Reinforcement Learning: Educational Approaches in Distance Education

Abstract

Distance education has progressed from correspondence courses to interactive digital platforms, yet challenges such as limited personalization, low engagement, and insufficient real-time support persist. Reinforcement Learning (RL), as part of Artificial Intelligence (AI), offers unique potential to address these issues by modeling learning as a feedback-driven, sequential decision-making process. Unlike supervised and unsupervised methods, RL agents adapt dynamically by receiving rewards or penalties, making them well-suited for simulation-based, learner-centered environments. This study reviews applications of RL in distance education, covering various domains. It highlights the role of RL in optimizing learning paths, personalizing instruction through learner profiles, and enabling adaptive recommendations with algorithms like Q-learning. By integrating RL with hybrid AI approaches, distance education can become more responsive, engaging, and effective, offering scalable solutions for diverse learners.