Abstracts
Résumé
Cette étude explore les perceptions et les pratiques liées à l’intelligence artificielle générative (IAg) dans l’enseignement postsecondaire au Nouveau-Brunswick (Canada). Basée sur une approche mixte, elle analyse les réponses de 281 participantes et participants issus de deux établissements d’enseignement. Les résultats montrent que l’adoption de l’IAg varie selon les profils, influençant les perceptions de son utilité et de ses implications éthiques. Tandis que les étudiants et étudiantes perçoivent l’IA comme un outil pédagogique, les enseignants et enseignantes expriment des préoccupations sur son impact. Ces divergences soulignent la nécessité d’une formation systématique pour développer une littératie de l’IA adaptée aux besoins du 21e siècle.
Mots-clés :
- Intelligence artificielle,
- technologie éducative,
- enseignement supérieur,
- attitudes des enseignants et enseignantes,
- attitudes des étudiants et étudiantes,
- littératie numérique,
- éthique,
- intégration des technologies,
- IA générative,
- littératie en IA
Abstract
This study examines perceptions and practices regarding generative artificial intelligence (GenAI) in postsecondary education in New Brunswick, Canada. Using a mixed-methods approach, it analyzes responses from 281 participants in two institutions. Findings reveal adoption disparities influencing perceptions of GenAI’s utility and ethical implications. While students view GenAI as a pedagogical tool, educators express concerns about its broader impact. These differences emphasize the need for formal training to foster AI literacy tailored to 21st‑century educational demands.
Keywords:
- Artificial intelligence,
- educational technology,
- higher education,
- teacher attitudes,
- student attitudes,
- digital literacy,
- ethics,
- technology integration,
- generative AI,
- AI literacy
Appendices
Références
- Akrich, M. (2010). Comment décrire les objets techniques? Techniques et culture, (54‑55), 205‑219. (Article original publié dans la même revue en 1987.) https://doi.org/10.4000/tc.4999
- Al-Abdullatif, A. M. et Alsubaie, M. A. (2024). ChatGPT in learning: Assessing students’ use intentions through the lens of perceived value and the influence of AI literacy. Behavioral Sciences, 14(9), article 845. https://doi.org/10.3390/bs14090845
- Asparouhov, T. et Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 397‑438. https://doi.org/cdqfvc
- Banh, L. et Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33, article 63. https://doi.org/m3tb
- Bishop, E. (2023). Critical literacy: Bringing theory to praxis. Dans A. Darder, K. Hernandez, K. D. Lam et M. Baltodano (dir.), The critical pedagogy reader (4e éd., p. 385‑396). Routledge. https://doi.org/pjsm
- Cao, L. (2023). Trans-AI/DS: Transformative, transdisciplinary and translational artificial intelligence and data science. International Journal of Data Science and Analytics, 15(2), 119‑132. https://doi.org/pjsn
- Carter, L., Liu, D. et Cantrell, C. (2020). Exploring the intersection of the digital divide and artificial intelligence: A hermeneutic literature review. AIS Transactions on Human‑Computer Interaction, 12(4), 253‑275. https://doi.org/10.17705/1thci.00138
- Chan, C. K. Y. et Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), article 43. https://doi.org/gshsfg
- Chen, K., Tallant, A. C. et Selig, I. (2024). Exploring generative AI literacy in higher education: Student adoption, interaction, evaluation and ethical perceptions. Information and Learning Sciences, 126(1/2), 132‑148. https://doi.org/10.1108/ILS-10-2023-0160
- Chiu, T. K. F., Chen, Y., Yau, K. W., Chai, C., Meng, H., King, I., Wong, S. et Yam, Y. (2024). Developing and validating measures for AI literacy tests: From self-reported to objective measures. Computers and Education: Artificial Intelligence, 7, article 100282. https://doi.org/10.1016/j.caeai.2024.100282
- Choudhury, A. et Shamszare, H. (2024). The impact of performance expectancy, workload, risk, and satisfaction on trust in ChatGPT: Cross-sectional survey analysis. JMIR Human Factors, 11, article e55399. https://doi.org/10.2196/55399
- Cohen, L., Manion, L. et Morrison, K. (2018). Research methods in education (8e éd.). Routledge.
- Creswell, J. W. et Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5e éd.). SAGE.
- Cuban, L. (1986). Teachers and machines. Teachers College Press.
- Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results [thèse de doctorat, Massachusetts Institute of Technology, États‑Unis]. DSpace@MIT. http://hdl.handle.net/1721.1/15192
- Formosa, P., Kashyap, B. et Sahebi, S. (2024). Generative AI and the future of democratic citizenship. Digital Government: Research and Practice, article 3674844. https://doi.org/10.1145/3674844
- Fortier, M. (2023, 8 février). Intelligence artificielle, malaise réel dans les cégeps. Le Devoir. https://ledevoir.com/...
- Graveleau, S. (2024, 3 mai). Comment l’intelligence artificielle commence à séduire les enseignants du supérieur. Le Monde. https://lemonde.fr/...
- Hu, K. (2023, 2 février). ChatGPT sets record for fastest-growing user base – Analyst note. Reuters. https://reuters.com/...
- Humlum, A. et Vestergaard, E. (2024). The adoption of ChatGPT (Working paper no 2024‑50). University of Chicago, Becker Friedman Institute for Economics. http://dx.doi.org/10.2139/ssrn.4807516
- Jackson, C. (2022, 5 janvier). Global opinions and expectations about artificial intelligence: A global advisor survey. Ipsos. https://ipsos.com/...
- Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S. et Huber, P. (2016). Artificial intelligence and computer science in education: From kindergarten to university. Dans Proceedings of 2016 IEEE Frontiers in Education Conference (FIE). IEEE. https://doi.org/10.1109/FIE.2016.7757570
- Kazley, A. S., Andresen, C., Mund, A., Blankenship, C. et Segal, R. (2024). Is use of ChatGPT cheating? Students of health professions perceptions. Medical Teacher. https://doi.org/pjsw
- KPMG. (2024). Generative AI Adoption Index. https://kpmg.com/...
- Kress, G. et Van Leeuwen, T. (2001). Multimodal discourse: The modes and media of contemporary communication. Arnold.
- Laupichler, M. C., Aster, A., Haverkamp, N. et Raupach, T. (2023). Development of the “scale for the assessment of non-experts’ AI literacy” – An exploratory factor analysis. Computers in Human Behavior Reports, 12, article 100338. https://doi.org/10/gtdv7r
- Laupichler, M. C., Aster, A. et Raupach, T. (2023). Delphi study for the development and preliminary validation of an item set for the assessment of non-experts’ AI literacy. Computers and Education: Artificial Intelligence, 4, article 100126. https://doi.org/10.1016/j.caeai.2023.100126
- Lee, V. R., Pope, D., Miles, S. et Zárate, R. C. (2024). Cheating in the age of generative AI: A high school survey study of cheating behaviors before and after the release of ChatGPT. Computers and Education: Artificial Intelligence, 7, article 100253. https://doi.org/10.1016/j.caeai.2024.100253
- Long, D. et Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Dans P. Bjørn et S. Zhao (dir.), Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. ACM. https://doi.org/10/ghbz2q
- Mackey, T. P. et Jacobson, T. E. (2011). Reframing information literacy as a metaliteracy. College & Research Libraries, 72(1), 62‑78. https://doi.org/10.5860/crl-76r1
- McCarthy, J., Minsky, M. L., Rochester, N. et Shannon, C. E. (2006). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955. AI Magazine, 27(4), 12‑14. (Ouvrage original paru en 1955.) https://doi.org/10.1609/aimag.v27i4.1904
- Michelot, F. (2022). Obstacles et opportunités stratégiques de l’avenir de la formation à distance. Une contribution à la planification stratégique de l’Université de Moncton. Distances et médiations des savoirs, (39). https://doi.org/10/gq6pzt
- Miller, R. (dir.). (2020). Transformer le futur. L’anticipation au XXIe siècle. Les Presses de l’Université de Montréal. (Ouvrage original publié en 2018 sous le titre Transforming the future: Anticipation in the 21st century.) https://unesdoc.unesco.org/...
- Minevich, M. (2023, 14 décembre). The dawn of AI disruption: How 2024 marks a new era in innovation. Forbes. https://forbes.com/...
- Ministère de l’Éducation et du Développement de la petite enfance. (2024a). Cadre d’orientation de l’intelligence artificielle. Gouvernement du Nouveau-Brunswick. https://www2.gnb.ca/...
- Ministère de l’Éducation et du Développement de la petite enfance. (2024b). Le guide d’intégration de l’IA pour les écoles. Gouvernement du Nouveau-Brunswick. https://www2.gnb.ca/...
- Muhson, A., Wayuni, D., Baroroh, K., Nurseto, T. et Uny, S. (2024). The resistance to artificial intelligence in education: Student perspectives and ethical implications. Dans Proceedings of the International Conference of Ethics of Business, Economics and Social Science (p. 26‑31). https://researchgate.net/publication/385565586
- Ng, D. T. K., Leung, J. K. L., Chu, S. K. W. et Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, article 100041. https://doi.org/10/gqv59z
- O’Dea, X., Tsz Kit Ng, D., O’Dea, M. et Shkuratskyy, V. (2024). Factors affecting university students’ generative AI literacy: Evidence and evaluation in the UK and Hong Kong contexts. Policy Futures in Education. https://doi.org/pjtg
- Street, B. V. (1984). Literacy in theory and practice. Cambridge University Press.
- Tahat, Z. Y., Habes, M., Hailat, K. M., Al Jwaniat, M. I., Safori, A. et Kamarudin, S. (2024). Attitudes of students towards press coverage of e-learning: An empirical study. Dans A. M. A. Musleh Al‑Sartawi, A. S. Aydiner et M. Kanan (dir.), Business analytical capabilities and artificial intelligence-enabled analytics: Applications and challenges in the digital era (Studies in computational intelligence, vol. 1151, p. 251‑263). Springer. https://doi.org/pjth
- Talsma, K., Schüz, B., Schwarzer, R. et Norris, K. (2018). I believe, therefore I achieve (and vice versa): A meta-analytic cross-lagged panel analysis of self-efficacy and academic performance. Learning and Individual Differences, 61(2018), 136‑150. https://doi.org/10/gc32c5
- UNESCO. (2013). Global media and information literacy assessment framework: Country readiness and competencies. https://unesdoc.unesco.org/...
- Vallerand, R. J. (1989). Vers une méthodologie de validation trans-culturelle de questionnaires psychologiques : implications pour la recherche en langue française. Psychologie canadienne, 30(4), 662‑680. https://doi.org/10/d3qc6k
- Yi, Y. (2021). Establishing the concept of AI literacy: Focusing on competence and purpose. Jahr – European Journal of Bioethics, 12(2), 353‑368. https://doi.org/10.21860/j.12.2.8
- Zou, M. et Huang, L. (2023). To use or not to use? Understanding doctoral students’ acceptance of ChatGPT in writing through technology acceptance model. Frontiers in Psychology, 14, article 1259531. https://doi.org/10.3389/fpsyg.2023.1259531

