Abstracts
Résumé
Le présent article rend compte des résultats d’une recherche auprès de 523 étudiants et étudiantes de licence et master de sciences de l’éducation et de la formation en France. Il vise à déterminer les usages et représentations de ces futurs professionnels et professionnelles de l’éducation concernant les robots conversationnels (RC). La méthode repose sur un questionnaire basé sur des échelles d’attitudes (littératie, utilité perçue, intention) et des entretiens semi-directifs. Les résultats montrent une faible compréhension de la manière dont les RC fonctionnent, les étudiantes et étudiants en ont un usage courant et varié dans le cadre privé et universitaire. Ils développent des stratégies leur permettant, selon eux, d’être plus efficaces dans leurs révisions et plus performants dans la réalisation de leurs travaux universitaires. Ces premiers résultats ouvrent la voie à d’autres recherches qui permettraient de comprendre comment ces outils récents pourraient être intégrés efficacement dans l’enseignement supérieur, particulièrement chez les futurs professionnels et professionnelles de l’éducation.
Mots-clés :
- Intelligence artificielle générative,
- robots conversationnels,
- sciences de l’éducation,
- usages,
- utilité,
- littératie,
- intention
Abstract
This paper reports on a study involving 523 undergraduate and master’s students in Education and Training Sciences in France. It aims to identify how these future education professionals use and perceive conversational robots. The method used was a questionnaire based on attitude scales (literacy, perceived usefulness, intention) and semi-structured interviews. The results show a poor understanding of how CRs work, although the students use them routinely and in a variety of ways in both private and academic settings. They are developing strategies that they believe enable them to be more efficient when reviewing and more effective in their academic work. These initial results pave the way for further research into how these new tools can be effectively integrated into higher education, particularly among future education professionals.
Keywords:
- Artificial generative intelligence,
- chatbot,
- educational sciences,
- uses,
- usefulness,
- literacy,
- intention
Appendices
Références
- Abbas, M., Jam, F. A. et Khan, T. I. (2024). Is it harmful or helpful? Examining the causes and consequences of generative AI usage among university students. International Journal of Educational Technology in Higher Education, 21, article 10. https://doi.org/gtm4gs
- Alvarez, L., Ortoleva, G., Sutter Widmer, D., Fritz, M., Bugmann, J., Boéchat-Heer, S. et Ramillon, C. (2024). Future teachers’ beliefs about generative AI. Assessing technology acceptance as students or as aspiring professionals. Journal of Technology and Teacher Education, 32(3), 383‑408. https://doi.org/10.70725/379206cljimb
- Antonenko, P. et Abramowitz, B. (2023). In-service teachers’(mis)conceptions of artificial intelligence in K‑12 science education. Journal of Research on Technology in Education, 55(1), 64‑78. https://doi.org/gt3qps
- Barrett, A. et Pack, A. (2023). Not quite eye to A.I.: Student and teacher perspectives on the use of generative artificial intelligence in the writing process. International Journal of Education Technology in Higher Education, 20, article 59. https://doi.org/g9qk99
- Blais, M. et Martineau, S. (2006). L’analyse inductive générale : description d’une démarche visant à donner un sens à des données brutes. Recherches qualitatives, 26(2), 1‑18. https://doi.org/10.7202/1085369ar
- Chai, C. S., Wang, X. et Xu, C. (2020). An extended theory of planned behavior for the modelling of Chinese secondary school students’ intention to learn artificial intelligence. Mathematics, 8(11), article 2089. https://doi.org/10.3390/math8112089
- Chai, C. S., Yu, D., King, R. B. et Zhou, Y. (2024). Development and validation of the artificial intelligence learning intention scale (AILIS) for university students. SAGE Open, 14(2). https://doi.org/g8qvvh
- 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, article 43. https://doi.org/gshsfg
- Chen, B., Zhu, X. et Castillo, F. D. (2024). Integrating generative AI in knowledge building. Computers and Education: Artificial Intelligence, 5, 100‑184. https://doi.org/10.1016/j.caeai.2023.100184
- Creswell, J. W. et Plano Clark, V. L. (2010). Designing and conducting mixed methods research (2e éd.). Sage.
