Towards Sustainable Learning in Online Education: A Reinforcement Learning Approach

Chaofan Zhai; Yicheng Song; Ravi Bapna; Junyao Ye

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arXiv · 2608.11245

Towards Sustainable Learning in Online Education: A Reinforcement Learning Approach

Chaofan Zhai·Yicheng Song·Ravi Bapna·Junyao Ye

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Abstract

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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Chaofan Zhai, Yicheng Song, Ravi Bapna, Junyao Ye. 2026-08-02. Towards Sustainable Learning in Online Education: A Reinforcement Learning Approach. https://arxiv.org/abs/2608.11245

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