Computers & Education最新文献_第6页

Theorizing AI literacy development using Habermas' three cognitive knowledge interests from a systematic review: A STEM interdisciplinary perspective 基于哈贝马斯的三种认知知识兴趣的人工智能读写能力发展理论综述：一个STEM跨学科的视角

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

Computers & Education

Pub Date : 2025-11-04 DOI: 10.1016/j.compedu.2025.105492

Yuk Mui Elly Heung , Hong-biao Yin , Lyn English , Thomas K.F. Chiu

Existing research demonstrates AI's potential to improve learning outcomes and engagement, emphasizing the importance of pedagogical designs that promote AI literacy. This systematic literature review investigates how different literacies connect to AI literacy and offers pedagogical suggestions from a science, technology, engineering, and mathematics (STEM) learning perspective. Using the PRISMA approach, we screened 1603 articles from four databases and selected 58 for this review. We theorize AI literacy development using Habermas' three cognitive knowledge interests: technical, practical, and emancipatory as the primary analytical framework. We examine each interest individually and collectively to structure and connect the progression levels within UNESCO's AI competency framework for teachers. Our findings highlight the need for an interdisciplinary approach, with various literacies—data, digital, mathematical, algorithmic, scientific, computational, media, language, and civic—being critical for developing AI literacy. We presented a hierarchical structure to describe how the literacies related to AI literacy. Moreover, we suggest age-appropriate, culturally sensitive pedagogical methodologies, with project-based and problem-based learning helpful in K-12 and higher education and game-based learning, which incorporates AI toys and role-play, especially advantageous in early childhood education. Furthermore, we emphasize case-based, reflective, and cultural learning as important strategies for establishing ethical AI citizenship by allowing students to balance sociocultural aspects while developing unbiased and responsible AI-enhanced applications for society and the environment.

现有研究表明，人工智能有潜力改善学习成果和参与度，强调了促进人工智能素养的教学设计的重要性。这篇系统的文献综述调查了不同的素养与人工智能素养之间的关系，并从科学、技术、工程和数学（STEM）学习的角度提出了教学建议。使用PRISMA方法，我们从4个数据库中筛选了1603篇文章，并选择了58篇用于本综述。我们将哈贝马斯的三个认知知识兴趣：技术、实践和解放作为主要的分析框架，将人工智能素养的发展理论化。我们单独和集体审查每个兴趣，以便在教科文组织教师人工智能能力框架内构建和连接进步水平。我们的研究结果强调了跨学科方法的必要性，数据、数字、数学、算法、科学、计算、媒体、语言和公民等各种素养对于培养人工智能素养至关重要。我们提出了一个层次结构来描述读写能力与人工智能读写能力之间的关系。此外，我们建议采用适合年龄的、文化敏感的教学方法，基于项目和基于问题的学习有助于K-12和高等教育，以及基于游戏的学习，其中结合了人工智能玩具和角色扮演，在幼儿教育中特别有利。此外，我们强调基于案例、反思性和文化学习是建立道德人工智能公民的重要策略，允许学生平衡社会文化方面，同时为社会和环境开发公正和负责任的人工智能增强应用。

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引用次数: 0

Investigating the effects of an LLM-based Socratic conversational agent on students’ academic performance and reflective thinking in higher education 基于法学硕士的苏格拉底对话代理对高等教育学生学习成绩和反思性思维的影响

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

Computers & Education

Pub Date : 2025-11-03 DOI: 10.1016/j.compedu.2025.105494

Linjin Xi , Yi Zhang , Qiyun Wang

The rapid advancement of GenAI has created unprecedented opportunities to enhance student learning through large language models (LLMs). While LLM-based conversational agents have demonstrated potential in improving accessibility and instructional support, evidence of their impact on higher-order thinking remains limited, especially within the context of Chinese higher education. This study introduces the Socratic Intelligent Conversational Agent (S-ICA), which integrates Socratic questioning strategies with semantic generation mechanisms to promote student learning and reflective thinking in disciplinary contexts. A total of 94 university students from China participated in the study, with random assignment to an experimental group (EG) using the S-ICA and a control group (CG) using a non-Socratic conversational agent (nS-ICA). The results showed that the EG outperformed the CG in both academic achievement and reflective thinking, particularly in the dimensions of “reflection” and “critical reflection”. Cognitive network analysis revealed that the EG students activated more advanced reflective pathways, linking understanding, reflection, and critical reflection processes. Although no significant differences were found in learning motivation between the groups, interviews with students indicated that the S-ICA facilitated more effective learning and deeper reflective engagement. These findings contribute to research on integrating classical pedagogical strategies into GenAI-based systems, offering insights into how such technologies can foster higher-order thinking and guide the design of future collaborative human–AI learning systems.

