Partenariat précaire : perceptions des étudiants à l’égard de l’IA générative dans l’apprentissage postsecondaire
DOI :
https://doi.org/10.18357/otessaj.2024.4.3.74Mots-clés :
postsecondaire, corégulation, ChatGPT, apprentissage, intelligence artificielle, IA générative, apprentissage autorégulé, interaction humain-IA, socially-shared regulation, human-AI collaborationRésumé
Les technologies d’IA générative (GenAI) offrent à la fois de puissantes possibilités et des risques importants pour l’apprentissage. Ainsi, les apprenants du niveau postsecondaire ont besoin de compétences essentielles pour réguler stratégiquement l’apprentissage grâce à l’IA. Alors que les convictions des étudiants à propos de GenAI ont un impact sur leurs interactions et leurs décisions, la recherche sur les points de vue des étudiants est émergente. Cette étude visait à examiner les perceptions des étudiants de niveau postsecondaire concernant l’utilisation efficace de GenAI pour l’apprentissage. Les participants étaient 125 étudiants de premier cycle d’une université canadienne. Les résultats ont indiqué que les étudiants étaient confiants dans leur capacité à utiliser GenAI pour l'apprentissage, percevaient GenAI comme étant efficace pour l'apprentissage et déclaraient utiliser GenAI dans un large éventail de tâches académiques, en particulier lorsqu'ils étaient confrontés à des défis cognitifs et de gestion du temps. Enfin, l’accès à l’information, le soutien à l’apprentissage personnalisé, les nouvelles approches d’apprentissage et le temps ont été considérés comme des avantages de GenAI. Cependant, les étudiants ont exprimé des inquiétudes quant à l’intégrité académique, à l’exactitude des informations et à l’impact sur l’apprentissage personnel. Les implications pour le développement des compétences des apprenants en matière d’interactions d’autorégulation avec l’IA, essentielles pour les études postsecondaires et au-delà, sont discutées.
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Références
Akata, Z., Balliet, D., de Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H., Hung, H., Jonker, C., Monz, C., Neerincx, M., Oliehoek, F., Prakken, H., Schlobach, S., van der Gaag, L., … Welling, M. (2020). A research agenda for hybrid intelligence: Augmenting human intellect with collaborative, adaptive, responsible, and explainable artificial intelligence. Computer, 53(8), 18–28. https://doi.org/10.1109/MC.2020.2996587
Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI and Ethics, 2(3), 431-440. https://doi.org/10.1007/s43681-021-00096-7
Al-Samarraie, H., Sarsam, S. M., Ibrahim Alzahrani, A., Chatterjee, A., & Swinnerton, B. J. (2024). Gender perceptions of generative AI in higher education. Journal of Applied Research in Higher Education. https://doi.org/10.1108/JARHE-02-2024-0109
Alford, J. (2023, February 1). ChatGPT sets record for fastest-growing user base – analyst note. Reuters. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
Amoozadeh, M., Daniels, D., Nam, D., Kumar, A., Chen, S., Hilton, M., ... & Alipour, M. A. (2024, March). Trust in Generative AI among students: An exploratory study. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (pp. 67-73). https://doi.org/10.1145/3626252.3630842
Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI. https://digitalcommons.uri.edu/cba_facpubs/548
Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. https://doi.org/10.61969/jai.1337500
Berg, C. (2023) "The case for generative AI in scholarly practice." Available at SSRN 4407587 http://dx.doi.org/10.2139/ssrn.4407587
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doiorg.ezproxy.library.uvic.ca/10.1191/1478088706qp063oa
Chan, C. K. Y., & Colloton, T. (2024). Generative AI in Higher Education: The ChatGPT Effect (p. 287). Taylor & Francis. https://doi.org/10.4324/9781003459026
Chan, C. & 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), 43. https://doi.org/10.1186/s41239-023-00411-8
Chan, C., & Zhou, W. (2023). Deconstructing student perceptions of Generative AI (GenAI) through an Expectancy Value Theory (EVT)-based instrument. ArXiv. https://doi.org/10.48550/arXiv.2305.01186.
Dai, Y., Chai, C. S., Lin, P. Y., Jong, M. S. Y., Guo, Y., & Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12(16), 6597. https://doi.org/10.3390/su12166597
De Backer, L., Van Keer, H., & Valcke, M. (2022). The functions of shared metacognitive regulation and their differential relation with collaborative learners’ understanding of the learning content. Learning and Instruction, 77, 101527. https://doi.org/10.1016/j.learninstruc.2021.101527
Hadwin, A. F., Järvelä, S., & Miller, M. (2011). Self-regulated, co-regulated, and socially shared regulation of learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 65–84). Routledge. https://doi.org/10.4324/9780203839010.ch5
Hadwin, A. F., Järvelä, S., & Miller, M. (2018). Self-regulation, co-regulation and shared regulation in collaborative learning environments. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 83–106). Routledge. https://doi.org/10.4324/9781315697048-6
Harrer, S. (2023). Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine. EBioMedicine, 90. https://doi.org/10.1016/j.ebiom.2023.104512.
