AI-DRIVEN PERSONALIZED LEARNING PATHWAYS: TRANSFORMING EDUCATIONAL OUTCOMES THROUGH ADAPTIVE CONTENT DELIVERY SYSTEMS

Abstract

This study presents a comprehensive AI-driven personalized learning system that transforms educational outcomes through adaptive content delivery mechanisms. We developed and evaluated an intelligent learning platform that leverages machine learning algorithms to create individualized learning pathways for diverse student populations. The platform utilizes student clustering, prediction algorithms of performance, and content recommendation systems to provide individualized learning experiences. Our empirical test with 500 students, 100 units of content, and 5,000 learning interactions shows significant educational outcome gains. The AI system attained 78.5% completion prediction accuracy and low prediction error of performance (MSE = 0.0112). Most importantly, students who utilized the personalized learning paths saw a mean 11.7% improvement in performance and a 6.3% increase in completion rates compared to the conventional means of learning. The platform successfully segmented four learner clusters, allowing for targeted intervention on underachieving students, average ones, high-achieving ones, and fast learners. Our findings provide compelling evidence that AI-driven personalization can forcefully counteract the inadequacies of one-size-fits-all education, with great potential for the advancement of educational equity and learning efficiency.