ASEMOTA PATRICK EDENTANLEN

This thesis presents the design and development of an Adaptive Learning Support System that leverages real-time learner analytics, intelligent recommendation techniques, and Explainable Artificial Intelligence (XAI) to enhance personalized education delivery. The proposed system integrates several interconnected modules—including data acquisition, learner state monitoring, adaptive content delivery, personalized recommendations, intelligent interventions, and teacher in-the-loop support—to create a responsive learning environment capable of adjusting to each learner’s unique needs. By analyzing behavioural patterns, content interactions, assessment performance, and contextual factors, the system dynamically recommends suitable learning materials while providing transparent explanations of its decisions. The intelligent services layer ensures scalability, interoperability, and continuous optimization across modules. Overall, the system aims to improve learner performance, engagement, and instructional efficiency, offering a robust and modern approach to adaptive education grounded in computational intelligence, machine learning, and human-centered design principles.