AGENTIC AI-BASED INTELLIGENT STUDY ASSISTANT USING LL MS AND VECTOR DATABASES

Abstract

By introducing intelligent and automated learning solutions, artificial intelligence (AI) has transformed contemporary educational systems. An Agentic AI-Based Intelligent Study Assistant is presented in this study. It makes use of Vector Databases and Large Language Models (LL Ms) to provide students with intelligent, context-aware, and individualized academic assistance. Natural language interaction is used to answer questions, summarize study materials, make notes, and help students learn more quickly with the proposed system. The primary focuses of the research are the creation and implementation of an intelligent system that is able to comprehend user input, retrieve relevant information through vector-based semantic search, and generate precise responses through advanced AI models. By combining the capabilities of language generation and external knowledge retrieval, the integration of Retrieval-Augmented Generation (RAG) techniques enhances the relevance and quality of responses. The system architecture includes components such as user interface, Agentic workflow, embedding models, vector database, and LLM integration. The model that has been proposed aims to make education more adaptable, to make learning easier, and to make students more productive. According to experimental analysis, the intelligent assistant performs better than conventional keyword-based systems in terms of response accuracy, contextual understanding, and user interaction. This study demonstrates how intelligent assistants can support contemporary learning environments through automation, personalization, and effective knowledge retrieval, as well as the growing role Agentic AI systems are playing in education.