Computers & Education最新文献_第7页

STEM education: Understanding secondary students’ epistemic cognition in the design process with the support of a personalized multi-agent system STEM教育：在个性化多智能体系统支持下理解中学生在设计过程中的认知

IF 10.5 1区教育学

Computers & Education Pub Date : 2026-03-01 Epub Date: 2025-11-10 DOI: 10.1016/j.compedu.2025.105504

Lei Gao , Morris Siu-Yung Jong , Ching Sing Chai , Keru Li

{"title":"STEM education: Understanding secondary students’ epistemic cognition in the design process with the support of a personalized multi-agent system","authors":"Lei Gao , Morris Siu-Yung Jong , Ching Sing Chai , Keru Li","doi":"10.1016/j.compedu.2025.105504","DOIUrl":"10.1016/j.compedu.2025.105504","url":null,"abstract":"<div><div>The engineering design process is a vital component of integrated STEM education, where students' design practices both shape and are shaped by their epistemic cognition (EC). Nonetheless, research on secondary students' EC in STEM contexts is limited, and implementing personalized STEM instruction that accommodates diverse students' interests and needs poses significant challenges. Harnessing a Multi-Agent System (MAS) afforded by Generative Artificial Intelligence (GAI) offers a promising pathway to address this gap. Grounded in the design thinking model of <em>E</em>DIPT (Empathize-Define-Ideate-Prototype-Test), this study developed a <em>P</em>ersonalized <em>MAS</em>, namely EPMAS, to support secondary students' STEM project-based design process. Twenty students, grouped into dyads, participated in a five-day STEM winter camp. We sought to explore students' EC within the EPMAS-supported design process and their perceptions of the system. Data were collected through in-depth group interviews, supplemented with student artifacts and interaction log data, and analyzed using theoretical thematic analysis. Findings revealed students' engagement with epistemic aims, ideals, and reliable processes, reflecting their developed understanding of engineering design. Contrary to common concerns about over-reliance on GAI, students demonstrated high epistemic agency, critically employing EPMAS's stepwise guidance as part of their epistemic repertoire to pursue creative solutions. These results suggest that EPMAS-supported authentic STEM learning holds promise for broader school adoption.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"242 ","pages":"Article 105504"},"PeriodicalIF":10.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145484961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Computers & Education Pub Date : 2026-03-01 Epub Date: 2025-10-30 DOI: 10.1016/j.compedu.2025.105491

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

{"title":"The impact of different collaboration formats on mathematical problem-solving in augmented reality","authors":"Candace Walkington , Julianna Washington-Henderson , Jonathan Hunnicutt , Mitchell J. Nathan","doi":"10.1016/j.compedu.2025.105491","DOIUrl":"10.1016/j.compedu.2025.105491","url":null,"abstract":"<div><div>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 <em>n</em> = 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.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"242 ","pages":"Article 105491"},"PeriodicalIF":10.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145404571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Embracing the challenge: Predicting self-testing in non-formal online courses using machine learning 迎接挑战：利用机器学习预测非正式在线课程中的自我测试

IF 10.5 1区教育学

Computers & Education Pub Date : 2026-03-01 Epub Date: 2025-11-20 DOI: 10.1016/j.compedu.2025.105507

Maria Klose , Philipp Handschuh , Diana Steger , Cordula Artelt

{"title":"Embracing the challenge: Predicting self-testing in non-formal online courses using machine learning","authors":"Maria Klose , Philipp Handschuh , Diana Steger , Cordula Artelt","doi":"10.1016/j.compedu.2025.105507","DOIUrl":"10.1016/j.compedu.2025.105507","url":null,"abstract":"<div><div>Technology-driven advancements have made adaptive and interactive learning techniques more accessible. Online courses increasingly integrate self-tests that offer automated and immediate feedback. Self-tests can help learners to identify knowledge gaps and to reinforce their retention and comprehension. However, not all learners readily use self-tests, raising the question of which factors may impact learners' engagement with self-tests. The present study focused on non-formal education, covering 45 online courses offered by Bavarian universities. Analyses were based on a sample of <em>N</em> = 1261 participants aged 16–84 years. We used a machine learning approach to predict learners' engagement with self-tests and to identify important influencing factors. Therefore, we included 50 predictor variables in an elastic net regression to explore the role of learner- and course-related characteristics. The predictor variables were drawn from self-report, process, and meta data. Overall, learners differed substantially in their self-testing behavior. The prediction model explained 11 % of the variance in learners' engagement with self-tests. Despite the model's modest explanatory power, the analysis identified potentially relevant predictors. The two most important predictor variables were learner commitment and the intention to obtain a confirmation of participation. Accordingly, course designers might implement extrinsic incentives—such as confirmations of participation—as a potentially useful strategy to encourage learners' engagement with self-tests. From a methodological perspective, the study highlights the importance of using appropriate statistical methods—such as machine learning algorithms—to understand complex learning behaviors.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"242 ","pages":"Article 105507"},"PeriodicalIF":10.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145560037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Retraction notice to “Collaborative AI-in-the-loop pedagogical conversational agent to enhance social and cognitive presence in cMOOC” [Computers & Education 240 (206) 105451] 关于“协作ai -in- loop教学会话代理增强cMOOC中的社交和认知存在”的撤回通知[Computers & Education 240 (206) 105451]

