Teacher Professional Reasoning for AI-Mediated Language Teaching Under Multi-Level AI Governance: An Embedded Multiple-Case Study
Keywords:
AI governance; English as a Foreign Language; generative artificial intelligence; professional reasoning; teacher professionalismAbstract
Generative artificial intelligence is reshaping teaching, learning, assessment, and professional practice in English as a Foreign Language. However, little is known about how university teachers exercise professional reasoning within emerging AI governance environments. This embedded qualitative multiple-case study explored how university teachers of English as a Foreign Language in Vietnam reasoned professionally about generative artificial intelligence. The study combined semi-structured interviews with 13 teachers from 13 universities and an analysis of 88 policy and governance documents comprising 10 national, 7 ministerial, and 71 institutional documents. A documented search, screening, inventory, and staged coding procedure enabled the systematic identification, classification, and analysis of the document corpus. Interview and documentary data were analysed separately, first within cases and governance levels and then across cases using matrices and joint displays. Findings showed that teachers' professional reasoning was developed through continual negotiation between classroom realities and multi-level AI governance. Participants moved beyond their traditional roles as language experts to become learning designers, pedagogical facilitators, critical mentors, and ethical guides. They set context-appropriate boundaries for AI use, reframed ethics in terms of transparency and cognitive engagement, and adapted assessment to preserve evidence of student learning. The documents revealed a layered but uneven governance landscape. National strategies articulated broad directions; ministerial documents provided partial guidance; and institutional policies varied considerably. The integration of the two datasets showed that AI literacy, governance, assessment, ethics, and teacher agency were interconnected with teachers’ professional reasoning. The study proposed the governance-informed Identity-Agency-Ethics-Assessment Practices heuristic for understanding AI-mediated teacher professionalism.
https://doi.org/10.26803/ijlter.25.9.29
References
Al-Ghamdi, S. M. M., Albahiri, M. H., & Alhaj, A. A. M. (2026). Teachers’ perceptions of the feasibility of using generative artificial intelligence in developing multiple-choice questions for formative assessment in Saudi Arabia. International Journal of Learning, Teaching and Educational Research, 25(4), 160–184.
https://doi.org/10.26803/ijlter.25.4.8
An, Y., Yu, J. H., & James, S. (2025). Investigating the higher education institutions' guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration. International Journal of Educational Technology in Higher Education, 22, Article 10. https://doi.org/10.1186/s41239-025-00507-3
Beijaard, D., Meijer, P. C., & Verloop, N. (2004). Reconsidering research on teachers’ professional identity. Teaching and Teacher Education, 20(2), 107–128.
https://doi.org/10.1016/j.tate.2003.07.001
Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40. https://doi.org/10.3316/QRJ0902027
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1471088706qp063oa
Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. Sage Publications.
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, Article 38. https://doi.org/10.1186/s41239-023-00408-3
Clarke, M. (2008). Language teacher identities: Co-constructing discourse and community. Multilingual Matters. https://doi.org/10.21832/9781847690838
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
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228–239. https://doi.org/10.1080/14703297.2023.2190148
Cui, Y., Meng, Y., & Tang, L. (2025). Reconsidering teacher assessment literacy in GenAI-enhanced environments: A scoping review. Teaching and Teacher Education, 165, 105163. https://doi.org/10.1016/j.tate.2025.105163
Emans, A., Oolbekkink-Marchand, H., Bakker, C., & De Bruijn, E. (2025). Teacher agency in the dynamics of educational practices: A theory synthesis. Frontiers in Education, 9, 1515123. https://doi.org/10.3389/feduc.2024.1515123
Farazouli, A., Cerratto Pargman, T., Bolander Laksov, K., & McGrath, C. (2025). Navigating uncertainty: University teachers’ experiences and perceptions of generative artificial intelligence in teaching and learning. Studies in Higher Education. Advance online publication. https://doi.org/10.1080/03075079.2025.2550766
Foung, D., Lin, L., & Chen, J. (2024). Reinventing assessments with ChatGPT and other online tools: Opportunities for GenAI-empowered assessment practices. Computers and Education: Artificial Intelligence, 6, 100250.
https://doi.org/10.1016/j.caeai.2024.100250
Huang, T., Wu, C., & Wu, M. (2025). Developing pre-service language teachers' GenAI literacy: An interventional study in an English language teacher education course. Discover Artificial Intelligence, 5, Article 163. https://doi.org/10.1007/s44163-025-00435-1
Jin, Y., Yan, L., Echeverria, V., Gaševi?, 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
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
https://doi.org/10.1016/j.lindif.2023.102274
Kavanagh, S. S., Conrad, J., & Dagogo-Jack, S. (2020). From rote to reasoned: Examining the role of pedagogical reasoning in practice-based teacher education. Teaching and Teacher Education, 89, Article 102991. https://doi.org/10.1016/j.tate.2019.102991
Kofinas, A. K., Tsay, C. H.-H., & Pike, D. (2025). The impact of generative AI on academic integrity of authentic assessments within a higher education context. British Journal of Educational Technology, 56(6), 2522–2549. https://doi.org/10.1111/bjet.13585
Kohnke, L., Zou, D., Ou, A. W., & Gu, M. M. (2025). Preparing future educators for AI-enhanced classrooms: Insights into AI literacy and integration. Computers and Education: Artificial Intelligence, 8, 100398.
