This systematic literature review explores the drivers of personalized learning in the context of artificial intelligence (AI) and Education 4.0. The study aims to identify key factors that enhance personalized learning and assess the role of AI in reinforcing these drivers. A comprehensive search across major peer-reviewed journals resulted in 102 relevant studies published between 2013 and 2022. The review highlights several critical drivers, including recognizing individual student characteristics, personalizing content delivery and instructional methods, customizing assessment and feedback mechanisms, and tailoring user interfaces and learning environments. AI is identified as a transformative catalyst, offering capabilities such as automated learner profiling, adaptive content recommendation, real-time assessment, and intelligent user interfaces. However, the successful adoption of AI in personalized learning requires addressing challenges such as developing educator competencies, refining theoretical frameworks, and navigating ethical considerations. The review provides valuable insights for educators, researchers, and policymakers, emphasizing the potential of AI to enhance personalized learning experiences and maximize individual potential in the digital economy.This systematic literature review explores the drivers of personalized learning in the context of artificial intelligence (AI) and Education 4.0. The study aims to identify key factors that enhance personalized learning and assess the role of AI in reinforcing these drivers. A comprehensive search across major peer-reviewed journals resulted in 102 relevant studies published between 2013 and 2022. The review highlights several critical drivers, including recognizing individual student characteristics, personalizing content delivery and instructional methods, customizing assessment and feedback mechanisms, and tailoring user interfaces and learning environments. AI is identified as a transformative catalyst, offering capabilities such as automated learner profiling, adaptive content recommendation, real-time assessment, and intelligent user interfaces. However, the successful adoption of AI in personalized learning requires addressing challenges such as developing educator competencies, refining theoretical frameworks, and navigating ethical considerations. The review provides valuable insights for educators, researchers, and policymakers, emphasizing the potential of AI to enhance personalized learning experiences and maximize individual potential in the digital economy.