Personalized Learning with AI: Adapting Education to Learners’ Needs

This chapter explores the transformative role of artificial intelligence (AI) in enabling personalized learning (PL). It draws on the theoretical foundations of constructivism, multiple intelligences, self-determination theory, connectivism, and data-driven decision-making to examine how AI supports learner engagement, autonomy, and self-regulation. It highlights intelligent tutoring systems, recommender engines, emotion-aware tools, and real-time feedback systems, which furnish relevant and adaptable learning pathways. Global case studies – such as Summit Public Schools, Carnegie Learning, Squirrel AI, and David Game College – are discussed to illustrate the effectiveness of these data-driven, human-AI collaboration-based, and scalable adaptive personalized instructional systems. The chapter also addresses ethical, pedagogical, and human-related implications and emphasizes the challenges related to algorithm bias, data privacy, and teacher readiness. Emerging trends, like the integration of AI with extended reality (XR) environments that aim toward a more immersive, experiential, and evidence-based learning, are also discussed.