Transforming Mentorship: An AI Powered Chatbot Approach to University Guidance

TL;DR

This paper introduces an AI-powered chatbot designed to provide personalized, on-demand guidance to university students by integrating data processing, semantic retrieval, and large language models to enhance student support services.

Contribution

It presents a novel hybrid retrieval approach combined with a large language model to deliver personalized university guidance through an efficient data pipeline.

Findings

High semantic relevance of chatbot responses (BERTScore 0.831, METEOR 0.809)

Efficient data update process (106.82 seconds vs. 368.62 seconds)

Potential to improve student support and planning at universities

Abstract

University students face immense challenges during their undergraduate lives, often being deprived of personalized on-demand guidance that mentors fail to provide at scale. Digital tools exist, but there is a serious lack of customized coaching for newcomers. This paper presents an AI-powered chatbot that will serve as a mentor for the students of BRAC University. The main component is a data ingestion pipeline that efficiently processes and updates information from diverse sources, such as CSV files and university webpages. The chatbot retrieves information through a hybrid approach, combining BM25 lexical ranking with ChromaDB semantic retrieval, and uses a Large Language Model, LLaMA-3.3-70B, to generate conversational responses. The generated text was found to be semantically highly relevant, with a BERTScore of 0.831 and a METEOR score of 0.809. The data pipeline was also very efficient, taking 106.82 seconds for updates, compared to 368.62 seconds for new data. This chatbot will be able to help students by responding to their queries, helping them to get a better understanding of university life, and assisting them to plan better routines for their semester in the open-credit university.