IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 138 10.51583/IJLTEMAS.2026.150700133 Knowledge An Empirical Study of Knowledge Graph–Augmented Retrieval for Hallucination Mitigation in University Academic Assistance Systems Shivang M. Patel Dr. Centre for Distance and Online Education, Gujarat University, Ahmedabad, Gujarat, India India Nilesh K. Modi Dr. School of Computer Science, Dr. Babasaheb Ambedkar Open University, Ahmedabad, Gujarat, India India 15 7 1768 1774 12 08 2026 17 08 2026 22 08 2026 Knowledge Graph–Augmented Retrieval Hallucination Mitigation Retrieval-Augmented Generation (RAG) Abstract

Large Language Models (LLMs) used in university chatbots often create false or misleading answers—a problem known as "hallucination". When students ask about course prerequisites, credit transfers, examination rules, or graduation policies in open and distance learning systems, standard AI systems frequently confuse similar policies or guess missing details. Standard document-search methods (Vector Retrieval-Augmented Generation or Vector-RAG) help by pulling relevant text passages, but they still struggle when answers depend on interconnected rules spread across multiple handbook pages. We built and tested a Knowledge Graph–Augmented Retrieval (KG-RAG) system. This approach connects traditional keyword/vector search with a structured "knowledge map" (a Knowledge Graph) that explicitly links courses, degree tracks, examination regulations, and prerequisite dependencies. We constructed a real-world multi-institutional benchmark dataset containing 2,850 policy passages compiled from 20 leading open, distance, and online universities globally (including The Open University UK, IGNOU, BAOU, University of London, and ASU Online) along with a structured graph of 3,400 entities and 8,200 relations. We evaluated 220 verified student queries across three systems: a base LLM, a standard Vector-RAG system, and our hybrid KG-RAG framework. The proposed KG-RAG system achieved 92.0% factual accuracy, outperforming standard Vector-RAG (84.0%) and the base LLM (61.0%). Hallucination rates dropped from 16.0% in standard Vector-RAG to just 6.0% in KG-RAG (p<0.001, d=1.42). On complex multi-hop queries requiring multi-step reasoning across academic policies, KG-RAG achieved 87.0% accuracy compared to 74.0% for Vector-RAG and 43.0% for the base LLM. Integrating a structured knowledge map provides deterministic boundaries that stop AI from guessing based on simple word similarity. The system traces exact policy pathways, significantly lowering hallucination risks in higher education administrative support while remaining computationally efficient for institutional deployment.

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