面向在线教育中的可持续学习：一种强化学习方法
Towards Sustainable Learning in Online Education: A Reinforcement Learning Approach
AI总结：
针对在线教育参与度低、长期效果差的问题，提出基于强化学习的AI Tutor模型，通过优化短期与长期学习结果，在2300万条学习记录的评估中优于基准模型，可提供自适应个性化支持。
AI中文摘要：
在线教育为来自不同背景的全球学习者提供了前所未有的可扩展性和可访问性，但往往存在参与度低、长期学习效果差的问题。为应对这些挑战，本文引入AI Tutor，一种基于强化学习的模型，旨在通过优化短期和长期学习结果来促进可持续学习。短期而言，AI-Tutor借鉴认知理论，引导学习者在获取新知识与巩固已有学习间取得平衡；长期而言，它对学习者参与度进行建模，以制定维持学习动机、降低辍学率的策略。这些改进使AI-Tutor能提供个性化指导，既促进有效学习又维持持续参与。对33700名学习者的2300万条学习记录进行的实证评估显示，AI Tutor在参与度、知识保留率及最终学习结果上均始终优于最先进的基准模型。学习路径分析进一步揭示，AI-Tutor如何针对不同特征的学习者调整策略，提供自适应且以人为本的支持。
英文摘要：
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.
