Ethical implications of Gen-AI and LLMs in computing education.

Zarb, Mark; Brown, John N.A.; Goodfellow, Martin; Liaskos, Konstantinos; Young, Tiffany

Abstract

The panel convenes five educators to discuss the ethical implications of utilising Generative AI (Gen-AI) and Large Language Models (LLMs) in computing education. Their expertise spans various domains, including organising national workshops on the implications of generative AI tools, conducting surveys on their use within curricula, implementing institutional policies related to technology use, and engaging with students directly in the classroom. They reflect on the evolution of Gen-AI and LLMs from challenging-to-use technologies to indispensable tools for users of all levels. Furthermore, they examine the ethical dilemmas arising from the widespread adoption of these technologies in educational contexts, particularly regarding issues of originality, integrity, and responsible use. In addition, they explore practical strategies for integrating ethics education into computing curriculum design and classroom practices. This includes discussions on the role of educators in guiding students towards ethical technology usage, addressing uncertainties surrounding Gen-AI tools, and fostering a culture of responsible innovation within educational institutions. Through their collective insights and experiences, the panel aims to provide recommendations for navigating the ethical complexities inherent in the integration of Gen-AI technologies into computing education curricula.

Citation

ZARB, M., BROWN, J.N.A., GOODFELLOW, M., LIASKOS, K. and YOUNG, T. 2024. Ethical implications of Gen-AI and LLMs in computing education. In Proceedings of the 1st Association for Computing Machinery virtual global computing education conference (SIGCSE Virtual 2024), 5-8 December 2024, [virtual event]. New York: ACM [online], volume 2, pages 293-294. Available from: https://doi.org/10.1145/3649409.3691074

• Presentation Conference Type: Conference Abstract
• Conference Name: 1st Association for Computing Machinery virtual global computing education conference (SIGCSE Virtual 2024)
• Start Date: Dec 5, 2024
• End Date: Dec 8, 2024
• Acceptance Date: Jul 26, 2024
• Online Publication Date: Dec 5, 2024
• Publication Date: Dec 5, 2024
• Deposit Date: Mar 3, 2025
• Publicly Available Date: Mar 3, 2025
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Volume: 2
• Pages: 293-294
• Book Title: SIGCSE Virtual 2024
• ISBN: 9798400706042
• DOI: https://doi.org/10.1145/3649409.3691074
• Keywords: ChatGPT; Curriculum design; Ethics; Generative AI; Large language models; Responsibility
• Public URL: https://rgu-repository.worktribe.com/output/2613840