Résumés
Abstract
The rapid advancement of artificial intelligence (AI) in education underscores transformative prospects for open and distributed learning, encompassing distance, hybrid, and blended learning environments. This qualitative study, grounded in narrative inquiry, investigates the experiences and perceptions of 141 preservice teachers engaged with AI, mainly through ChatGPT, over a 3-week implementation on Zoom to understand its influence on their evolving professional identities and instructional methodologies. Employing Strauss and Corbin’s methodological approach of open, axial, and selective coding to analyze reflective narratives, the study unveils significant themes that underscore the dual nature of AI in education. Key findings reveal ChatGPT’s role in enhancing educational effectiveness and accessibility while raising ethical concerns regarding academic integrity and balanced usage. Specifically, ChatGPT was found to empower personalized learning and streamline procedures, yet challenges involving information accuracy and data security remained. The study significantly contributes to teacher education discourse by revealing AI’s complex educational impacts, highlighting an urgent need for comprehensive ethical AI literacy in teacher training curricula. However, critical ethical considerations and practical challenges involving academic integrity, information accuracy, and balanced AI use are also brought to light. The research also spotlights the need for responsible AI implementation in open and distributed learning to optimize educational outcomes while addressing potential risks. The study’s insights advocate for future-focused AI literacy frameworks that integrate technological adeptness with ethical considerations, preparing teacher candidates for an intelligent digital educational landscape.
Keywords:
- artificial intelligence (AI),
- ChatGPT,
- AI in education,
- AIED,
- AI in teacher education,
- narrative inquiry
Veuillez télécharger l’article en PDF pour le lire.
Télécharger
Parties annexes
Bibliography
- Adama, E. A., Sundin, D., & Bayes, S. (2016). Exploring the sociocultural aspect of narrative inquiry: A dynamic nursing research methodology. Clinical Nursing Studies, 4(4). https://doi.org/10.5430/cns.v4n4p1
- Addo, S. A., & Sentance, S. (2023, December 14–15). Teachers’ motivation for teaching AI in K-12 settings. In HCAIep ’23: Proceedings of the 2023 Conference on Human Centered Artificial Intelligence: Education and Practice. Association for Computing Machinery. https://doi.org/10.1145/3633083.3633192
- Araka, E., Oboko, R., Maina, E., & Gitonga, R. (2022). Using educational data mining techniques to identify profiles in elf-regulated learning: An empirical evaluation. The International Review of Research in Open and Distributed Learning, 23(1), 131–162. https://doi.org/10.19173/irrodl.v22i4.5401
- Aung, Z. H., Sanium, S., Songsaksuppachok, C., Kusakunniran, W., Precharattana, M., Chuechote, S., Pongsanon, K., & Ritthipravat, P. (2022). Designing a novel teaching platform for AI: A case study in a Thai school context. Journal of Computer Assisted Learning, 38(6), 1714–1729. https://doi.org/10.1111/jcal.12706
- Bower, K. L., Lewis, D. C., & Paulus, T. M. (2021). Using ATLAS for Mac to enact narrative analysis: Metaphor of generativity from LGBT older adult life stories. Qualitative Research, 22(6), 933–950. https://doi.org/10.1177/1468794121999008
- Carvalho, L., Martinez-Maldonado, R., Tsai, Y.-S., Markauskaite, L., & De Laat, M. (2022). How can we design for learning in an AI world? Computers and Education: Artificial Intelligence, 3, Article 100053. https://doi.org/10.1016/j.caeai.2022.100053
- Cavalcanti, A. P., Barbosa, A., Carvalho, R., Freitas, F., Tsai, Y.-S., Gašević, D., & Mello, R. F. (2021). Automatic feedback in online learning environments: A systematic literature review. Computers and Education: Artificial Intelligence, 2, Article 100027. https://doi.org/10.1016/j.caeai.2021.100027
- Celik, I. (2023). Towards intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, Article 107468. https://doi.org/10.1016/j.chb.2022.107468
- Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1), Article 38. https://doi.org/10.1186/s41239-023-00408-3
- Chase, S. (2005). Narrative inquiry: Multiple lenses, approaches, voices. In N. Denzin & Y. Lincoln (Eds.), Sage handbook of qualitative research (3rd ed., pp. 651–679). Sage Publications.
