用强化学习打造能持续激励学生的智能导师
Towards Sustainable Learning in Online Education: A Reinforcement Learning Approach
- 基于认知理论设计，平衡新学与复习，提升短期学习效果
- 通过建模学习者参与度，降低33,700名用户的学习中途放弃率
- 适合在线教育平台优化个性化学习路径，提升长期留存
在线教育虽具全球可及性，却常面临参与度低与长期学习效果差的问题。为此，我们提出AI Tutor，一种基于强化学习的模型，旨在优化短期学习成效与长期学习可持续性。短期内，该模型依据认知理论，协调新知识获取与已有知识巩固；长期内，通过建模学习者参与度，制定维持动机、减少辍学的策略。在包含33,700名学习者、2300万条学习记录的数据集上，AI Tutor在参与度、知识保留和最终学习成果上均优于现有最优基线。学习路径分析显示，该系统能根据学习者特征自适应调整策略，提供更具人性化的支持。
原文摘要 · Abstract (English)
Online education offers unprecedented scalability and accessibility to global learners from diverse backgrounds, but it often suffers from low engagement and poor long term learning effectiveness. To address these challenges, we introduce AI Tutor, a reinforcement learning based model designed to promote sustainable learning by optimizing both short and longterm learning outcomes. In the short term, AI-Tutor draws on cognitive theory to guide learners through a balance of acquiring new knowledge and reinforcing prior learning. In the long term, it models learner engagement to inform strategies that sustain motivation and reduce dropout. These enhancements enable AI-Tutor to provide personalized guidance that fosters both effective learning and sustained participation. Empirical evaluations on 23 million learning records from 33,700 learners show that AI Tutor consistently outperforms state-of-the-art baselines across engagement, knowledge retention, and final learning outcomes. Learning path analyses further reveal how AI-Tutor adapts its strategies to learners with diverse profiles, offering adaptive and human-centered support.
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