Reinforcement Learning for Personalised Education

Abstract

The application of machine learning (ML) in education has significantly enhanced the potential for personalised learning experiences. Among various ML approaches, reinforcement learning (RL) has emerged as a powerful tool for developing adaptive education systems tailored to individual student needs. Personalised learning pathways, driven by RL, leverage student behaviour, performance, and preferences to create customised educational trajectories. This paper reviews the state-of-the-art in applying reinforcement learning to adaptive education systems, exploring how RL models are trained to make decisions that optimise learning outcomes. We analyse key research trends, challenges, and opportunities in designing RL-based systems that cater to diverse student populations. Moreover, we highlight case studies where RL has demonstrated the ability to foster student engagement, improve retention rates, and enhance overall learning effectiveness. Finally, we discuss future directions, emphasising ethical considerations, scalability, and the integration of RL with other AI technologies to revolutionise education.