I. Groher, P. Heißenberger, M. Vierhauser: Design and Deployment of a Course-Aware AI Tutor in an Introductory Programming Course, In Proceedings of the 18th International Conference on Computer Supported Education (CSEDU 2026), May 18-20, 2026, Benidorm, Spain.
Large language models (LLMs) have rapidly become part of how students solve programming tasks, offering immediate explanations and even full solutions. Previous work has highlighted that novice programmers often rely heavily on LLMs, thereby neglecting their own problem-solving skills. To address this challenge, we designed a course-specific online Python tutor that provides retrieval-augmented, course-aligned guidance without generating complete solutions. The tutor integrates a web-based programming environment with a conversational agent that offers hints, Socratic questions, and explanations grounded in course materials.
In this paper, we describe the design and implementation of the tutor and report on its application in an introductory Python course. Students used the system during self-study to work on homework assignments on collections and functions, while the tutor also supported questions about the broader course material. We collected structured student feedback and analyzed interaction logs to investigate how they engaged with the tutor’s guidance. We observed that students used the tutor primarily for conceptual understanding, implementation guidance, and debugging, and perceived it as a course-aligned, context-aware learning support that encourages engagement rather than direct solution copying.