Abstract

This study systematically examines the evolution of personalized learning in education, focusing on the transformative potential of artificial intelligence (AI) in higher education. It aims to identify how AI addresses the barriers of traditional teacher-led and student-driven personalization methods, offering scalable and adaptive solutions to enhance individualized learning experiences. A systematic review was conducted using the Scopus database, analyzing 55 peer-reviewed published articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework guided the methodology, ensuring a rigorous approach to selecting and analyzing studies. Data extraction emphasized two guiding questions: the evolution of personalized learning and the methods employed to achieve it. Qualitative and quantitative analyses were employed to categorize findings and map trends over time. The review highlights a significant shift from traditional teacher-led models to technology-driven approaches in personalized learning. AI emerges as a pivotal tool, offering real-time data analysis, adaptive learning environments, and enhanced student autonomy. Despite challenges such as implementation costs and scalability, AI-driven personalization demonstrates the potential to overcome limitations in traditional education systems, particularly in higher education. This study uniquely bridges the gap between pedagogical approaches and technological advancements, showcasing AI's capability to revolutionize learning personalization. It provides actionable insights for educators and institutions aiming to implement effective, scalable, and inclusive educational strategies.