AI-Driven Personalized Learning: Enhancing Student Engagement and Academic Performance

Abstract

This research explores the impact of AI-driven personalized learning systems on enhancing student engagement and improving academic performance. By utilizing machine learning algorithms such as Reinforcement Learning (RL) and Collaborative Filtering, the study investigates how AI can provide tailored educational experiences that meet the diverse needs and preferences of individual students. Data collected from several higher education institutions shows that AI-powered systems significantly increase student engagement, improve academic performance, and reduce dropout rates by offering real-time feedback and personalized learning pathways. Despite its potential, the study addresses the challenges of data privacy, algorithmic bias, and the need for ethical considerations in AI deployment. The findings highlight AI's transformative role in modern education, providing scalable solutions to support student success and retention.