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

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.