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Impact Factor : 8.14

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Paper Title: AI Tutor: A Domain-Specific Teaching Bot Powered by Retrieval-Augmented Generation and LLMs
Authors Name: Suraj Raykar , Ayush Hande , Himanshu Kapure , Prof. Ishwar Bharambe
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Published Paper Id: IJRTI2606011
Published In: Volume 11 Issue 6, June-2026
DOI:
Abstract: We present an AI-powered personalized tutoring system that integrates Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) to deliver domain-specific educational content. The proposed framework uses a curated knowledge base of textbooks, examples, and solved problems and a vector-retrieval engine to ground an LLM’s responses in authoritative sources. By dynamically retrieving relevant information at query time, the system avoids the hallucination problems of standalone LLMs and remains up-to-date without costly retraining. A multi-layer architecture combines a semantic search over a vector database, prompt engineering, and student modeling to produce personalized, step-by-step explanations at an appropriate difficulty level. Preliminary analysis shows the RAG-enhanced tutor achieves higher answer accuracy and factual consistency than an unaugmented LLM. We describe the system design, data flow, and implementation details, and discuss advantages such as transparency (traceable sources) and scalability. The result is an intelligent tutor that provides adaptive, accurate instruction for diverse learners, error. The proposed AI tutor implements an integrated architecture comprising domain knowledge modeling, student performance tracking, adaptive tutoring strategies, and an intuitive conversational interface. By synthesizing question-answer pairs from domain-specific educational resources, the system builds a comprehensive knowledge library that can be continuously updated without retraining the underlying LLM. Through real-time assessment and personalized feedback mechanisms, the tutor provides targeted guidance tailored to each student's learning gaps and strengths, delivering step-by-step problem-solving assistance at an optimal difficulty level. This intelligent tutoring system aims to reduce educational costs while improving learning outcomes and accessibility, making high-quality domain-specific education available to diverse learners regardless of geographic location or resource constraints. The implementation demonstrates how RAG-LLM technology can be effectively deployed in educational contexts to create sustainable, scalable, and highly personalized learning solutions.
Keywords: Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Intelligent Tutoring System, Personalized Learning, Educational AI, Semantic Search, Conversational AI, Adaptive Learning, Vector Database, AI Tutor
Cite Article: "AI Tutor: A Domain-Specific Teaching Bot Powered by Retrieval-Augmented Generation and LLMs", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 6, page no.a98-a108, June-2026, Available :http://www.ijrti.org/papers/IJRTI2606011.pdf
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2606011
Registration ID:212815
Published In: Volume 11 Issue 6, June-2026
DOI (Digital Object Identifier):
Page No: a98-a108
Country: Pune, Maharashtra, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2606011
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2606011
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

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