GenAI的快速发展为通过大型语言模型（llm）提高学生的学习创造了前所未有的机会。虽然基于法学硕士的会话代理在提高可访问性和教学支持方面已经显示出潜力，但它们对高阶思维的影响证据仍然有限，特别是在中国高等教育的背景下。本研究引入苏格拉底式智能对话代理（S-ICA），将苏格拉底式提问策略与语义生成机制相结合，以促进学生在学科情境中的学习和反思性思维。共有94名来自中国的大学生参加了这项研究，随机分配到使用S-ICA的实验组（EG）和使用非苏格拉底会话代理（nS-ICA）的对照组（CG）。结果表明，EG在学业成绩和反思思维两个维度上都优于CG，尤其是在“反思”和“批判性反思”两个维度上。认知网络分析显示，EG学生激活了更高级的反思途径，将理解、反思和批判性反思过程联系起来。虽然小组之间的学习动机没有显著差异，但对学生的访谈表明，S-ICA促进了更有效的学习和更深层次的反思参与。这些发现有助于将经典教学策略整合到基于genai的系统中，为这些技术如何培养高阶思维和指导未来人类-人工智能协作学习系统的设计提供见解。

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引用次数: 0

Fostering scientific inquiry with hybrid intelligence: A semester-long experiment on knowledge acquisition in the science classroom 用混合智能培养科学探究：科学课堂知识获取的一学期实验

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

Computers & Education

Pub Date : 2025-11-02 DOI: 10.1016/j.compedu.2025.105493

Fan Chen , Bo Xiong , Gaowei Chen

Scientific inquiry is essential for developing deep conceptual understanding and exploratory skill, yet its complexity presents significant challenges for classroom implementation. While technologies like learning analytics (LA) and artificial intelligence (AI) offer promising tools for monitoring and guidance, the comparative and longitudinal effects of their integration remain underexplored. This study addresses this gap through a semester-long quasi-experiment with 177 secondary students across four conditions representing progressively enhanced technological support: a control (GICG), a structured system (BICG), the system plus an LA dashboard (ADIG), and the full integration of the system, dashboard, and an AI assistant (HIIG). A mixed-method analysis was conducted to assess knowledge acquisition and learning experiences. Quantitative findings revealed a clear performance hierarchy in theme-specific knowledge assessments (HIIG > ADIG ≈ BICG > GICG), with the fully integrated HIIG group demonstrating significantly higher comprehensive knowledge acquisition than the control group. Qualitative findings highlighted positive student perceptions of technology-augmented inquiry, like improved engagement, reduced frustration, and enhanced ability to navigate inquiry challenges, though the level of technological support influenced help-seeking behaviors and collaboration dynamics. This study provides empirical evidence for the synergistic value of integrating AI and LA, demonstrating that their combination creates an effective scaffolding system that fosters sustained knowledge acquisition by supporting both the cognitive and affective dimensions of student inquiry in authentic classrooms.

科学探究对于发展深刻的概念理解和探索技能至关重要，但其复杂性对课堂实施提出了重大挑战。虽然学习分析（LA）和人工智能（AI）等技术为监测和指导提供了很有前途的工具，但它们整合的比较和纵向影响仍未得到充分探索。本研究通过对177名中学生进行为期一个学期的准实验，在四种条件下解决了这一差距，这些条件代表了逐步增强的技术支持：控制（gigg）、结构化系统（BICG）、系统加LA仪表板（ADIG），以及系统、仪表板和人工智能助手（HIIG）的完全集成。采用混合方法分析知识获取和学习经验。定量研究结果显示，在特定主题知识评估（HIIG > ADIG≈BICG > gig）中，表现层次明显，完全整合HIIG组的综合知识获取显著高于对照组。定性研究结果强调了学生对技术增强探究的积极看法，如提高参与度，减少挫折感，增强应对探究挑战的能力，尽管技术支持水平影响了寻求帮助的行为和协作动态。本研究为整合AI和LA的协同价值提供了经验证据，表明它们的结合创造了一个有效的脚手架系统，通过支持真实课堂中学生探究的认知和情感维度，促进持续的知识获取。

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引用次数: 0

The impact of different collaboration formats on mathematical problem-solving in augmented reality 增强现实中不同协作形式对数学问题解决的影响

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

Computers & Education

Pub Date : 2025-10-30 DOI: 10.1016/j.compedu.2025.105491

Candace Walkington , Julianna Washington-Henderson , Jonathan Hunnicutt , Mitchell J. Nathan

Augmented reality (AR), where digital holograms are layered over the real world, is rapidly being applied to different educational domains, including mathematics education. However, little is known about how AR can be effectively designed for collaborative use. There are both practical and theory-based reasons to set up collaboration in AR in different ways, which result in different costs and opportunities for learning. Here we compare parallel collaboration, where each student separately experiences AR holograms on their own AR device, to observer/observed collaboration, where one student experiences AR overlays on their AR device, while another observes their first-person camera feed, to joint collaboration, where students see a single, synced AR hologram that updates based on their partners’ actions. We report a pre-registered within-subjects study where n = 70 high school students in pairs confronted holographic geometry tasks. We examine their actions and gestures, as well as their post-test performance, as it varied by the collaboration condition through which they learned the target mathematical concepts. Results suggest that parallel collaboration is most effective overall, with some evidence that joint collaboration might be superior for higher-knowledge learners. In addition, different collaboration formats predict different uses of gestures and actions, but we do not see positive associations between these gestures and actions and post-test performance. Implications for theory, design, and educators are discussed.

