AI-Driven Personalization and Adaptive Learning Environments
Abstract
Artificial intelligence is reshaping higher education by enabling personalized, adaptive learning environments. This chapter examines how AI-driven personalization, combined with digital twin technologies, creates dynamic ecosystems that simulate and enhance individual learning journeys. Drawing on contemporary literature, it explores key mechanisms such as intelligent tutoring systems, adaptive content delivery, open learner models, and large language model applications. It highlights digital twins as persistent learner representations supporting continuous simulation and feedback. The chapter also addresses ethical concerns, including data privacy, algorithmic bias, and equitable access, while proposing a framework for sustainable, lifelong AI-powered learning. Practical implications for educators, policymakers, and technology developers are outlined, along with key directions for future research in this evolving interdisciplinary field.