Hellmich, E. A., Vinall, K., Brandt, Z. M., Chen, S., & Sparks, M. M. (2024). ChatGPT in language education: Centering learner voices. Technology in Language Teaching & Learning, 6(3), 1741-1741. https://doi.org/10.29140/tltl.v6n3.1741
Järvelä, S., Nguyen, A., & Hadwin, A. (2023). Human and artificial intelligence collaboration for socially shared regulation in learning. British Journal of Educational Technology, 54(5), 1057–1076. https://doi.org/10.1111/bjet.13325
Jiayu, Y. (2023). Challenges and opportunities of generative artificial intelligence in higher education student educational management. Advances in Educational Technology and Psychology, 7(9). https://doi.org/10.23977/aetp.2023.070914
Klingbeil, A., Grützner, C., & Schreck, P. (2024). Trust and reliance on AI—An experimental study on the extent and costs of overreliance on AI. Computers in Human Behavior, 160, 108352. https://doi-org/10.1016/j.chb.2024.108352
Lobczowski, N. G., Lyons, K., Greene, J. A., & McLaughlin, J. E. (2021). Socially shared metacognition in a project-based learning environment: A comparative case study. Learning, Culture and Social Interaction, 30, 100543. https://doi.org/10.1016/j.lcsi.2021.100543
Lodge, J. M., Thompson, K., & Corrin, L. (2023). Mapping out a research agenda for generative artificial intelligence in tertiary education. Australasian Journal of Educational Technology, 39(1), 1–8. https://doi.org/10.14742/ajet.8695
Lubowitz, J.H. (2023). ChatGPT, an artificial intelligence chatbot, is impacting medical literature. Arthroscopy, 39(5): 1121-1122. https://doi.org/10.1016/j.arthro.2023.01.015
Miller, M., & Hadwin, A. F. (2024). Comparing the effectiveness of CSCL scripts for shared task perceptions in socially shared regulation of collaborative learning. International Journal of Computer-Supported Collaborative Learning, 19(4), 455–478. https://doi.org/10.1007/s11412-024-09434-3
Molenaar, I. (2022). Towards hybrid human‐AI learning technologies. European Journal of Education, 57(4), 632-645. https://doi.org/10.1111/ejed.12527
Ng, D., Tan, C., & Leung, J. (2024). Empowering student self-regulated learning and science education through ChatGPT: A pioneering pilot study. British Journal of Educational Technology, 55, 1328-1353. https://doi.org/10.1111/bjet.13454.
Nguyen, H., Nguyen, A. (2024). Reflective practices and self-regulated learning in designing with generative artificial intelligence: An ordered network analysis. Journal of Science Education and Technology. https://doi.org/10.1007/s10956-024-10175-z
Panadero, E., Klug, J., & Järvelä, S. (2016). Third wave of measurement in the self-regulated learning field: When measurement and intervention come hand in hand. Scandinavian Journal of Educational Research, 60(6), 723-735. https://doi.org/10.1080/00313831.2015.1066436
Pedersen, I. (2024). Generative AI Adoption in Postsecondary Education, AI Hype, and ChatGPT’s Launch. The Open/Technology in Education, Society, and Scholarship Association Journal, 4(1), 1-19. DOI: https://doi.org/10.18357/otessaj.2024.4.1.59
Sharples, M. (2023). Towards social generative AI for education: Theory, practices and ethics. Learning: Research and Practice, 9 (2), 159-167. https://doi.org/10.1080/23735082.2023.2261131
Sijing, L., & Lan, W. (2018, August). Artificial intelligence education ethical problems and solutions. In 2018 13th International Conference on Computer Science & Education (ICCSE) (pp. 1-5). IEEE. https://doi.org/10.1109/ICCSE.2018.8468773
Terveen, L. G. (1995). Overview of human-computer collaboration. Knowledge-Based Systems, 8(2–3), 67–81. https://doi.org/10.1016/0950-7051(95)98369-H
Wang, D., Weisz, J. D., Muller, M., Ram, P., Geyer, W., Dugan, C., ... & Gray, A. (2019). Human-AI collaboration in data science: Exploring data scientists' perceptions of automated AI. In Proceedings of the ACM on human-computer interaction, 3(CSCW) (pp. 1–24). https://doi-org.ezproxy.library.uvic.ca/10.1145/3359313
Webster, E. A., & Hadwin, A. F. (2015). Emotions and emotion regulation in undergraduate studying: Examining students’ reports from a self-regulated learning perspective. Educational Psychology, 35(7), 794-818. https://doi.org/10.1080/01443410.2014.895292
Winne, P. H. (2017). Learning analytics for self-regulated learning. Handbook of learning analytics, 754, 241-249.
Winne, P., & Hadwin, A. (1998). Studying as self-regulated learning (pp. 291–318). Routledge. https://doi.org/10.4324/9781410602350-19
Zastudil, C., Rogalska, M., Kapp, C., Vaughn, J., & MacNeil, S. (2023, October). Generative ai in computing education: Perspectives of students and instructors. In 2023 IEEE Frontiers in Education Conference (FIE) (pp. 1-9). IEEE. https://doi.org/10.48550/arXiv.2308.04309
Zheng, J., Xing, W., & Zhu, G. (2019). Examining sequential patterns of self-and socially shared regulation of STEM learning in a CSCL environment. Computers & Education, 136, 34-48. https://doi.org/10.1016/j.compedu.2019.03.005
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3-17. https://doi.org/10.1207/s15326985ep2501_2
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