From assistance to misconduct: unpacking the complex role of generative AI in student learning.

Axelsson, Andreas; Wallgren, Daniel Tomas; Verma, Udit; Cajander, Åsa; Daniels, Mats; Eckerdal, Anna; McDermott, Roger

Abstract

This research-to-practice full paper discusses students' views on the role of generative artificial intelligence (GenAI) in their learning. The rapid integration of GenAI in educational settings has prompted significant interest in its implications for learning and academic integrity. This study investigates the adoption and impact of GenAI tools among computing students at a university, focusing on how they are utilized for educational purposes and their ethical implications. Semi-structured interviews with nine computing students were used to examine GenAI's specific use and timing. Additionally, it explores students' perceptions of the trustworthiness of GenAI outputs and identifies the students' ethical boundaries concerning its use in academic work. The findings reveal that while GenAI tools might enhance learning efficiency and provide substantial educational support, they raise significant ethical concerns, particularly regarding academic misconduct. The study highlights the need for educational strategies to navigate the challenges posed by GenAI technologies. Finally, three recommendations for computing education are outlined. This research contributes to the ongoing discourse on GenAI in education by describing the student's reflections on GenAI.

Citation

AXELSSON, A., WALLGREN, D.T., VERMA., U., CAJANDER, Å., DANIELS, M., ECKERDAL, A. and MCDERMOTT, R. 2024. From assistance to misconduct: unpacking the complex role of generative AI in student learning. In Proceedings of the 2024 IEEE (Institute of Electrical and Electronics Engineers) Frontiers in education conference (FIE 2024), 13-16 October 2024, Washington, DC, USA. Piscataway: IEEE [online], article number 10893133. Available from: https://doi.org/10.1109/FIE61694.2024.10893133

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 IEEE (Institute of Electrical and Electronics Engineers) Frontiers in education conference (FIE 2024)
• Start Date: Oct 13, 2024
• End Date: Oct 16, 2024
• Acceptance Date: Sep 20, 2024
• Online Publication Date: Feb 26, 2025
• Publication Date: Dec 31, 2024
• Deposit Date: Feb 27, 2025
• Publicly Available Date: Feb 27, 2025
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Article Number: 10893133
• Series ISSN: 2377-634X
• DOI: https://doi.org/10.1109/FIE61694.2024.10893133
• Keywords: Generative AI; Student learning; Cheating; Motivation; Misconduct
• Public URL: https://rgu-repository.worktribe.com/output/2715694