https://doi.org/10.1016/j.caeai.2025.100398
Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., Lekkas, D., & Palmer, E. (2024). The impact of generative AI on higher education learning and teaching: A study of educators' perspectives. Computers and Education: Artificial Intelligence, 6, 100221. https://doi.org/10.1016/j.caeai.2024.100221
Loughran, J. (2019). Pedagogical reasoning: The foundation of the professional knowledge of teaching. Teachers and Teaching, 25(5), 523–535.
https://doi.org/10.1080/13540602.2019.1633294
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
McDonald, N., Johri, A., Ali, A., & Hingle, A. (2024). Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines. arXiv. https://arxiv.org/abs/2402.01659
Messick, S. (1989). Validity. In R. L. Linn (Ed.), Educational measurement (3rd ed., pp. 13–103). Macmillan.
Moorhouse, B. L., Wan, Y., Wu, C., Kohnke, L., Ho, T. Y., & Kwong, T. (2024). Developing language teachers’ professional generative AI competence: An intervention study in an initial language teacher education course. System, 125, 103399.
https://doi.org/10.1016/j.system.2024.103399
National Assembly of Vietnam. (2025). Law on Artificial Intelligence (Law No. 134/2025/QH15). Thu Vien Phap Luat ?Law library?.
Ngo, P. A. (2026). Implementing a flipped blended learning model in EFL instruction for engineering students: A qualitative evaluation. Arab World English Journal, 17(1), 339–356. https://dx.doi.org/10.24093/awej/vol17no1.21
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, Article 267. https://doi.org/10.1007/s10791-025-09745-5
Priestley, M., Biesta, G., & Robinson, S. (2015). Teacher agency: An ecological approach. Bloomsbury Academic. https://doi.org/10.5040/9781474219426
Prilop, C. N., Mah, D.-K., Jacobsen, L. J., Hansen, R. R., Weber, K. E., & Hoya, F. (2025). Generative AI in teacher education: Educators’ perceptions of transformative potentials and the triadic nature of AI literacy explored through AI-enhanced methods. Computers and Education: Artificial Intelligence, 9, 100471.
https://doi.org/10.1016/j.caeai.2025.100471
Ruiz, C. C., & Yambao, F. M. (2026). AI literacy and professional development engagement as predictors of mathematics teachers’ readiness for AI-enhanced instruction. International Journal of Learning, Teaching and Educational Research, 25(2), 655–675.
https://doi.org/10.26803/ijlter.25.2.29
Shenoy, P., & Saarela, M. (2026). A critical review and actionable framework for integrating generative AI into teacher professional development. Discover Education, 5, Article 570. https://doi.org/10.1007/s44217-026-01579-7
Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 57(1), 1–22.
https://doi.org/10.17763/haer.57.1.j463w79r56455411
Shulman, L. S. (1998). Theory, practice, and the education of professionals. The Elementary School Journal, 98(5), 511–526. https://doi.org/10.1086/461912
Sperling, K., Stenberg, C.-J., McGrath, C., Åkerfeldt, A., Heintz, F., & Stenliden, L. (2024). In search of artificial intelligence (AI) literacy in teacher education: A scoping review. Computers and Education Open, 6, 100169. https://doi.org/10.1016/j.caeo.2024.100169
Squires, A. (2009). Methodological challenges in cross-language qualitative research: A research review. International Journal of Nursing Studies, 46(2), 277–287.
https://doi.org/10.1016/j.ijnurstu.2008.08.006
Tan, X., Cheng, G., & Ling, M. H. (2025). Artificial intelligence in teaching and teacher professional development: A systematic review. Computers and Education: Artificial Intelligence, 8, 100355. https://doi.org/10.1016/j.caeai.2024.100355
Temple, B., & Young, A. (2004). Qualitative research and translation dilemmas. Qualitative Research, 4(2), 161–171. https://doi.org/10.1177/1468794104044430
Tong, A., Sainsbury, P., & Craig, J. (2007). Consolidated criteria for reporting qualitative research (COREQ): A 32-item checklist for interviews and focus groups. International Journal for Quality in Health Care, 19(6), 349–357.
https://doi.org/10.1093/intqhc/mzm042
United Nations Educational, Scientific and Cultural Organization. (2023). Guidance for generative AI in education and research. UNESCO Publishing.
https://unesdoc.unesco.org/ark:/48223/pf0000386693
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage Publications.
Zhang, Y., Lai, C., & Gu, M. M. Y. (2025). Becoming a teacher in the era of AI: A multiple-case study of pre-service teachers’ investment in AI-facilitated learning-to-teach practices. System, 133, 103746. https://doi.org/10.1016/j.system.2025.103746
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