- Damiano, A. D., Lauría, E. J. M., Sarmiento, C. et Zhao, N. (2024). Early perceptions of teaching and learning using generative AI in higher education. Journal of Educational Technology Systems, 52(3), 346‑375. https://doi.org/psjv
- Daverne-Bailly, C. et Wittorski, R. (2022). Méthodologie de la recherche en sciences de l’éducation et de la formation : postures, pratiques et formes. ISTE éditions.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319‑340.https://doi.org/10.2307/249008
- El Bahlouli, Y. (2024). L’impact pédagogique des agents conversationnels en éducation : revue de littérature scientifique. Le français aujourd’hui, 2024/3(226), 27‑38. https://doi.org/10.3917/lfa.226.0027
- El Karfa, I. (2024). ChatGPT : un outil pour améliorer les compétences rédactionnelles et argumentatives des élèves. Le français aujourd’hui, 2024/3(226), 51‑68. https://doi.org/10.3917/lfa.226.0051
- Fleischmann, K. (2024). Generative artificial intelligence in graphic design education: A student perspective. Canadian Journal of Learning and Technology, 50(1). https://doi.org/10.21432/cjlt28618
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. et Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 63(11), 139‑144. https://doi.org/10.1145/3422622
- Grinbaum, A., Chatila, R., Devillers, L., Martin, C., Kirchner, C., Perrin, J. et Tessier, C. (2023). Systèmes d’intelligence artificielle générative : enjeux d’éthique (avis no 7 du CNPEN). Comité national pilote d’éthique du numérique. https://cea.hal.science/cea-04153216v1
- Guha, A., Grewal, D. et Atlas, S. (2023). Generative AI and marketing education: What the future holds. Journal of Marketing Education, 46(1), 6‑17. https://doi.org/psjx
- Johnston, H., Wells, R. F., Shanks, E. M., Boey, T. et Parsons, B. N. (2024). Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educational Integrity, 20, article 2. https://doi.org/pfgh
- Karnalim, O., Ayub, M. et Kusbiantoro, K. (2024). Perspective of AI chatbots in K‑12 education. Dans Z. Altinay, M. Chang, R. Kuo et A. Tlili (dir.), Proceedings – 2024 IEEE International Conference on Advanced Learning Technologies (ICALT) (p. 239‑241). IEEE Computer Society. https://doi.org/10.1109/ICALT61570.2024.00076
- Kelly, A., Sullivan, M. et Strampel, K. (2023). Generative artificial intelligence: University student awareness, experience, and confidence in use across disciplines. Journal of University Teaching and Learning Practice, 20(6), article 12. https://doi.org/10.53761/1.20.6.12
- Lee, S. et Park, G. (2024). Development and validation of ChatGPT literacy scale. Current Psychology, 43(21), 18992-19004. https://doi.org/gtzp6w
- Lintner, T. (2024). A systematic review of AI literacy scales. Npj Science of Learning, 9, article 50. https://doi.org/g8vpx8
- 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
- Malmström, H., Stöhr, C. et Ou, A. W. (2023). Chatbots and other AI for learning: A survey of use and views among university students in Sweden (Chalmers studies in communication and learning in higher education, rapport no 2023:1). Chalmers University of Technology. https://doi.org/10.17196/cls.csclhe/2023/01
- Mustroph, C. et Steinbock, J. (2024). ChatGPT in foreign language education: Friend or foe? A quantitative study on pre‑service teachers’ beliefs. Technology in Language Teaching & Learning, 6(1).https://doi.org/10.29140/tltl.v6n1.1133
- Paillé, P. et Mucchielli, A. (2012). L’analyse qualitative en sciences humaines et sociales (3e éd.). Armand Colin. https://doi.org/10.3917/arco.paill.2012.01
- Romero, M., Heiser, L. et Lepage, A. (dir.). (2023). Enseigner et apprendre à l’ère de l’intelligence artificielle [livre blanc]. Canopé. https://hal.science/hal-04013223
- Saúde, S., Barros, J. P. et Almeida, I. (2024). Impacts of generative artificial intelligence in higher education: Research trends and students’ perceptions. Social Sciences, 13(8), 4‑10. https://doi.org/10.3390/socsci13080410
- Schiel, J., Bobek, B. L. et Schnieders, J. Z. (2023). High school students’ use and impressions of AI tools. ACT Research [rapport]. ACT, Inc. https://eric.ed.gov/?id=ED638428
- Spatola, N. (2019). L’interaction homme-robot, de l’anthropomorphisme à l’humanisation. L’année psychologique, 119(4), 515‑563. https://doi.org/10.3917/anpsy1.194.0515
- Stöhr, C., Ou, A. W. et Malmström, H. (2024). Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study. Computers and Education: Artificial Intelligence, 7, 100‑259.https://doi.org/10.1016/j.caeai.2024.100259
- The jamovi project. (s.d.). jamovi (version 2.3.28) [logiciel de statistique]. https://jamovi.org
- Urmeneta, A. et Romero, M. (2024). Creative applications of artificial intelligence in education. Palgrave Macmillan. https://doi.org/10.1007/978-3-031-55272-4
- Waluyo, B. et Kusumastuti, S. (2024). Generative AI in student English learning in Thai higher education: More engagement, better outcomes? Social Sciences & Humanities Open, 10, 101‑146. https://doi.org/10.1016/j.ssaho.2024.101146
- Wood, D. et Moss, S.-H. (2024). Evaluating the impact of students’ generative AI use in educational contexts. Journal of Research in Innovative Teaching & Learning, 17(2), 152‑167. https://doi.org/10.1108/JRIT-06-2024-0151
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