The Transformative Journey of Artificial Intelligence Toward Personalized Learning

This study examines the current state of artificial intelligence (AI) in education and its potential to enhance personalized learning. An integrative literature review, following PRISMA guidelines, analyzed 49 highly cited articles from Scopus and ScienceDirect, published post-2018. Key applications of AI in personalized learning include: AI tutoring systems, learning outcome prediction, assessment support, skill/need identification, teaching activity management, and adaptive content delivery. While these applications offer benefits, challenges such as algorithmic biases, over-automation, data privacy concerns, and the need for pedagogical alignment were identified. The review underscores the transformative potential of AI when integrated ethically and coherently into instructional models, supported by strong data governance. It offers a comprehensive perspective on AI’s role in personalized learning, highlighting the need for multi-stakeholder collaboration, continuous validation, inclusive design, and prioritization of student-centered learning. Emerging areas like affective computing and transparent AI architectures are also discussed as future frontiers in this field.