A4 Refereed article in a conference publication
Utilizing Large Language Model for Programming Course Exercise Generation
Authors: Kaila, Erkki; Rytilahti, Juuso; Lempinen, William; Lindgren, Luuka
Editors: Arabnia, Hamid R.; Deligiannidis, Leonidas; Amirian, Soheyla; Ghareh Mohammadi, Farid; Shenavarmasouleh, Farzan
Conference name: International Conference on the AI Revolution
Publication year: 2026
Journal: Communications in Computer and Information Science
Book title : AI Revolution : Research, Ethics and Society : International Conference, AIR-RES 2025, Las Vegas, NV, USA, April 14–16, 2025, Proceedings, Part I
Volume: 2721
First page : 204
Last page: 217
ISBN: 978-3-032-12312-1
eISBN: 978-3-032-12313-8
ISSN: 1865-0929
eISSN: 1865-0937
DOI: https://doi.org/10.1007/978-3-032-12313-8_15
Publication's open availability at the time of reporting: No Open Access
Publication channel's open availability : Partially Open Access publication channel
Web address : https://doi.org/10.1007/978-3-032-12313-8_15
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/508231613
Self-archived copy's version: Final draft
Large language models (LLMs) are potentially powerful tools for automating educational tasks. In this paper, we observe two use cases of LLMs related to introductory programming education. In the first case, we created an LLM-based tool for creating variations of existing exercises. In the second case, we used LLM for generating the unit tests and good-quality feedback for students’ answers to programming exercises. Both approaches were studied by gathering data from two instances of a large introductory programming course. Our results indicate, that both approaches were successful. In addition to discussing the results, we discuss the insights gained, the identified use cases, and the significance of the rapid progress the LLMs have on programming education.
Funding information in the publication:
This work has been supported by FAST, the Finnish Software Engineering Doctoral Research Network, funded by the Ministry of Education and Culture, Finland.
