Transforming Mentorship: An AI Powered Chatbot Approach to University Guidance
This provides on-demand mentorship for students at BRAC University, but it is incremental as it applies existing AI methods to a specific educational context.
The paper tackles the lack of personalized guidance for university students by developing an AI-powered chatbot that uses a hybrid retrieval approach and LLaMA-3.3-70B for responses, achieving a BERTScore of 0.831 and METEOR score of 0.809, with an efficient data pipeline taking 106.82 seconds for updates.
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.