Reinforcement Techniques for Dynamic Adaptive Learning

Abstract

The goal of dynamic adaptive learning systems is to tailor instruction by meeting the needs of each student instantly. In order to enable ongoing modification of instructional content based on student performance and engagement, this study investigates reinforcement learning strategies. The suggested technique dynamically modifies learning strategies and content complexity by modelling learning as an interactive feedback-driven process. When compared to static and rule-based systems, experimental results demonstrate better learning outcomes and engagement, indicating the effectiveness of reinforcement strategies for intelligent and scalable adaptive learning environments.