- Cheng, Y.-P., Cheng, S.-C., & Huang, Y.-M. (2022). An Internet articles retrieval agent combined with dynamic associative concept maps to implement online learning in an artificial intelligence course. The International Review of Research in Open and Distributed Learning, 23(1), 63–81. https://doi.org/10.19173/irrodl.v22i4.5437
- Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, Article 100118. https://doi.org/10.1016/j.caeai.2022.100118
- Clandinin, D. J. (2006). Narrative inquiry: A methodology for studying lived experience. Research Studies in Music Education, 27(1), 44–54. https://doi.org/10.1177/1321103X060270010301
- Cohen, L., Manion, L., & Morrison, K. (2017). Research methods in education (8th ed.). Routledge. https://doi.org/10.4324/9781315456539
- Edwards, C., Edwards, A., Spence, P. R., & Lin, X. (2018). I, teacher: Using artificial intelligence (AI) and social robots in communication and instruction. Communication Education, 67(4), 473–480. https://doi.org/10.1080/03634523.2018.1502459
- ElSayary, A. (2024). An investigation of teachers’ perceptions of using ChatGPT as a supporting tool for teaching and learning in the digital era. Journal of Computer Assisted Learning, 40(3), 931–945. https://doi.org/10.1111/jcal.12926
- García-Peñalvo, F. J. (2023). La percepción de la Inteligencia Artificial en contextos educativos tras el lanzamiento de ChatGPT: disrupción o pánico [The perception of artificial intelligence in educational contexts after the launch of ChatGPT: Disruption or panic?]. Education in the Knowledge Society (EKS), 24, Article e31279. https://doi.org/10.14201/eks.31279
- Gentile, M., Città, G., Perna, S., & Allegra, M. (2023). Do we still need teachers? Navigating the paradigm shift of the teacher’s role in the AI era. Frontiers in Education, 8, Article 1161777. https://doi.org/10.3389/feduc.2023.1161777
- Guilherme, A. (2019). AI and education: The importance of teacher and student relations. AI & Society, 34(1), 47–54. https://doi.org/10.1007/s00146-017-0693-8
- Hashem, R., Ali, N., Zein, F. E., Fidalgo, P., & Khurma, O. A. (2024). AI to the rescue: Exploring the potential of ChatGPT as a teacher ally for workload relief and burnout prevention. Research and Practice in Technology Enhanced Learning, 19, Article 23. https://doi.org/10.58459/rptel.2024.19023
- Heintz, F. (2021). Three interviews about K-12 AI education in America, Europe, and Singapore. KI—Künstliche Intelligenz, 35(2), 233–237. https://doi.org/10.1007/s13218-021-00730-w
- Henry, J., Hernalesteen, A., & Collard, A.-S. (2021). Teaching artificial intelligence to K-12 through a role-playing game questioning the intelligence concept. KI—Künstliche Intelligenz, 35(2), 171–179. https://doi.org/10.1007/s13218-021-00733-7
- Hsu, T.-C., Abelson, H., & Van Brummelen, J. (2022). The effects on secondary school students of applying experiential learning to the conversational AI learning curriculum. The International Review of Research in Open and Distributed Learning, 23(1), 82–103. https://doi.org/10.19173/irrodl.v22i4.5474
- Hwang, G.-J., Tu, Y.-F., & Tang, K.-Y. (2022). AI in online-learning research: Visualizing and interpreting the journal publications from 1997 to 2019. The International Review of Research in Open and Distributed Learning, 23(1), 104–130. https://doi.org/10.19173/irrodl.v23i1.6319
- Jiang, P., Namaziandost, E., Azizi, Z., & Razmi, M. H. (2022). Exploring the effects of online learning on EFL learners’ motivation, anxiety, and attitudes during the COVID-19 pandemic: A focus on Iran. Current Psychology, 42(3), 2310–2324. https://doi.org/10.1007/s12144-022-04013-x
- Kaminski, E. (2003). Promoting preservice teacher education students’ reflective practice in mathematics. Asia-Pacific Journal of Teacher Education, 31(1), 21–32. https://doi.org/10.1080/13598660301619
- Karataş, F., Abedi, F. Y., Ozek Gunyel, F., Karadeniz, D., & Kuzgun, Y. (2024). Incorporating AI in foreign language education: An investigation into ChatGPT’s effect on foreign language learners. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12574-6
- Kayima, F. (2021). The role of reflective practice in mediating development of preservice science teachers’ professional and classroom knowledge. Interdisciplinary Journal of Environmental and Science Education, 18(1), Article e2262. https://doi.org/10.21601/ijese/11364
- Keeley, K. (2023). AI-assisted teacher wellness: Theory and practice. In S. Hai-Jew (Ed.), Generative AI in teaching and learning (pp. 201–216). IGI Global. https://doi.org/10.4018/979-8-3693-0074-9.ch008
- Kim, J., Lee, H., & Cho, Y. H. (2022). Learning design to support student-AI collaboration: Perspectives of leading teachers for AI in education. Education and Information Technologies, 27(5), 6069–6104. https://doi.org/10.1007/s10639-021-10831-6
