The Future of Classrooms: How Generative AI is Personalizing Student Learning

Abstract

Generative artificial intelligence (AI) is increasingly transforming educational practices by enabling personalized content generation, adaptive instruction, and real-time learning support. This study examines how generative AI contributes to personalized student learning while considering its pedagogical, technological, and socio-technical implications. A systematic narrative literature review was conducted using peer-reviewed literature published between 2021 and 2026 and retrieved from Scopus, Web of Science, Google Scholar, and IEEE Xplore. The selected literature was analyzed thematically according to adaptive learning mechanisms, student engagement and cognitive outcomes, changing educator roles, and ethical challenges, including data privacy and algorithmic equity. The synthesis indicates that generative AI can support personalized learning by adapting explanations, learning materials, and feedback to students' needs, while also creating opportunities for more flexible and learner-centered instruction. However, its educational value is influenced by limitations related to academic integrity, over-reliance on AI, unequal access, data privacy, and the need for appropriate teacher oversight. These findings suggest that effective integration of generative AI requires a balanced human-AI approach in which technological personalization is supported by educator guidance, institutional policies, ethical safeguards, and equitable access.