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

Patrick Bassner, Eduard Frankford, Stephan Krusche

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Integrating AI-driven tools in higher education is an emerging area with transformative potential. This paper introduces Iris, a chat-based virtual tutor integrated into the interactive learning platform Artemis that offers personalized, context-aware assistance in large-scale 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 computer-based 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.

Original languageEnglish
Title of host publicationITiCSE 2024 - Proceedings of the 2024 Conference Innovation and Technology in Computer Science Education
PublisherAssociation for Computing Machinery
Pages394-400
Number of pages7
ISBN (Electronic)9798400706004
DOIs
StatePublished - 3 Jul 2024
Event29th Conference Innovation and Technology in Computer Science Education, ITiCSE 2024 - Milan, Italy
Duration: 8 Jul 202410 Jul 2024

Publication series

NameAnnual Conference on Innovation and Technology in Computer Science Education, ITiCSE
Volume1
ISSN (Print)1942-647X

Conference

Conference29th Conference Innovation and Technology in Computer Science Education, ITiCSE 2024
Country/TerritoryItaly
CityMilan
Period8/07/2410/07/24

Keywords

  • chatgpt
  • cs1
  • education technology
  • generative ai
  • interactive learning
  • large language models
  • programming exercises

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