An Efficient Small Language Model with Retrieval-Augmented Generation for Educational Information Systems

Abstract

Educational institutions maintain a large amount of information in notices, academic regulations, syllabi, faculty profiles, examination instructions, admission documents, and other institutional records. Students and staff often need this information in a simple question-and-answer form, but the information is usually distributed across many documents. Large language models can provide natural conversations, but their size, computing requirements, and tendency to produce unsupported answers make direct use difficult for a college-level information system. This paper presents a practical framework that combines a Small Language Model (SLM) with Retrieval-Augmented Generation (RAG) for answering educational and institution-specific questions. The proposed system keeps the college information in an external knowledge base and retrieves relevant content before generating an answer. This separates the changing college information from the language model and makes the system easier to update. The paper describes the data preparation process, retrieval pipeline, prompt construction, SLM integration, evaluation plan, and possible deployment on limited hardware. The proposed framework is intended for academic institutions that want a conversational information assistant without depending completely on a large hosted language model. The paper also discusses accuracy, relevance, response time, hallucination, privacy, and maintenance as important evaluation areas.