SRLAgent：通过游戏化与大语言模型辅助提升自主学习能力

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

June 11, 2025

作者: Wentao Ge, Yuqing Sun, Ziyan Wang, Haoyue Zheng, Weiyang He, Piaohong Wang, Qianyu Zhu, Benyou Wang

cs.AI

摘要

自主学习（Self-regulated Learning, SRL）对于大学生应对日益增长的学术要求与独立性至关重要。SRL技能的不足可能导致学习习惯混乱、动力低下及时间管理不善，从而削弱学习者在挑战性环境中取得成功的潜力。通过一项涉及59名大学生的形成性研究，我们识别出学生在发展SRL技能过程中面临的主要挑战，包括目标设定、时间管理及反思性学习方面的困难。为应对这些挑战，我们引入了SRLAgent，一个借助大型语言模型（LLMs）辅助的系统，通过游戏化设计和LLMs的适应性支持来培养SRL技能。基于Zimmerman的三阶段SRL框架，SRLAgent让学生在互动游戏环境中进行目标设定、策略执行及自我反思。该系统利用LLMs提供实时反馈与支架，支持学生的独立学习。我们采用组间设计对SRLAgent进行了评估，将其与无Agent功能的基线系统及传统多媒体学习条件进行对比。结果显示，SRLAgent组在SRL技能上显著提升（p < .001，Cohen's d = 0.234），且相较于基线系统展现出更高的参与度。本研究强调了在游戏化环境中嵌入SRL支架与实时AI支持的价值，为旨在促进深度学习与元认知技能发展的教育技术提供了设计启示。

English

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.