增强现实（AR）技术将数字全息图分层置于现实世界之上，正迅速应用于不同的教育领域，包括数学教育。然而，对于如何有效地设计AR以供协作使用，人们知之甚少。在AR中建立协作的方式不同，既有现实的原因，也有理论的原因，这就导致了不同的成本和学习机会。在这里，我们比较了并行协作（每个学生分别在自己的AR设备上体验AR全息图）、观察者/被观察者协作（一个学生在自己的AR设备上体验AR叠加，而另一个学生观察他们的第一人称摄像头反馈）和联合协作（学生看到一个同步的AR全息图，该全息图会根据合作伙伴的动作更新）。我们报告了一项预先注册的受试者研究，其中n = 70名高中生成对面对全息几何任务。我们检查了他们的动作和手势，以及他们在测试后的表现，因为它随着他们学习目标数学概念的合作条件而变化。结果表明，并行协作总体上是最有效的，有证据表明，联合协作可能对更高知识的学习者更有利。此外，不同的协作格式预测了手势和动作的不同使用，但我们没有看到这些手势和动作与测试后表现之间的积极联系。讨论了对理论、设计和教育工作者的影响。

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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 : 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

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 : 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

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 : 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

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 : 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

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 : 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

Development and implementation of a generative AI-based personalized recommender system to improve students’ self-regulated learning and academic performance 基于生成式人工智能的个性化推荐系统的开发与实施，提高学生自主学习和学习成绩

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

Computers & Education

Pub Date : 2025-10-14 DOI: 10.1016/j.compedu.2025.105486

Xinyi Luo, Sikai Wang, Khe Foon Hew

Students' ability to manage their own learning is crucial for academic success, but many struggle with self-regulation, often leading to disengagement. Past studies have explored tactics to boost students' self-regulated learning (SRL), such as writing self-reflective reports and incorporating prompts in video lectures. These tactics, however, often lack timely, personalized feedback due to the labor-intensive nature of such support. In this study, we report the development and implementation of an innovative large language model (LLM)-based recommender system named SRLAdvisor to address these limitations. The system combines a web interface with a series of LLM-based agents, allowing students to receive immediate, personalized feedback. By processing students' natural language interactions in real-time, SRLAdvisor dynamically profiles their self-regulation and delivers personalized recommendations. This study involved 22 postgraduate students and employed a sequential explanatory mixed-methods approach to evaluate students’ self-regulation and learning performance. We found near-perfect agreement between the system detection and human coding. SRLAdvisor exhibited greater recommendation precision in self-monitoring and self-evaluation tasks. Students who perceived the system as highly useful showed significantly greater self-reported SRL gains than those with lower perceptions. Additionally, a k-means cluster analysis indicated that students who engaged more frequently with SRLAdvisor achieved significantly greater improvements in learning performance. These findings underscore the potential of leveraging LLMs to deliver personalized recommendations. However, they also reveal potential concerns, such as over-reliance on AI and prompt monotony, that need to be further examined. These issues are particularly relevant in the context of LLM-supported SRL interventions and warrant further discussion.

学生管理自己学习的能力对学业成功至关重要，但许多人在自我调节方面遇到困难，往往导致他们脱离学习。过去的研究探索了促进学生自我调节学习（SRL）的策略，比如写自我反思报告和在视频讲座中加入提示。然而，由于这种支持的劳动密集型性质，这些策略往往缺乏及时、个性化的反馈。在这项研究中，我们报告了一个名为SRLAdvisor的创新的基于大语言模型（LLM）的推荐系统的开发和实现，以解决这些限制。该系统将网络界面与一系列基于法学硕士的代理相结合，使学生能够获得即时的个性化反馈。通过实时处理学生的自然语言互动，SRLAdvisor动态地分析他们的自我调节并提供个性化的建议。本研究以22名研究生为研究对象，采用序贯解释混合方法评估学生的自我调节和学习表现。我们发现在系统检测和人类编码之间存在近乎完美的一致性。SRLAdvisor在自我监控和自我评价任务中表现出更高的推荐精度。那些认为该系统非常有用的学生比那些认为该系统非常有用的学生表现出更大的自我报告的SRL收益。此外，k-means聚类分析表明，更频繁地使用SRLAdvisor的学生在学习成绩上取得了显著更大的进步。这些发现强调了利用法学硕士提供个性化推荐的潜力。然而，它们也揭示了潜在的问题，比如过度依赖人工智能和即时单调，这些问题需要进一步研究。这些问题与法学硕士支持的SRL干预措施特别相关，值得进一步讨论。

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引用次数: 0