Misconception-Aware LLM Programming Tutor: Lessons Learned from Student-Tutor Interactions
Abstract
Large Language Models (LLMs) are increasingly used as programming tutors, but their feedback is often generic and prone to solution leakage. To address these issues, we present MisconceptionTutor, which grounds feedback in common student misconceptions. Through both pre-deployment analyses and a real-classroom deployment, we find that even simple prompting frameworks can meaningfully steer tutor behavior to be more pedagogically oriented and noticeably more satisfying to students.
Fields of Study & Subjects
Intelligent Tutoring Systems and Adaptive Learning
Teaching and Learning Programming
Topic Modeling
Publication details
- Journal / source
- Proceedings of the 57th ACM Technical Symposium on Computer Science Education V.2
- Publisher
- ACM
- Pages
- 1455–1456
- Publication date
- 13 February 2026