DEVELOPMENT OF AN AI-ASSISTED ADAPTIVE LEARNING SYSTEM FOR HIGHER EDUCATION

This project introduces an AI-Assisted Adaptive Learning System designed to overcome the limitations of traditional, one-size-fits-all Learning Management Systems (LMS) in higher education. The system leverages a transparent, rule-based recommendation engine to provide a personalized learning experience tailored to individual student profiles, including proficiency level, learning style, and interests. The primary objectives include conceptualizing an integrated framework for an LMS-based adaptive system, analyzing the importance of user personae features, and developing an efficient content-based recommender model. The research methodology involves a comprehensive literature review, an integrated ADDIEScrum development framework, and the implementation of a system architecture built on the MERN (MongoDB, Express.js, React.js, Node.js) technology stack. The motivation stems from addressing the critical need for personalized education to improve student engagement, comprehension, and academic outcomes. The study contributes a novel model for adaptive learning and provides a scalable framework for integrating AI-driven personalization into existing educational infrastructures.