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.

翻译：大学生在本科阶段面临巨大挑战，往往难以获得个性化、按需的指导，而传统导师制难以大规模提供此类支持。现有数字工具虽多，但严重缺乏针对新生的定制化辅导。本文提出一种人工智能驱动的聊天机器人，旨在作为BRAC大学学生的导师。其核心组件是一个数据摄取管道，能够高效处理和更新来自多样化来源（如CSV文件和大学网页）的信息。该聊天机器人采用混合检索方法，结合BM25词法排序与ChromaDB语义检索技术，并利用大型语言模型LLaMA-3.3-70B生成对话式响应。实验表明生成文本具有高度语义相关性，BERTScore达0.831，METEOR分数为0.809。数据管道效率显著，更新数据仅需106.82秒，而处理新数据耗时368.62秒。该聊天机器人可通过回答学生咨询、帮助其深入理解大学生活、协助规划开放学分制下的学期日程，有效提升学生的适应能力。