ADAPTIVE REINFORCEMENT LEARNING FRAMEWORK FOR PERSONALIZED EDUCATIONAL TRAJECTORIES IN AI-DRIVEN HIGHER EDUCATION SYSTEMS

Abstract

In the rapidly evolving landscape of digital higher education, personalized learning has become a critical component in enhancing student performance and engagement. Traditional self-paced learning systems, while flexible, often lack dynamic adaptability and fail to respond effectively to real-time student behavior. This study proposes an Adaptive Reinforcement Learning (ARL) framework designed to optimize individualized learning trajectories in AI-driven educational environments.

The proposed model conceptualizes the learning process as a Markov Decision Process (MDP), where student knowledge states, learning actions, and reward mechanisms interact dynamically. By employing Q-learning algorithms, the system continuously refines its decision-making strategy based on student performance, engagement, and retention metrics. Unlike static rule-based systems such as Fuzzy Logic, the ARL framework enables continuous learning and autonomous adaptation.

Experimental simulations indicate that the proposed approach significantly improves learning efficiency, reduces cognitive overload, and enhances long-term knowledge retention. This research demonstrates the transformative potential of reinforcement learning in developing next-generation intelligent tutoring systems and adaptive educational platforms.