AI-Powered Study Assistant Using Retrieval-Augmented Generation (RAG).

Artificial Intelligence (AI) has emerged as a transformative technology in the field of education by enabling intelligent systems that support personalized and interactive learning experiences. With the increasing availability of digital learning resources such as textbooks, lecture notes, research papers, and study guides, students often face difficulties in efficiently locating relevant information and understanding large volumes of educational content. Traditional search-based learning methods are time-consuming and frequently fail to provide contextually relevant results, reducing learning efficiency and knowledge retention. The primary problem addressed in this research is the challenge of retrieving accurate and meaningful information from extensive educational documents. Conventional educational platforms and keyword-based search systems often lack the ability to understand the context of user queries, resulting in irrelevant search outcomes and increased effort for learners. Additionally, standalone AI systems may generate responses that are not grounded in the user’s study materials, affecting the reliability of the information provided. To overcome these limitations, this project proposes an AI-Powered Study Assistant Using Retrieval-Augmented Generation (RAG). The proposed system combines semantic document retrieval with Generative Artificial Intelligence to provide accurate, context-aware, and document-based responses. The framework allows users to upload educational documents, perform semantic searches, interact with an AI-powered chatbot, generate summaries, create quizzes and flashcards, manage notes, and monitor learning progress through analytics. By retrieving relevant information from uploaded study materials before generating responses, the RAG architecture improves the accuracy and reliability of educational assistance. The developed system demonstrates effective document-based question answering, intelligent information retrieval, automated study material generation, and personalized learning support. The results indicate that the proposed approach reduces information search time, improves learning efficiency, enhances knowledge retention, and provides an engaging educational experience. The system serves as an intelligent learning platform that bridges the gap between information retrieval and knowledge generation in modern digital education.