Efficient chatbot for university admission consultation using large language models

This paper presents an Artificial Intelligence–driven chatbot for university admission consultation using Large Language Models (LLMs). The system integrates semantic retrieval with Retrieval-Augmented Generation (RAG) and employs a hybrid strategy that combines vector similarity and keyword matching to provide accurate and context-aware answers. The chatbot was trained on admission FAQs, official documents, and consultation records from Dong A University, ensuring relevance to real user needs. Implementation leverages efficient prompt construction and memory management to support interactive and personalized responses. Experimental results show improved retrieval precision and practical benefits in reducing staff workload and offering consistent support to prospective students. Current limitations include the use of a single-university dataset and a technical evaluation focused on retrieval metrics. Future work will expand to multi-institution data, user studies, and multilingual or voice-enabled interaction to enhance generalizability and real-world impact.