Optimization of Personalized Learning Paths in Educational AI Driven by Student Behavior Data

Abstract

With the advancement of artificial intelligence (AI) and data-driven methodologies, personalized learning has emerged as a transformative approach to education, enabling tailored instruction based on individual student needs. Distance learning has its own pros like the overall experience of students being humble and getting more humble and connected to learning even outside of their home grounds but this seems to take away one of our cardinal principles of optimizing the classrooms by improving what we have. Education systems were historically built around the one-size-fits-all learning model, a rigid structure that cannot account for the variation between students as learners and thus requires an AI-driven approach that customizes learning sequences on-the-fly. We propose a new framework that combines machine learning, reinforcement learning, and behavior analytics to optimize personalized learning paths. Using deep learning methods on a massive amount of data about student interactivity, our model utilizes prior knowledge gained, active participation, and performance on evaluations to restructure tertiary learning sequence. A reinforcement learning agent further enhances path adaptation by continuously optimizing instructional strategies according to real-time student feedback. Our experimental results, conducted on a large-scale educational dataset, demonstrate significant improvements in student engagement, knowledge retention, and learning efficiency. We discuss key challenges such as data sparsity, computational constraints, while outlining future research directions for more robust and interpretable AI-driven learning path optimization.