This systematic literature review explores the key drivers of personalized learning in the context of artificial intelligence (AI) and Education 4.0. The study analyzed 102 relevant studies published between 2013 and 2022, identifying critical factors that contribute to the effectiveness of personalized learning. These drivers include recognizing individual student characteristics, personalizing content delivery and instructional methods, customizing assessment and feedback mechanisms, tailoring user interfaces and learning environments, and leveraging AI technologies. AI offers significant opportunities to enhance personalized learning through 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 educators' competencies, refining theoretical frameworks, and navigating ethical considerations related to data privacy and bias. The review highlights the importance of AI in enhancing personalized learning by providing tailored educational experiences that maximize individual potential and relevance in the digital economy. It also emphasizes the need for further research to address the remaining challenges and unexplored opportunities in AI-driven personalized learning. The findings underscore the transformative potential of AI in education, offering unprecedented capabilities in learner profiling, adaptive content delivery, real-time feedback, and intelligent interfaces. This aligns with the vision of creating digital spaces or classrooms conducive to personalized learning, leveraging generative artificial intelligence to tailor instruction around core concepts, principles, and skills. The study concludes that AI has the potential to revolutionize education by enabling personalized learning experiences that cater to diverse learner needs, preferences, and competencies.This systematic literature review explores the key drivers of personalized learning in the context of artificial intelligence (AI) and Education 4.0. The study analyzed 102 relevant studies published between 2013 and 2022, identifying critical factors that contribute to the effectiveness of personalized learning. These drivers include recognizing individual student characteristics, personalizing content delivery and instructional methods, customizing assessment and feedback mechanisms, tailoring user interfaces and learning environments, and leveraging AI technologies. AI offers significant opportunities to enhance personalized learning through 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 educators' competencies, refining theoretical frameworks, and navigating ethical considerations related to data privacy and bias. The review highlights the importance of AI in enhancing personalized learning by providing tailored educational experiences that maximize individual potential and relevance in the digital economy. It also emphasizes the need for further research to address the remaining challenges and unexplored opportunities in AI-driven personalized learning. The findings underscore the transformative potential of AI in education, offering unprecedented capabilities in learner profiling, adaptive content delivery, real-time feedback, and intelligent interfaces. This aligns with the vision of creating digital spaces or classrooms conducive to personalized learning, leveraging generative artificial intelligence to tailor instruction around core concepts, principles, and skills. The study concludes that AI has the potential to revolutionize education by enabling personalized learning experiences that cater to diverse learner needs, preferences, and competencies.