RETHINKING TEACHER COMPETENCIES: AI LITERACY AMONG ELT TEACHERS IN UZBEKISTAN
DOI:
https://doi.org/10.65164/rmvh7n82Keywords:
AI literacy, ELT teachers, teacher competencies, generative AI, teacher education, UzbekistanAbstract
The rapid integration of generative AI into higher education has transformed English language teaching (ELT), challenging traditional understandings of teacher competencies. Although the debate on AI tools and student learning has been a subject of research, the impact of AI literacy on teachers' professional roles has received less scholarly attention. This qualitative research is based on the multidimensional framework of AI literacy and examines the views of in-service ELT teachers in Uzbekistan on AI literacy following professional development training. Ten teachers (n=10) from higher education institutions were interviewed using semi-structured methods, with thematic saturation used to determine the number of participants. The results show that AI literacy encompasses more than just knowledge of the technology; it is defined by four interconnected skills: Critical Evaluation of AI-generated content, Prompt Literacy, AI-responsive assessment redesign, and Ethical Mediation. Teachers were more likely to shift their role from a passive user of AI tools to an evaluator and critical mediator. In particular, assessment methods progressed from product assessment to assessments focused on process and transparency. The study suggested the need to introduce the concept of AI literacy as a multi-dimensional professional competency to be incorporated into ELT teacher education programs. Recommendations for curriculum reform, professional development program design, and policy on AI literacy in the context of Higher education in Central Asia are presented.
References
1. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
2. Boud, D., & Molloy, E. (Eds.). (2013). Feedback in higher and professional education: Understanding it and doing it well. Routledge. https://doi.org/10.4324/9780203074336
3. Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE.
735
4. Ebrahimi, Z., Shakib Kotamjani, S., Qosimov, A., & Xodabande, I. (2025). Mapping the emerging research landscape on applications of ChatGPT in language teacher education: A systematic narrative literature review. Discover Artificial Intelligence, 5, Article 349.https://doi.org/10.1007/s44163-025-00631-z
5. Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903
6. 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., Pfeiffer, F., 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
7. Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868
8. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE.
9. Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727
10. 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, 100041. https://doi.org/10.1016/j.caeai.2021.100041
11. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15. https://doi.org/10.1186/s40561-023-00237-x
12. Zhai, X. (2022). ChatGPT user experience: Implications for education. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4312418
13. 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, 107468. https://doi.org/10.1016/j.chb.2022.107468
14. Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL IOE Press.
15. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
16. Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The Digital Competence Framework for Citizens. Publications Office of the European Union. https://doi.org/10.2760/115376
17. White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023). A prompt pattern catalog to enhance prompt engineering with ChatGPT. arXiv. https://doi.org/10.48550/arXiv.2302.11382
18. Zamfirescu-Pereira, J. D., Wong, R. Y., Hartmann, B., & Yang, Q. (2023). Why Johnny can’t prompt: How non-AI experts try (and fail) to design LLM prompts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1–21). ACM. https://doi.org/10.1145/3544548.3581388
