International Journal of Science, Engineering and TechnologyAuthors: Mohd Abdulla, Navanish Mehta, Abdulla Ansari, Saif Ali Abhishek Tyagi
Abstract: Artificial Intelligence technologies are increasingly being adopted in educational platforms to improve learning accessibility and interactive learning support. Traditional Learn-ing Management Systems (LMS) primarily focus on content delivery and administrative management but often lack adaptive learning capabilities and contextual educational assistance. This paper presents the design and development of a scalable AI-driven Learning Management System that integrates Retrieval-Augmented Generation (RAG), LangChain, and Vector Database technologies to enhance personalized education and student engagement. The proposed system allows users to upload educational documents in PDF format and interact with the content through AI-powered features such as contextual question answering, automatic flashcard generation, intelligent quiz creation, and personalized study assistance. The system utilizes MERN stack architecture for scalable full-stack development, JWT and Google OAuth for secure authentication, MongoDB Atlas Vector Search for semantic retrieval, and LangChain for orchestrating AI workflows. A Flutter-based mobile application is developed using clean architecture and Provider state management to ensure modu-larity, scalability, and maintainability. The implemented RAG pipeline improves contextual accuracy by retrieving relevant document embeddings before generating responses from the Large Language Model (LLM). Experimental analysis demon-strates improved learning accessibility, efficient revision support, and enhanced user interaction compared to traditional LMS platforms. The developed system demonstrates the practical use of AI-assisted educational platforms for improving student interaction, revision efficiency, and contextual learning support.
