Machine Learning in Education

This narrative review examines the historical evolution, current applications, and major challenges of machine learning (ML) in education, positioning ML as a transformative yet deeply contested force in contemporary teaching and learning. Tracing developments from early computer-assisted instruction and intelligent tutoring systems to contemporary deep learning, natural language processing, and generative AI, the review shows how these technologies have expanded education’s capacity for personalization, prediction, automation, content generation, and large-scale data-driven decision-making. It synthesizes evidence across key domains, including student performance prediction, early warning systems, adaptive learning, intelligent tutoring, automated assessment, learning analytics, curriculum design, and inclusive education. In addition, the review critically highlights persistent limitations and risks, particularly algorithmic bias, data privacy concerns, limited interpretability, uneven pedagogical value, infrastructure constraints, and the disruption of conventional assessment by generative AI. Rather than treating ML as a purely technical innovation, the paper argues that its edu