Towards Sustainable Learning in Online Education: A Reinforcement Learning Approach

Research introduces 'AI-Tutor', a reinforcement learning model aimed at enhancing online education by fostering sustainable learning. This model addresses common challenges in online learning, specifically low engagement and poor long-term learning effectiveness, by optimizing both short-term outcomes through cognitive theory integration and long-term engagement to sustain motivation and reduce dropout.

Why it matters

Addressing low engagement and poor long-term learning outcomes in online education is critical for maximizing its potential benefits. This research proposes an AI-driven approach to personalize learning paths, which could significantly improve the efficacy and retention rates in digital learning platforms globally.

Key insights

• Online education provides significant scalability and accessibility but struggles with learner engagement and long-term learning effectiveness.
• The 'AI-Tutor' model utilizes reinforcement learning to optimize learning outcomes.
• Short-term optimization in AI-Tutor focuses on balancing new knowledge acquisition with prior learning reinforcement, guided by cognitive theory.
• Long-term optimization within AI-Tutor models learner engagement to sustain motivation and reduce dropout rates.
• AI-Tutor aims to provide personalized guidance to promote effective and sustainable learning in online environments.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.11245