跳转至

迈向在线教育的可持续学习：一种强化学习方法

文章背景与核心概要

在线教育虽然提供了全球化的可及性，但长期以来一直面临着学生参与度低和辍学率高的痛点。为了克服这些局限性，本文引入了一种名为 AI-Tutor 的新型强化学习模型，旨在推动可持续学习。该模型借鉴认知理论，在短期内平衡知识获取与复习，同时通过对学习者参与度进行建模来维持长期学习动力，从而提供个性化、以人为本的指导。

基于 2300 万条学习记录的实证结果表明，AI-Tutor 在显著提升学生参与度、知识保留率以及整体学习成果方面表现出色。该研究不仅通过大规模真实数据验证了强化学习在教育领域的巨大潜力，还为未来开发自适应、具人文关怀的智能教育系统提供了重要的理论与实践参考。

📌 Summary

Online education provides global accessibility but frequently struggles with low student engagement and high dropout rates. To overcome these limitations, this paper introduces AI-Tutor, a novel reinforcement learning-based model designed to foster sustainable learning. By drawing on cognitive theory to balance knowledge acquisition with review in the short term, and modeling learner engagement to sustain motivation in the long term, AI-Tutor delivers personalized, human-centered guidance. Empirical results from 23 million learning records demonstrate that AI-Tutor significantly improves student engagement, knowledge retention, and overall learning outcomes.

📄 Metadata

  • arXiv ID: arXiv:2608.11245
  • Primary Subject: Artificial Intelligence (cs.AI)
  • Secondary Subjects: Computers and Society (cs.CY); Machine Learning (cs.LG)
  • Submission Date: August 2, 2026
  • Authors: Chaofan Zhai, Yicheng Song, Ravi Bapna, Junyao Ye

📄 Metadata

  • arXiv ID: arXiv:2608.11245
  • Primary Subject: Artificial Intelligence (cs.AI)
  • Secondary Subjects: Computers and Society (cs.CY); Machine Learning (cs.LG)
  • Submission Date: August 2, 2026
  • Authors: Chaofan Zhai, Yicheng Song, Ravi Bapna, Junyao Ye

👥 Authors

  • Chaofan Zhai
  • Yicheng Song
  • Ravi Bapna
  • Junyao Ye

👥 Authors

  • Chaofan Zhai
  • Yicheng Song
  • Ravi Bapna
  • Junyao Ye

🔍 Abstract

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及性，但它往往受到参与度低和长期学习效果差的困扰。为了解决这些挑战，我们引入了 AI-Tutor，这是一个基于强化学习的模型，旨在通过优化短期和长期学习成果来促进可持续学习。

在短期内，AI-Tutor 借鉴认知理论，引导学习者平衡新知识的获取与对先前学习的巩固。从长期来看，它对学习者参与度进行建模，以指导维持动力和减少辍学的策略。这些增强功能使 AI-Tutor 能够提供个性化指导，从而促进有效学习和持续参与。

对来自 33,700 名学习者的 2300 万条学习记录进行的实证评估表明，AI-Tutor 在参与度、知识保留和最终学习成果方面一直优于最先进的基线。学习路径分析进一步揭示了 AI-Tutor 如何针对具有不同特征的学习者调整其策略，从而提供自适应且以人为本的支持。

🔍 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.

📚 Citations & References

📚 Citations & References