E-Learning Platform with AI-Based Recommendations

Abstract: The rapid expansion of e-learning platforms has significantly transformed the educational landscape by offering flexible and accessible learning opportunities. However, the overwhelming volume of digital content presents challenges in identifying resources tailored to individual learner needs. Artificial Intelligence (AI)-driven recommender systems have emerged as effective tools for delivering personalized learning experiences. This paper provides a comprehensive analysis of key AI-based recommendation models, including content-based filtering, collaborative filtering, hybrid approaches, and reinforcement learning. Through literature synthesis and experimental validation, we examine how these models enhance learner engagement, adaptability, and outcomes. Additionally, we explore common challenges such as the cold-start problem, data sparsity, bias, and privacy concerns. Our evaluation results highlight the superior performance of hybrid models in balancing accuracy and personalization. The paper concludes with proposed future directions, including the integration of explainable AI and fairnessaware algorithms to ensure transparency and inclusivity in educational recommendations. These insights contribute to the development of intelligent, learner-centric e-learning systems.