Utilizing Large Language Model for Programming Course Exercise Generation




Kaila, Erkki; Rytilahti, Juuso; Lempinen, William; Lindgren, Luuka

Arabnia, Hamid R.; Deligiannidis, Leonidas; Amirian, Soheyla; Ghareh Mohammadi, Farid; Shenavarmasouleh, Farzan

International Conference on the AI Revolution

2026

 Communications in Computer and Information Science

AI Revolution : Research, Ethics and Society : International Conference, AIR-RES 2025, Las Vegas, NV, USA, April 14–16, 2025, Proceedings, Part I

2721

204

217

978-3-032-12312-1

978-3-032-12313-8

1865-0929

1865-0937

DOIhttps://doi.org/10.1007/978-3-032-12313-8_15

https://doi.org/10.1007/978-3-032-12313-8_15

https://research.utu.fi/converis/portal/detail/Publication/508231613



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.



This work has been supported by FAST, the Finnish Software Engineering Doctoral Research Network, funded by the Ministry of Education and Culture, Finland.


Last updated on 28/01/2026 11:34:03 AM