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.
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DHRUV ATTREY
Parul University
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DHRUV ATTREY (Mon,) studied this question.
www.synapsesocial.com/papers/69df2cb9e4eeef8a2a6b1f19 — DOI: https://doi.org/10.5281/zenodo.19552313