IF 10.5 1区教育学

Computers & Education Pub Date : 2026-03-01 Epub Date: 2025-12-04 DOI: 10.1016/j.compedu.2025.105516

Jianjun Xiao , Yulin Tian , Cixiao Wang

引用次数: 0

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

Computers & Education Pub Date : 2026-03-01 Epub Date: 2025-11-04 DOI: 10.1016/j.compedu.2025.105492

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

{"title":"Theorizing AI literacy development using Habermas' three cognitive knowledge interests from a systematic review: A STEM interdisciplinary perspective","authors":"Yuk Mui Elly Heung , Hong-biao Yin , Lyn English , Thomas K.F. Chiu","doi":"10.1016/j.compedu.2025.105492","DOIUrl":"10.1016/j.compedu.2025.105492","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"242 ","pages":"Article 105492"},"PeriodicalIF":10.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145441547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring students' self-regulated learning behavioural patterns and perceptions in an English speaking task within a generative AI-supported immersive VR environment 在生成式人工智能支持的沉浸式虚拟现实中，探索学生在英语口语任务中自我调节的学习行为模式和感知

IF 10.5 1区教育学

Computers & Education Pub Date : 2026-03-01 Epub Date: 2025-11-27 DOI: 10.1016/j.compedu.2025.105515

Lei Tao, Yanjie Song, Jiachen Fu

{"title":"Exploring students' self-regulated learning behavioural patterns and perceptions in an English speaking task within a generative AI-supported immersive VR environment","authors":"Lei Tao, Yanjie Song, Jiachen Fu","doi":"10.1016/j.compedu.2025.105515","DOIUrl":"10.1016/j.compedu.2025.105515","url":null,"abstract":"<div><div>Generative AI (GenAI) has introduced new opportunities for self-regulated learning (SRL) by enabling adaptive, conversational support in immersive educational environments. Yet little is known about how SRL unfolds in real time during oral communication tasks with GenAI support in immersive virtual reality (iVR). Most studies rely on post-task questionnaires, leaving unclear how different types of agent support influence regulatory processes and whether GenAI agents provide more effective support than scripted ones. This randomised experimental study compared college students' SRL behaviours under two conditions: a control group interacting with a scripted agent (34 students) and an experimental group interacting with a GenAI agent (37 students). Their interactions were coded using a validated SRL framework and analysed through behavioural sequence analysis. Results showed that learners with GenAI support demonstrated more frequent reflection behaviours (21.2 % vs. 8.9 %) and greater diversity in their regulatory strategies, indicating more adaptive engagement across SRL phases. Within the GenAI condition, two learner profiles were identified: strategic learners, who sustained longer and more diverse regulatory cycles, and reactive learners, whose behaviours were shorter and more affect-driven, demonstrating that individual learners responded differently to GenAI support. Regression and ANCOVA analyses confirmed that these behavioural profiles significantly predicted learners' perceived SRL gains, after controlling for baseline differences. These findings show that GenAI agents provide adaptive support that strengthens learners' planning, monitoring, and reflection processes in immersive speaking tasks. They also underline the educational significance of linking behavioural profiles with adaptive agent design to advance personalised support in iVR language learning environments.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"242 ","pages":"Article 105515"},"PeriodicalIF":10.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

More inspiration and attention: How Generative AI tools impact graduate students' affective engagement in L2 source-based academic writing 更多的灵感和关注：生成人工智能工具如何影响研究生在基于L2资源的学术写作中的情感参与

IF 10.5 1区教育学

Computers & Education Pub Date : 2026-03-01 Epub Date: 2025-11-05 DOI: 10.1016/j.compedu.2025.105495

Zhangze Chen , Jia Zhu , Joshua McGrane , Therese N. Hopfenbeck , Yilin Wang , Yilong Ji

