Alan: A Classroom Deployment of An Educational Chatbot That Cites Resources

The rise of large language models (LLMs) such as ChatGPT presents new opportunities and challenges for computing education. While these models can provide instant assistance, their generality and lack of source grounding can produce inaccurate or unverifiable responses. This extended abstract introduces Alan, a course-specific virtual teaching assistant built on a retrieval-augmented generation (RAG) framework. Alan answers student questions using verified course resources such as lecture slides, lab handouts, notes, and recordings, and cites these sources directly in each response. Deployed in a large introductory programming course, Alan was used by over 250 students and logged more than 2,000 queries. A mixed-methods analysis combining usage data and survey responses shows that students found Alan most helpful for conceptual and administrative questions, valuing its transparency and course alignment. We conclude with lessons learned about integrating grounded AI support in computing classrooms.