SRLAgent：透過遊戲化與大型語言模型輔助提升自主學習能力

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

自我調節學習（SRL）對於大學生應對日益增加的學業要求和獨立性至關重要。SRL技能的不足可能導致學習習慣混亂、動機低落以及時間管理不善，從而削弱學習者在挑戰性環境中茁壯成長的能力。通過一項涉及59名大學生的形成性研究，我們發現了學生在發展SRL技能時面臨的關鍵挑戰，包括目標設定、時間管理和反思學習方面的困難。為應對這些挑戰，我們引入了SRLAgent，這是一個由大型語言模型（LLMs）輔助的系統，通過遊戲化和LLMs的適應性支持來培養SRL技能。基於Zimmerman的三階段SRL框架，SRLAgent使學生能夠在互動的遊戲化環境中進行目標設定、策略執行和自我反思。該系統提供由LLMs驅動的即時反饋和支架，以支持學生的獨立學習努力。我們使用受試者間設計評估了SRLAgent，將其與基線系統（無Agent功能的SRL）和傳統的多媒體學習條件進行比較。結果顯示，SRLAgent組在SRL技能上有顯著提升（p < .001，Cohen's d = 0.234），並且與基線相比具有更高的參與度。這項工作強調了在遊戲化環境中嵌入SRL支架和即時AI支持的價值，為旨在促進深度學習和元認知技能發展的教育技術提供了設計啟示。

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.