{"title":"More inspiration and attention: How Generative AI tools impact graduate students' affective engagement in L2 source-based academic writing","authors":"Zhangze Chen , Jia Zhu , Joshua McGrane , Therese N. Hopfenbeck , Yilin Wang , Yilong Ji","doi":"10.1016/j.compedu.2025.105495","DOIUrl":"10.1016/j.compedu.2025.105495","url":null,"abstract":"<div><div>Amidst the growing hype surrounding Generative AI (GenAI) for second language (L2) writing purposes, multiple studies have emerged, but few have emphasized its effects on students' affective engagement when they utilize it for source-based L2 academic writing. Against this backdrop, the present study employed a randomized controlled trial (RCT) with 81 participants in China to evaluate students' affective engagement through quantitative data from questionnaires and qualitative data from stimulated recall with screencasts and semi-structured interviews. The findings show that traditional AI tools (e.g., DeepL) and GenAI tools show different effects on students' affective engagement across writing stages. GenAI tools, particularly when integrated with suggested prompts, significantly enhanced students' overall positive affect. Participants reported increased feelings of inspiration, alertness, attentiveness, and determination. However, qualitative analyses additionally revealed that students using suggested prompts experienced mixed emotions in later writing stages, particularly during revision, when AI outputs occasionally mismatched their individual needs and stylistic preferences. Moreover, students' affective engagement was dynamic, fluctuating across writing stages and prompt design and construction. This research offers insights for educators on how to effectively integrate GenAI into L2 writing classrooms.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"242 ","pages":"Article 105495"},"PeriodicalIF":10.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145447351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical analysis of in-service teachers’ barriers to technology-integrated instruction: A review of 2000–2024 publications 在职教师技术整合教学障碍的层次分析：2000-2024年文献综述

IF 10.5 1区教育学

Computers & Education Pub Date : 2026-03-01 Epub Date: 2025-11-17 DOI: 10.1016/j.compedu.2025.105509

Chih-Hung Chen , Hua-Yi Fei , Chin-Chung Tsai

{"title":"Hierarchical analysis of in-service teachers’ barriers to technology-integrated instruction: A review of 2000–2024 publications","authors":"Chih-Hung Chen , Hua-Yi Fei , Chin-Chung Tsai","doi":"10.1016/j.compedu.2025.105509","DOIUrl":"10.1016/j.compedu.2025.105509","url":null,"abstract":"<div><div>Barriers to technology adoption in education have emerged as a critical issue in technology-integrated instruction, widely recognized as key factors shaping teachers' willingness and readiness to adopt technology in classroom practice. This systematic literature review analyzed in-service teachers' barriers to technology-integrated instruction, based on 116 empirical studies published from 2000 to 2024 in the Web of Science SSCI database. A four-order hierarchical framework was proposed in this review to analyze the barriers: (1) First-order barriers, which include limited systemic support such as lack of resources, technical support, and time; (2) Second-order barriers, involving inadequate beliefs and attitudes such as teachers' concerns, emotion, and resistance to technology use; (3) Third-order barriers, addressing classroom management challenges; and (4) Fourth-order barriers, related to the lack of design thinking. Results drawn from the 116 studies revealed that first-order barriers were the most frequently mentioned challenges, followed by the second-, third-, and fourth-order barriers. Many studies identified overlapping barriers, with the combination of first- and second-order barriers being the most frequently mentioned, revealing more frequent co-occurrence of limited systemic support and inadequate beliefs and attitudes. Moreover, university teachers exhibited particularly higher occurrence rates across the second-, third-, and fourth-order barriers. This review also investigated the role of COVID-19 pandemic for the teachers' barriers to technology integration. Compared to non-pandemic studies, teachers confronted significantly higher occurrence rates of the second- and third-order barriers to technology integration during the COVID-19 pandemic. This implied that the sudden shift to fully online instruction increased teachers’ internal resistance, and highlighted the challenges of classroom management in a fully online instructional environment.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"242 ","pages":"Article 105509"},"PeriodicalIF":10.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145553950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Computers & Education Pub Date : 2026-03-01 Epub Date: 2025-11-02 DOI: 10.1016/j.compedu.2025.105493

Fan Chen , Bo Xiong , Gaowei Chen

{"title":"Fostering scientific inquiry with hybrid intelligence: A semester-long experiment on knowledge acquisition in the science classroom","authors":"Fan Chen , Bo Xiong , Gaowei Chen","doi":"10.1016/j.compedu.2025.105493","DOIUrl":"10.1016/j.compedu.2025.105493","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"242 ","pages":"Article 105493"},"PeriodicalIF":10.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145424031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Computers & Education Pub Date : 2026-02-01 Epub Date: 2025-11-03 DOI: 10.1016/j.compedu.2025.105494

Linjin Xi , Yi Zhang , Qiyun Wang

{"title":"Investigating the effects of an LLM-based Socratic conversational agent on students’ academic performance and reflective thinking in higher education","authors":"Linjin Xi , Yi Zhang , Qiyun Wang","doi":"10.1016/j.compedu.2025.105494","DOIUrl":"10.1016/j.compedu.2025.105494","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"241 ","pages":"Article 105494"},"PeriodicalIF":10.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145434320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0