- Kim, K., & Kwon, K. (2023). Exploring the AI competencies of elementary school teachers in South Korea. Computers and Education: Artificial Intelligence, 4, Article 100137. https://doi.org/10.1016/j.caeai.2023.100137
- Kutsyuruba, B., & Stasel, R. S. (2023). Narrative inquiry. In J. M. Okoko, S. Tunison, & K. D. Walker (Eds.), Varieties of qualitative research methods (pp. 325–332). Springer. https://doi.org/10.1007/978-3-031-04394-9_51
- Lawrence, L., Echeverria, V., Yang, K., Aleven, V., & Rummel, N. (2024). How teachers conceptualise shared control with an AI co‐orchestration tool: A multiyear teacher‐centred design process. British Journal of Educational Technology, 55(3), 823–844. https://doi.org/10.1111/bjet.13372
- Le-Nguyen, H.-T., & Tran, T. T. (2023). Generative AI in terms of its ethical problems for both teachers and learners: Striking a balance. In S. Hai-Jew (Ed.), Generative AI in teaching and learning (pp. 144–173). IGI Global. https://doi.org/10.4018/979-8-3693-0074-9.ch006
- Liu, G. L., & Wang, Y. (2024). Modeling EFL teachers’ intention to integrate informal digital learning of English (IDLE) into the classroom using the theory of planned behavior. System, 120, Article 103193. https://doi.org/10.1016/j.system.2023.103193
- McGovern, A., & Fager, J. (2007, March). Creating significant learning experiences in introductory artificial intelligence. SIGCSE ’07: Proceedings of the 38th SIGCSE Technical Symposium on Computer Science Education (pp. 39–43). Association for Computing Machinery. https://doi.org/10.1145/1227310.1227325
- Nazaretsky, T., Ariely, M., Cukurova, M., & Alexandron, G. (2022). Teachers’ trust in AI-powered educational technology and a professional development program to improve it. British Journal of Educational Technology, 53(4), 914–931. https://doi.org/10.1111/bjet.13232
- Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2022). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies, 28(7), 8445–8501. https://doi.org/10.1007/s10639-022-11491-w
- Ng, D. T K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041
- Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137–161. https://doi.org/10.1007/s11423-023-10203-6
- Özdil, B. M., & Kunt, N. (2023). Do bi/multilingual learners play by the rules of the game? A postmodern approach to L1/L2 use and learner investment. Journal of Language, Identity & Education. Advance online publication. https://doi.org/10.1080/15348458.2023.2180372
- Rodriguez, M. E., Guerrero-Roldán, A. E., Baneres, D., & Karadeniz, A. (2022). An intelligent nudging system to guide online learners. The International Review of Research in Open and Distributed Learning, 23(1), 41–62. https://doi.org/10.19173/irrodl.v22i4.5407
- Strauss, A., & Corbin, J. (2008). Basics of qualitative research (3rd ed.): Techniques and procedures for developing grounded theory. Sage Publications. https://doi.org/10.4135/9781452230153
- Tan, K.-H., & Lim, B. P. (2018). The artificial intelligence renaissance: Deep learning and the road to human-level machine intelligence. APSIPA Transactions on Signal and Information Processing, 7(1), Article e6. https://doi.org/10.1017/atsip.2018.6
- Tzeng, J.-W., Lee, C.-A., Huang, N.-F., Huang, H.-H., & Lai, C.-F. (2022). MOOC evaluation system based on deep learning. The International Review of Research in Open and Distributed Learning, 23(1), 21–40. https://doi.org/10.19173/irrodl.v22i4.5417
- Vadivel, B., Namaziandost, E., Rezai, A., & Azizi, Z. (2023). A paradigm shift in teaching and learning due to the COVID-19 pandemic: Areas of potential and challenges of online classes. English as a Foreign Language International Journal, 27(2). https://doi.org/10.56498/5062722023
- Wang, Y. (2023). Probing into the boredom of online instruction among Chinese English language teachers during the COVID-19 pandemic. Current Psychology, 43, 12144–12158. https://doi.org/10.1007/s12144-022-04223-3
- Wang, Y., & Lu, X. (2023). Research on the evaluation and cultivation design of AI literacy to promote the development of cognitive Intelligence among pre-service teachers. Proceedings—2023 International Conference on Culture-Oriented Science and Technology, CoST 2023 (pp. 116–120). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CoST60524.2023.00032
- Wang, Y., Pan, Z., & Wang, M. (2023). The moderating effect of participation in online learning activities and perceived importance of online learning on EFL teachers’ teaching ability. Heliyon, 9(3), Article e13890. https://doi.org/10.1016/j.heliyon.2023.e13890
- Xu, S., & Connelly, M. (2010). Narrative inquiry for school-based research. Narrative Inquiry, 20(2), 349–370. https://doi.org/10.1075/NI.20.2.06XU
- Zhi, R., Wang, Y., & Wang, Y. (2023). The role of emotional intelligence and self-efficacy in EFL teachers’ technology adoption. The Asia-Pacific Education Researcher. Advance online publication. https://doi.org/10.1007/s40299-023-00782-6