What question did this study set out to answer?

The aim is to develop an intelligent university assistant that accurately responds to student queries using verified institutional information.

April 15, 2026Open Access

UNI ASSIST-AI: An Intelligent University Assistant Chatbot with LLM, RAG, and Multimodal Capabilities

Key Points

The aim is to develop an intelligent university assistant that accurately responds to student queries using verified institutional information.
Developed a chatbot utilizing Retrieval-Augmented Generation (RAG) methodology.
Integrated a semantic vector retrieval pipeline with a GPT-based generative model.
Enabled multimodal input capabilities for text, voice, and image queries.
Evaluated the system using precision, recall, and F1-scores against rule-based and vanilla LLM systems.
Achieved a Precision of 0.87, indicating high accuracy in relevant responses.
Obtained a Recall of 0.84, demonstrating good ability to identify relevant information.
Reached an F1-score of 0.85, reflecting a balance between precision and recall.

Abstract

University support systems are under increasing pressure to handle high volumes of student queries accurately and at scale. This paper presents UNI ASSIST-AI, a Retrieval-Augmented Generation (RAG)-based intelligent university assistant that grounds every generated response in verified institutional knowledge. The system integrates a semantic vector retrieval pipeline with a GPT-based generative model, and extends it with multimodal input capabilities—supporting text, voice (via ASR), and image (via OCR) queries. Experimental evaluation yields a Precision of 0.87, Recall of 0.84, and F1-score of 0.85, outperforming both rule-based and vanilla LLM baselines.

UNI ASSIST-AI: An Intelligent University Assistant Chatbot with LLM, RAG, and Multimodal Capabilities

Key Points

Abstract

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