AI-Powered Adaptive Learning: Personalizing Education for Improved Student Outcomes

Abstract

AI-driven adaptive learning systems evaluate students' learning behaviours, strengths, and deficiencies to provide tailored teaching materials, enhancing engagement and academic achievement. The article examined the influence of AI-driven adaptive learning on student results, emphasizing its function in accommodating varied learning requirements, diminishing knowledge disparities, and augmenting student motivation. It analyses essential elements of adaptive learning models, including intelligent tutoring systems, predictive analytics, and personalised content delivery, while addressing concerns of data privacy, ethical considerations, and disparities in digital access. The study seeks to elucidate the efficacy of AI-driven learning solutions in enhancing information retention, critical thinking, and overall academic achievement through an examination of empirical and case studies from diverse educational contexts. The key areas for focus include ethics, teacher training, AI adaptability, and policy integration to maximize AI’s educational benefits.