AI in Personalized Education: A Review of Adaptive Learning Systems and Student Engagement

Abstract

Artificial intelligence is reshaping education by enabling personalized learning experiences, intelligent tutoring systems, and automated assessments. This paper reviews AI-driven adaptive learning models, recommendation algorithms, and sentiment analysis in student engagement. We analyze machine learning applications in curriculum design, early dropout prediction, and learning analytics. Challenges such as algorithmic bias, student data privacy, and the digital divide are discussed. The review concludes with future research directions in AI-powered virtual classrooms and interactive learning experiences.