Abstract: In modern university education, managing diverse student inquiries poses a significant burden on instructors, highlighting the increasing importance of personalized support. To address this challenge, we developed an AI chatbot that provides responses tailored to specific course content, using lecture materials and video transcripts. The system is built on the OpenAI API (GPT-5) and integrates Retrieval-Augmented Generation (RAG) technology.　The developed chatbot is deployed in two courses, “Requirements Engineering” and “Career Design for IT Engineers,” and evaluated with 80 university students. Evaluation results indicate that approximately 80% of the students positively rated the chatbot’s responses as “helpful”, confirming its high utility in aspects such as organizing and re-presenting lecture content, suggesting future actions, and offering diverse perspectives. Furthermore, the use of past student comments and instructor responses as learning data proved highly valuable as a supplementary resource for enhancing student understanding. This research shows AI chatbots are practical tools for personalized support and reducing instructor workload. Future work aims to build a more personalized and effective learning support environment by incorporating features such as tracking individual student learning progress and enabling instructors to analyze conversation logs.

Keywords: AI chatbot; educational support; Large Language Model; Retrieval-Augmented Generation Introduction