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Mapping student-AI interaction dynamics in multi-agent learning environments

Supporting personalized learning and reducing performance gaps

Datos Bibliográficos

ID21538779
AutoresZhanxin Hao (0000-0002-1526-8515, Tsinghua University), Jie Cao (0000-0001-8268-954X, University of North Carolina at Chapel Hill), Ruimiao Li (0000-0002-9146-1624, Shanghai Normal University), Jifan Yu (0000-0003-3430-4048, Tsinghua University), Zhiyuan Liu (0000-0002-6331-0810, Tsinghua University), Yu Zhang (0000-0002-0059-270X, Tsinghua University, autor de correspondencia)
Año2026
Volumen241
Páginas105472
Fecha de publicación2026-02-01
Open AccessSí
TipoARTICLE
RevistaComputers & Education (JOURNAL)
Identificadores de la revistaISSN: 0360-1315 • E-ISSN: 1873-782X
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.compedu.2025.105472
OpenAlexW4414958200
IdiomaEN
Citas recibidas5
Referencias citadas68

Cognition · Collaborative learning · Informal learning · Personalized Learning · Intelligent Tutoring Systems and Adaptive Learning · Online and Blended Learning · Online Learning and Analytics

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  • Engagement Dynamics in AI ‐Mediated Informal Digital Learning of English

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  • Student Perspectives on Teacher Interpersonal Behaviours in AI ‐Mediated Higher Education

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  • Supporting students’ self-regulated learning in online learning using artificial intelligence applications

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  • The technology acceptance model (TAM)

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  • Learning design to support student-AI collaboration

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  • Interaction and learning engagement in online learning

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  • Education in the Era of Generative Artificial Intelligence (AI)

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  • Addressing the Ceiling Effect when Assessing STEM Out-Of-School Time Experiences

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  • From Prompt to Polished

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  • Shaping the Future of Education

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  • Generative Artificial Intelligence and Extended Cognition in Science Learning Contexts

    Open Access•Ángel Rivera-Novoa, Daniel Augusto Duarte Arias•Science & Education•2026

  • Self-assessing a test with a possible bonus enhances low performers’ academic performance

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  • Automated detection of emotional and cognitive engagement in Mooc discussions to predict learning achievement

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  • What matters in AI-supported learning

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  • Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom

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  • Challenges to assessing motivation in Mooc learners

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  • A robot-based digital storytelling approach to enhancing EFL learners’ multimodal storytelling ability and narrative engagement

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  • Unpacking help-seeking process through multimodal learning analytics

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  • Students' reflections on their experience with ChatGPT

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  • Differences in student-AI interaction process on a drawing task

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  • Examining students’ intention to use ChatGPT

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  • Interacting with ChatGPT for internal feedback and factors affecting feedback quality

    Angela Choi Fung Tam•Assessment & Evaluation in Higher…•2025

  • Intrinsic, Extrinsic, and Amotivational Styles as Predictors of Behavior

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  • Investigating the Influence of Interaction on Learning Persistence in Online Settings

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  • Relationships between student engagement and academic achievement

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  • Autonomy in children's learning

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  • When helping helps

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  • Toward a motivational model of couple happiness

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  • Promises and challenges of generative artificial intelligence for human learning

    Open Access•Lixiang Yan, Samuel Greiff et al.•Nature Human Behaviour•2024

  • Human-AI collaboration patterns in AI-assisted academic writing

    Open Access•Andy Nguyen, Yvonne Hong et al.•Studies in Higher Education•2024

Citas por año5
Intervalo de citas2025 - 2026 (2)
Altamente citadoNo

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Ethnos_APP • Proyecto Open Source • Licencia MIT • Frontend v2.0.0 • Privacidad y Cookies • Documentación de la API: api.ethnos.app/docs • Código de la API: GitHub • DOI: 10.5281/zenodo.17049435 • Código del Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae