Codes éthiques pour l'intelligence artificielle générative dans l'enseignement supérieur: sont-ils importants?
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Résumé
L'émergence de l'outil d'intelligence artificielle générative (IAG) ChatGPT en 2022 a marqué un tournant dans le contexte des développements de l'IA, mais aussi dans la réflexion éthique sur son utilisation dans divers contextes, y compris dans l'éducation. Depuis lors, diverses lignes directrices, recommandations et politiques ont été élaborées pour guider les utilisations acceptables et responsables de l'IA générative, également connues sous le nom de codes d'éthique pour la IAG. Les universités n'ont pas été épargnées par ces développements et se sont également associées à la formulation de lignes directrices et de politiques institutionnelles spécifiques afin de recommander et de réglementer les types d'utilisations de la IAG qui sont acceptables dans leur contexte d'enseignement supérieur. Dans cet article de réflexion, nous soulevons des questions sur l'importance et l'utilité des codes d'éthique pour la IAG dans le contexte de l'enseignement supérieur et sur les défis auxquels ils sont confrontés dans leur conception, leur mise en œuvre et leur évaluation. Dans l'ensemble, notre objectif est d'encourager le débat sur la manière de garantir une utilisation responsable et éthique de l'IA générative par les enseignants et les étudiants, tout en s'alignant sur leur capacité d'action et leur autonomisation.
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Références
AI Pioneers (2025). Evaluation schema and handbook for AI in education on data, privacy, ethics, and EU values (WP5). https://aipioneers.org/evaluation-schema-for-ai-in-education-on-data-privacy-ethics-and-eu-values-wp5/
BMJ (1996). 313 The Nuremberg Code (1947). https://doi.org/10.1136/bmj.313.7070.1448 DOI: https://doi.org/10.1136/bmj.313.7070.1448
Corbin, T., Dawson, P., & Liu, D. (2025). Talk is cheap: why structural assessment changes are needed for a time of GenAI, Assessment & Evaluation in Higher Education, 50(7), 1087-1097. https://doi.org/10.1080/02602938.2025.2503964 DOI: https://doi.org/10.1080/02602938.2025.2503964
Corrêa, N. K., Galvão, C., Santos, J. W., Del Pino, C., Pinto, E. P., Barbosa, C., Massmann, D., Mambrini, R., Galvão, L., Terem, E., &[A6.1] de Oliveira, N. (2023). Worldwide AI ethics: A review of 200 guidelines and recommendations for AI governance. Patterns, 4(10). https://doi.org/10.1016/j.patter.2023.100857 DOI: https://doi.org/10.1016/j.patter.2023.100857
Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press. DOI: https://doi.org/10.12987/9780300252392
Dabis, A., & Csaki, C. (2024). AI and ethics: Investigating the first policy response of higher education institutions to the challenge of generative AI, Humanities & Social Science Communications, 11, 1006. https://doi.org/10.1057/s41599-024-03526-z DOI: https://doi.org/10.1057/s41599-024-03526-z
Hao, K. (2025). Empire of AI. Penguin Press.
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I.,[A7.1] & Koedinger, K. R. (2022). Ethics of AI in Education : Towards a Community-Wide Framework. International Journal of Artificial Intelligence in Education, 32(3), 504 526. https://doi.org/10.1007/s40593-021-00239-1 DOI: https://doi.org/10.1007/s40593-021-00239-1
Jin, Y., Yan, L., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. (2025). Generative AI in higher education: A global perspective of institutional adoption policies and guidelines. Computers and Education: Artificial Intelligence, 8, 100348. https://doi.org/10.1016/j.caeai.2024.100348 DOI: https://doi.org/10.1016/j.caeai.2024.100348
Luo, J. (2024). A critical review of GenAI policies in higher education assessment: a call to reconsider the “originality” of students’ work . Assessment & Evaluation in Higher Education, 49(5), 651–664. https://doi.org/10.1080/02602938.2024.2309963 DOI: https://doi.org/10.1080/02602938.2024.2309963
Mejias, U. A., & Couldry, N. (2024). Data Grab: The New Colonialism of Big Tech (and How to Fight Back). WH Allan. DOI: https://doi.org/10.7208/chicago/9780226832319.001.0001
Miao, F. & Holmes, W.[A8.1] (2023). Guidance for generative AI in education and research. Unesco Publishing. https://doi.org/10.54675/EWZM9535 DOI: https://doi.org/10.54675/EWZM9535
Miller, A. G. (2004). Milgram Experiments. In Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao (Eds), The SAGE Encyclopedia of Social Science Research Methods (p. 645). Sage Publications. https://doi.org/10.4135/9781412950589.n561 DOI: https://doi.org/10.4135/9781412950589.n561
Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1, 501–507. https://doi.org/10.1038/s42256-019-0114-4 DOI: https://doi.org/10.1038/s42256-019-0114-4
Mouta, A., Pinto-Llorente, A.M., & Torrecilla-Sánchez, E.M. (2024). Uncovering Blind Spots in Education Ethics: Insights from a Systematic Literature Review on Artificial Intelligence in Education. International Journal of Artificial Intelligence in Education, 34, 1166–1205. https://doi.org/10.1007/s40593-023-00384-9 DOI: https://doi.org/10.1007/s40593-023-00384-9
Narayanan, A., & Kapoor, S. (2025). AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell the Difference. Princeton University Press. DOI: https://doi.org/10.1515/9780691277929
Newig, J., Jager, N. W., Challies, E., & Kochskämper, E. (2023). Does stakeholder participation improve environmental governance? Evidence from a meta-analysis of 305 case studies. Global Environmental Change, 82, 102705. https://doi.org/10.1016/j.gloenvcha.2023.102705 DOI: https://doi.org/10.1016/j.gloenvcha.2023.102705
O’Neil, C. (2017). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Penguin.
