Boosting Student Engagement in STEM: Integrating Large Language Model-Based Virtual Agents into Alternate Reality Games
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| Title: | Boosting Student Engagement in STEM: Integrating Large Language Model-Based Virtual Agents into Alternate Reality Games |
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| Language: | English |
| Authors: | Minkai Wang (ORCID |
| Source: | Journal of Computer Assisted Learning. 2025 41(6). |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 19 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Learner Engagement, STEM Education, Natural Language Processing, Artificial Intelligence, Technology Uses in Education, Educational Technology, Computer Simulation, Computer Games, Educational Games, Game Based Learning, Problem Solving, Academic Achievement, Metacognition, Predictor Variables |
| DOI: | 10.1111/jcal.70139 |
| ISSN: | 0266-4909 1365-2729 |
| Abstract: | 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. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1489797 |
| Database: | ERIC |
| Abstract: | 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. |
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| ISSN: | 0266-4909 1365-2729 |
| DOI: | 10.1111/jcal.70139 |
