Authors: Rithica B, Sai Srija R
Abstract: Personalized learning has emerged as a vital component of modern educational systems due to the diversity in students’ learning abilities, preferences, and academic backgrounds. Traditional e-learning platforms often provide uniform learning content to all learners, which fails to address individual needs and learning gaps. This limitation results in reduced student engagement and suboptimal learning outcomes. To overcome these challenges, this paper proposes an AI-driven personalized learning recommendation system that dynamically suggests learning materials, assessments, and learning paths tailored to individual students. The proposed system utilizes machine learning techniques to analyze learner profiles, historical academic performance, learning behavior, and preferences. Based on these parameters, intelligent recommendations are generated to support adaptive and learner-centric education. Experimental evaluation demonstrates that the proposed system significantly improves student engagement, learning efficiency, and academic performance when compared with conventional learning management systems. The findings highlight the effectiveness of artificial intelligence in transforming digital education into a personalized and adaptive learning environment. Personalized learning has emerged as a vital component of modern educational systems due to the diversity in students’ learning abilities, preferences, and academic backgrounds. Traditional e-learning platforms often provide uniform learning content, resulting in reduced engagement and limited academic effectiveness. This paper proposes an AI-driven personalized learning recommendation system that utilizes learning analytics and machine learning techniques to analyze learner profiles, academic performance, and behavioral patterns.
