SRLAgent: Enhancing Self-Regulated Learning Skills through Gamification and LLM Assistance

2025/06/11 by Wentao Ge, Ge, Wentao, Yuqing Sun +12 · 1 citation
Computer Science · Psychology · #Educational Games and Gamification #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #I.2.1 #I.2.6 #Innovative Teaching and Learning Methods #Intelligent Tutoring Systems and Adaptive Learning

paper · pdf · doi:10.48550/arxiv.2506.09968

openalex publication_date 2025/06/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Self-regulated learning (SRL) is crucial for college students navigating increased academic demands and independence. Insufficient SRL skills can lead to disorganized study habits, low motivation, and poor time management, undermining learners ability to thrive in challenging environments. Through a formative study involving 59 college students, we identified key challenges students face in developing SRL skills, including difficulties with goal-setting, time management, and reflective learning. To address these challenges, we introduce SRLAgent, an LLM-assisted system that fosters SRL skills through gamification and adaptive support from large language models (LLMs). Grounded in Zimmermans three-phase SRL framework, SRLAgent enables students to engage in goal-setting, strategy execution, and self-reflection within an interactive game-based environment. The system offers real-time feedback and scaffolding powered by LLMs to support students independent study efforts. We evaluated SRLAgent using a between-subjects design, comparing it to a baseline system (SRL without Agent features) and a traditional multimedia learning condition. Results showed significant improvements in SRL skills within the SRLAgent group (p < .001, Cohens d = 0.234) and higher engagement compared to the baselines. This work highlights the value of embedding SRL scaffolding and real-time AI support within gamified environments, offering design implications for educational technologies that aim to promote deeper learning and metacognitive skill development.

Citations

• Enhancing LLM-Based Feedback: Insights from Intelligent Tutoring Systems and the Learning Sciences
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