OEIAC (2025). The PIO model. https://oeiac.cat/en/the-pio-model/
Parker, L., Loper, A.J., Hayes, J., Karakas, A., White, S., & Hallman, H. (2025). Comparative analysis of artificial intelligence policies in universities across five countries. Discover Computing, 28, 267. https://doi.org/10.1007/s10791-025-09745-5 DOI: https://doi.org/10.1007/s10791-025-09745-5
Porayska-Pomsta, K., & Holmes, W. (2022). Conclusions: Towards ethical AIED. In W. Holmes and K. Porayska-Pomsta (eds), The Ethics of Artificial Intelligence in Education: Practices, Challenges, and Debates. Taylor and Francis. DOI: https://doi.org/10.4324/9780429329067-14
Rasky, E. (2024). Generative AI Policy in Higher Education: A Preliminary Survey. Digital Policy Hub. https://www.cigionline.org/static/documents/DPH-paper-Rasky_0Pw3nS7.pdf
Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act), Official Journal L 2024/1689 (2024). https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng
Ross, J. [A9.1](2025, June 10). Universities’ AI safeguards promote ‘enforcement illusion’. Times Higher Education. https://www.timeshighereducation.com/news/universities-ai-safeguards-promote-enforcement-illusion
Shadbolt, N., & Hampson, R. (2025). As If Human: Ethics and Artificial Intelligence. Yale University Press. DOI: https://doi.org/10.2307/jj.13760051
Sinclair, A. (1993). Approaches to organisational culture and ethics. Journal of Business Ethics, 12, 63–73. https://doi.org/10.1007/BF01845788 DOI: https://doi.org/10.1007/BF01845788
Širec, K., & Rožman, M. (2025). Collaborative Leadership for Quality Assurance: A Case Study on Developing a Strategic Quality Manual in Higher Education. Education Sciences, 15(12), 1627. https://doi.org/10.3390/educsci15121627 DOI: https://doi.org/10.3390/educsci15121627
Tsai, Y. S., & Gasevic, D. (2017). Learning analytics in higher education---challenges and policies: a review of eight learning analytics policies. In Proceedings of the seventh international learning analytics & knowledge conference (pp. 233-242). https://doi.org/10.1145/3027385.3027400 DOI: https://doi.org/10.1145/3027385.3027400
UNESCO (2023). Recommendation on the Ethics of Artificial Intelligence. United Nations Educational, Scientific and Cultural Organization. https://unesdoc.unesco.org/ark:/48223/pf0000381137
Wu, C., Zhang, H., & Carroll, J. M. (2024). AI Governance in Higher Education: Case Studies of Guidance at Big Ten Universities. Future Internet, 16(10), 354. https://doi.org/10.3390/fi16100354 DOI: https://doi.org/10.3390/fi16100354
Xu, Y., Xiao, L., Mokhtar, N. A., & Sulaiman, M. K. A. M. (2025). Participatory Approaches to Landscape Planning in Urban Fringe Areas: A Systematic Review of Community Co-Design and Institutional Governance Frameworks. Journal of Cultural Analysis and Social Change, 10(2), 3535–3547. https://doi.org/10.64753/jcasc.v10i2.2140 DOI: https://doi.org/10.64753/jcasc.v10i2.2140
Zeivots, S., Hopwood, N., Wardak, D., & Cram, A. (2025). Co-design practice in higher education: practice theory insights into collaborative curriculum development. Higher Education Research & Development, 44(3), 769-783. https://doi.org/10.1080/07294360.2024.2410269 DOI: https://doi.org/10.1080/07294360.2024.2410269
Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. Profile Books