Artificial Intelligence in Education: A Brief Narrative Review

Abstract

Artificial intelligence (AI) has shifted from a peripheral research interest to a central concern in contemporary education, a movement greatly accelerated by the public release of generative AI tools after 2022. This brief narrative review synthesises empirical and conceptual literature on artificial intelligence in education (AIEd), tracing its development from early intelligent tutoring systems to present-day large language models, and organising the field around its principal application areas: intelligent tutoring and personalised instruction, adaptive learning and learning analytics, automated assessment and feedback, conversational and generative tools, and support for teachers and administration. Evidence from several meta-analyses indicates that well-designed intelligent tutoring systems produce moderate positive effects on learning, though they remain less effective than expert human tutoring; evidence on generative AI is more recent and mixed. Across the literature, recurring challenges concern academic integrity, data privacy, algorithmic bias, equity and the digital divide, and over-reliance. The review argues that AI is most defensibly understood as an augmentation of skilled teaching rather than a substitute for it, and that realising its benefits depends less on the technology itself than on pedagogy, governance, and investment in human capabilities.