Artificial Intelligence-Driven Personalized Learning: Application, Challenges, and Future Directions in Digital Education

Abstract

With the rapid advancement of digital technologies, artificial intelligence (AI) has emerged as a transformative force in reshaping digital education. Personalized learning, as a core objective of modern educational reform, has been significantly empowered by AI technologies. This study explores the application of AI-driven personalized learning in digital education, analyzes the key challenges in its implementation, and proposes potential future directions. By reviewing relevant literature and case studies, the research identifies four major application dimensions of AI in personalized learning: adaptive learning systems, intelligent learning analytics, personalized content recommendation, and intelligent tutoring systems. It also reveals critical challenges including data privacy and security risks, technical accessibility gaps, teacher-AI collaboration barriers, and ethical dilemmas. Finally, the study suggests future directions such as strengthening interdisciplinary research, optimizing AI algorithm fairness, promoting inclusive AI education, and establishing standardized evaluation frameworks. This research provides valuable insights for educators, policymakers, and technology developers to promote the healthy and sustainable development of AI-driven personalized learning in digital education.