AI-Driven Tutoring in Programming Education: Using Large Language Models to Generate Code Feedback

Iris: An AI-Driven Virtual Tutor For Computer Science Education

Integrating AI-driven tools in higher education is an emerging area with transformative potential. This paper introduces Iris, a chatbased virtual tutor integrated into the interactive learning platform Artemis that offers personalized, context-aware assistance in largescale educational settings. Iris supports computer science students by guiding them through programming exercises and is designed to act as a tutor in a didactically meaningful way. Its calibrated assistance avoids revealing complete solutions, offering subtle hints or counter-questions to foster independent problem-solving skills. For each question, it issues multiple prompts in a Chain-of-Thought to GPT-3.5-Turbo. The prompts include a tutor role description and examples of meaningful answers through few-shot learning. Iris employs contextual awareness by accessing the problem statement, student code, and automated feedback to provide tailored advice. An empirical evaluation shows that students perceive Iris as effective because it understands their questions, provides relevant support, and contributes to the learning process. While students consider Iris a valuable tool for programming exercises and homework, they also feel confident solving programming tasks in computerbased exams without Iris. The findings underscore students’ appreciation for Iris’ immediate and personalized support, though students predominantly view it as a complement to, rather than a replacement for, human tutors. Nevertheless, Iris creates a space for students to ask questions without being judged by others.