Integration of Generative AI in Mern Stack for Personalized E-Learning Platform

Abstract: - In the age of artificial intelligence, e-learning platforms are moving beyond static course delivery to become intelligent, adaptive, and user-centred learning environments. This paper presents an AI-powered e-learning platform that offers a role-based system designed for both students and teachers. The student dashboard provides personalized learning paths, course recommendations, and automated feedback through machine learning and natural language processing techniques. The teacher dashboard enables course management and performance tracking through data-driven insights. It includes interactive, graph-based visualizations—one depicts student enrolment trends in courses and another analyses the number of lectures uploaded by instructors in specific subjects. The proposed framework integrates deep learning models, transfer learning, and explainable AI to enhance personalization, transparency, and engagement. Attention-based mechanisms and learning analytics are used to monitor learners' progress and improve the relevance of content. Experimental validation will be conducted using benchmark educational datasets and real user interactions to ensure reliability, scalability, and ethical AI use. The overall goal is to develop a smart, transparent, and inclusive e-learning system that empowers both teachers and learners through data-driven insights. Keywords: AI in education, personalized e-learning, adaptive learning, role-based dashboards, deep learning, transfer learning, explainable AI, learning analytics, data visualization