Boosting Student Engagement in STEM: Integrating Large Language Model‐Based Virtual Agents Into Alternate Reality Games.
Background: STEM education aims to develop innovation and problem‐solving skills through interdisciplinary learning, yet struggles to foster student engagement and interdisciplinary thinking. Whilst alternate reality games (ARGs) can boost motivation via game‐based problem‐solving, integrating large...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 6; pp. 1 - 20 |
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| Autores principales: | , , , , , |
| Formato: | clinical trial pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189524199&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189524199 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Dec2025 vid: 41 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 189524199 189524199 189524199 10.1111/jcal.70139 189524199 ppf: 1 ppct: 19 formats: tig: atl: Boosting Student Engagement in STEM: Integrating Large Language Model‐Based Virtual Agents Into Alternate Reality Games. aug: au: Wang, Minkai Zhu, Jingdong Hwang, Gwo‐Jen Chang, Shao‐Chen Yang, Qi‐Fan Zhang, Di affil: College of Education, Zhejiang University of Technology, Hangzhou, China sug: subj: Science Education Technology Education Engineering Education Mathematics Education Students, Elementary Psychosocial Factors Artificial Intelligence, Generative Learning Methods Video Games Student Attitudes Evaluation Motivation Evaluation Academic Performance Evaluation Cognition Evaluation Learning Evaluation Computer-Assisted Instruction Program Evaluation Human Child Adolescence China Schools, Elementary Quasi-Experimental Studies Male Female Problem Solving Education, Interdisciplinary Feedback Pretest-Posttest Control Group Design Clinical Trials Funding Source Questionnaires Analysis of Covariance Pearson's Correlation Coefficient Correlational Studies Comparative Studies Systems Design Software Design Summated Rating Scaling Quantitative Studies Descriptive Statistics Task Performance and Analysis Child Behavior Evaluation Adolescent Behavior Evaluation Exploratory Research Regression Child: 6-12 years Adolescent: 13-18 years Male Female ab: Background: STEM education aims to develop innovation and problem‐solving skills through interdisciplinary learning, yet struggles to foster student engagement and interdisciplinary thinking. Whilst alternate reality games (ARGs) can boost motivation via game‐based problem‐solving, integrating large language models (LLMs) remains underexplored. LLM‐based virtual agents offer new opportunities for adaptive support. Objectives: This study aimed to investigate the effectiveness of an LLM‐assisted ARG system (LLM‐ARG) in enhancing academic performance, metacognitive awareness, and engagement. Methods: A quasi‐experimental study compared LLM‐ARG with conventional ARG methods amongst primary school students. The experimental group used LLM‐ARG with personalised virtual agent support, whilst the control group employed a conventional ARG with a traditional, rule‐based virtual agent that offered only pre‐scripted feedback. Data were collected through pre‐ and post‐tests, metacognitive awareness questionnaires, and interaction logs. ANCOVA and correlation analyses were conducted. Results and Conclusions: LLM‐ARG significantly improved learning achievements and metacognitive awareness compared to conventional ARG. High‐frequency interactions promoted exploration but did not consistently enhance problem‐solving, whilst low‐frequency interactions led to higher success via goal‐directed strategies. Metacognitive competence emerged as a key predictor of academic performance, highlighting the need to balance exploration with efficiency. This study demonstrates how LLM‐driven scaffolding supports diverse learning strategies and promotes adaptive learning in STEM education. pubtype: Academic Journal doctype: clinical trial pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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