Personalized Learning Through AI-Driven Adaptive Content Delivery Mode: A Reinforcement Learning and NLP-Based Framework

Abstract

"Personalized Learning through AI-Driven Multimodal Adaptive Systems: A Reinforcement Learning Approach with Real-Time Feedback" presents a comprehensive study on designing and implementing an AI-powered adaptive learning system. The system dynamically personalizes not just the content but also the mode of delivery—text, video, or gamified— using reinforcement learning (RL), multimodal behavioural analytics, and natural language processing (NLP). It integrates real-time data streams such as click logs, gaze tracking, and student performance to tailor the learning experience, addressing the diverse preferences and needs of learners in online education environments. Traditional adaptive learning systems focus primarily on performance metrics and lack dynamic content mode adaptation or personalized feedback mechanisms. This research addresses those limitations by developing a system that uses Deep Q-Networks to recommend optimal content delivery modes and BERT-based NLP models for personalized, real-time feedback. It also integrates ethical AI safeguards, including bias mitigation, data anonymization, and explainability through SHAP values. Chapter 1 introduces the challenges in current e-learning systems, emphasizing the need for adaptive delivery modes and personalized feedback to enhance engagement and retention. Chapter 2 reviews recent research across six themes: adaptive AI systems, real-time assessment, delivery mode effectiveness, student engagement, dynamic adaptation, and ethical considerations, identifying gaps that inform the thesis objectives. Chapter 3 outlines the problem statement, research questions, and objectives, framing the need for a scalable and ethical AI-based learning system. Chapter 4 presents the methodology, including dataset integration from EdNet, OULAD, and ASSISTments; system design using RL and NLP modules; and the technical implementation of feature engineering and content recommendation. Chapter 5 details experimental results and performance analysis, demonstrating the effectiveness of the RL and NLP models. The chapter also uses SHAP visualizations to interpret model decisions and verify transparency and fairness. Chapter 6 concludes with a summary of contributions and discusses the future scope of expanding to real-time deployment, incorporating deeper multimodal analytics, and testing in broader educational contexts.