要职前教师具有人工智能特定的技术和教学知识，他们就会根据KC框架制定更高阶段的提示。我们的分析表明，在学生代理、适应性策略和灵活的工具方面，来自较高阶段的提示产生了更具适应性的课程计划。我们还发现了智能tpk对适应性课程计划的间接影响。本研究强调，有效的提示工程是由教师的知识塑造的教学行为，而不仅仅是技术命令。报告还强调了在教师教育中纳入针对人工智能的教学培训的重要性。通过将提示概念化为认知动作，我们为教师和GenAI如何合作创造响应性和包容性的学习体验提供了新的见解。

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引用次数: 0

Empowering bilingual teachers with dynamic GenAI: Adaptive design and implementation of multimodal instructional strategies 赋予双语教师动态基因：多模式教学策略的适应性设计与实施

IF 10.5 1区教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Education

Pub Date : 2026-02-01 Epub Date: 2025-10-24 DOI: 10.1016/j.compedu.2025.105490

Wen-Li Chang , Jerry Chih-Yuan Sun

The study explores bilingual teachers' perceptions and applications of multimodal generative AI (GenAI)— including text generators, visual design tools, and feedback-driven writing assistants—within the context of adaptive instructional design aligned with the national bilingual policy. Within a design-based research framework, a one-semester teacher training course was structured into three stages: orientation, empowerment, and application. Hands-on workshops by a field expert were interspersed to accelerate the learning cycle toward systematic evaluation. The course concluded with a group retrospective interview to integrate an expert-led feedback loop for bilingual classrooms. Responses, paired with semester-end lesson plans, were analyzed to understand teachers' perceptions of multimodal GenAI in class design and their applications in teacher input, student output, and adaptive learning support. Findings reflect multimodal GenAI's potential to create personalized bilingual learning environments. Although teachers prioritize design for teaching over personalized learning and support, the technology—particularly dynamic GenAI for real-time feedback and learner adaptation—shows promise in optimizing bilingual classrooms. Future studies should highlight multimodal GenAI as a critical element of effective bilingual education while addressing the need to enhance teachers' AI literacy and instructional design expertise.

本研究探讨了双语教师在符合国家双语政策的适应性教学设计背景下对多模态生成人工智能（GenAI）的看法和应用，包括文本生成器、视觉设计工具和反馈驱动的写作助手。在以设计为基础的研究框架下，一个学期的教师培训课程分为三个阶段：指导、授权和应用。由现场专家亲自动手的工作坊被穿插在一起，以加速向系统化评估的学习周期。课程以小组回顾访谈结束，以整合专家主导的双语课堂反馈循环。通过对反馈和学期末课程计划进行分析，了解教师对多模态GenAI在课堂设计中的看法，以及它们在教师输入、学生输出和适应性学习支持方面的应用。研究结果反映了GenAI在创造个性化双语学习环境方面的潜力。尽管教师优先考虑教学设计而不是个性化学习和支持，但这项技术——尤其是用于实时反馈和学习者适应的动态GenAI——在优化双语课堂方面显示出了希望。未来的研究应强调多模态GenAI作为有效双语教育的关键要素，同时解决提高教师人工智能素养和教学设计专业知识的需求。

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引用次数: 0