用AI聊天机器人解决大学生个性化辅导缺失问题
Transforming Mentorship: An AI Powered Chatbot Approach to University Guidance
- 融合关键词匹配与语义检索，用大模型生成回复
- 响应内容相关性高，BERTScore达0.831，处理效率提升63%
- 适合新生快速了解大学生活与课程规划
大学生在本科阶段面临诸多挑战，常因导师无法规模化提供个性化、即时指导而受限。现有数字工具也缺乏针对新生的定制化辅导。本文提出一个面向布拉克大学学生的AI聊天机器人系统。核心是高效的数据接入管道，可从CSV文件和校方网页等多源更新信息。聊天机器人采用混合检索策略：结合BM25词法排序与ChromaDB语义检索，并使用LLaMA-3.3-70B大语言模型生成对话响应。生成文本语义相关性高，BERTScore为0.831，METEOR得分为0.809。数据更新耗时仅106.82秒，较旧方案（368.62秒）提速63%。该系统能帮助学生解答疑问，更好理解校园生活，并辅助规划开放学分制下的学期安排。
原文摘要 · Abstract (English)
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.
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