AI literacy of pre-service English teachers: Proficiency, strategies, and barriers

Authors

  • Febriyanti English Department, STKIP PGRI Bandar Lampung, Indonesia
  • Eva Nurchurifiani English Department, STKIP PGRI Bandar Lampung, Indonesia
  • Tommy Hastomo English Department, STKIP PGRI Bandar Lampung, Indonesia
  • Andri Wicaksono Primary School Teacher Education Department, STKIP PGRI Bandar Lampung, Indonesia
  • Sukma Septian Nasution English and Linguistics Department, University of Otago, New Zealand

DOI:

https://doi.org/10.30762/jeels.v13i1.6293

Keywords:

AI literacy, ELT, learning strategies, mixed-methods research, pre-service teachers

Abstract

Artificial intelligence (AI) has become increasingly significant in higher education, making multidimensional AI literacy essential for pre-service English teachers. Yet, empirical evidence in English Language Teaching (ELT) remains limited, particularly regarding students’ AI literacy profiles, the autonomous strategies they use to develop such literacy, and the barriers affecting responsible AI use. This study employed an explanatory sequential mixed-method design in Indonesia. Quantitative data were collected through a 30-item AI literacy questionnaire administered to 200 pre-service English teachers, followed by semi- structured interviews and focus group discussions with a purposive subsample of 25 participants. The findings indicate an uneven AI literacy profile. Although participants demonstrated strong competence in the practical use of AI tools, they showed weaker performance in higher-order dimensions, especially evaluative and ethical aspects. The qualitative data further revealed that students developed AI literacy through self-directed strategies such as prompt experimentation and output verification, but these efforts were constrained by limited institutional guidance and persistent ethical uncertainty. The study suggests that teacher education programs should incorporate structured AI literacy instruction supported by clear institutional policies to strengthen critical, reflective, and responsible AI engagement in ELT contexts.

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References

Abbas, M., Jam, F. A., & 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(1), 1–22. https://doi.org/10.1186/s41239-024-00444-7

Alam, A., & Mohanty, A. (2023). Educational technology: Exploring the convergence of technology and pedagogy through mobility, interactivity, AI, and learning tools. Cogent Engineering, 10(2), 1–37. https://doi.org/10.1080/23311916.2023.2283282

Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Computers and Education Open, 6, 100173. https://doi.org/10.1016/j.caeo.2024.100173

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(1), 10. https://doi.org/10.1186/s41239-025-00507-3

Braun, V., Clarker, V., & Rance, N. (2015). How to use thematic analysis with interview data. In A. Vossler & N. Moller (Eds.), The Counselling & Psychotherapy Research Handbook, (pp. 183–197). Sage. https://doi.org/10.4135/9781473909847.n13

Cardon, P., Fleischmann, C., Aritz, J., Logemann, M., & Heidewald, J. (2023). The challenges and opportunities of AI-assisted writing: Developing AI literacy for the AI age. Business and Professional Communication Quarterly, 86(3), 257–295. https://doi.org/10.1177/2329490623117651

Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014

Chiu, T. K. F., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171

Creswell, J. W., & Clark, V. L. P. (2017). Designing and Conducting Mixed Methods Research. Sage Publications.

Dakakni, D., & Safa, N. (2023). Artificial intelligence in the L2 classroom: Implications and challenges on ethics and equity in higher education: A 21st century Pandora’s box. Computers and Education: Artificial Intelligence, 5, 100179. https://doi.org/10.1016/j.caeai.2023.100179

Deng, G., & Zhang, J. (2023). Technological pedagogical content ethical knowledge (TPCEK): The development of an assessment instrument for pre-service teachers. Computers & Education, 197, 104740. https://doi.org/10.1016/j.compedu.2023.104740

Ferguson, F., & Takane, Y. (1989). Statistical analysis in psychology and education. McGraw-Hill,.

Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 1–28. https://doi.org/10.3390/soc15010006

Haetami. (2025). AI-Driven educational transformation in Indonesia: From learning personalization to institutional management. AL-ISHLAH: Jurnal Pendidikan, 17(2), 1819–1832. https://doi.org/10.35445/alishlah.v17i2.7448

Harrington, D. (2009). Confirmatory factor analysis. Oxford University Press.

Hastomo, T., Mandasari, B., & Widiati, U. (2024). Scrutinizing Indonesian pre-service teachers’ technological knowledge in utilizing AI-powered tools. Journal of Education and Learning (EduLearn), 18(4), 1572–1581. https://doi.org/10.11591/edulearn.v18i4.21644

Helmiatin, Hidayat, A., & Kahar, M. R. (2024). Investigating the adoption of AI in higher education: A study of public universities in Indonesia. Cogent Education, 11(1), 1–15. https://doi.org/10.1080/2331186X.2024.2380175

Jin, Y., Martinez-Maldonado, R., Gašević, D., & Yan, L. (2025). GLAT: The generative AI literacy assessment test. Computers and Education: Artificial Intelligence, 9, 100436. https://doi.org/10.1016/j.caeai.2025.100436

Jin, Y., Yan, L., Echeverria, V., Gašević, 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

Laupichler, M. C., Aster, A., Meyerheim, M., Raupach, T., & Mergen, M. (2024). Medical students’ AI literacy and attitudes towards AI: a cross-sectional two-center study using pre-validated assessment instruments. BMC Medical Education, 24(1), 401. https://doi.org/10.1186/s12909-024-05400-7

Lee, I., Ali, S., Zhang, H., DiPaola, D., & Breazeal, C. (2021). Developing Middle School Students’ AI Literacy. Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, 191–197. https://doi.org/10.1145/3408877.3432513

Lintner, T. (2024). A systematic review of AI literacy scales. Npj Science of Learning, 9(1), 50. https://doi.org/10.1038/s41539-024-00264-4

Long, D., Roberts, J., Magerko, B., Holstein, K., DiPaola, D., & Martin, F. (2023). AI literacy: Finding common threads between education, design, policy, and explainability. Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, 1–6. https://doi.org/10.1145/3544549.3573808

McDonald, N., Johri, A., Ali, A., & Collier, A. H. (2025). Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines. Computers in Human Behavior: Artificial Humans, 3, 100121. https://doi.org/10.1016/j.chbah.2025.100121

Morgan, A., Sibson, R., & Jackson, D. (2022). Digital demand and digital deficit: conceptualising digital literacy and gauging proficiency among higher education students. Journal of Higher Education Policy and Management, 44(3), 258–275. https://doi.org/10.1080/1360080X.2022.2030275

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

Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082–1104. https://doi.org/10.1111/bjet.13411

Schiff, D. (2022). Education for AI, not AI for Education: The Role of Education and Ethics in National AI Policy Strategies. International Journal of Artificial Intelligence in Education, 32(3), 527–563. https://doi.org/10.1007/s40593-021-00270-2

Stolpe, K., & Hallström, J. (2024). Artificial intelligence literacy for technology education. Computers and Education Open, 6, 100159. https://doi.org/10.1016/j.caeo.2024.100159

Wu, Y., Zhang, W., & Lin, C. (2025). Generative Artificial Intelligence in University Education. IT Professional, 27(2), 69–74. https://doi.org/10.1109/MITP.2025.3545629

Zhang, J., & Zhang, Z. (2024). AI in teacher education: Unlocking new dimensions in teaching support, inclusive learning, and digital literacy. Journal of Computer Assisted Learning, 40(4), 1871–1885. https://doi.org/10.1111/jcal.12988

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Published

24-04-2026

How to Cite

Febriyanti, Nurchurifiani, E., Hastomo, T., Wicaksono, A., & Nasution, S. S. (2026). AI literacy of pre-service English teachers: Proficiency, strategies, and barriers. JEELS (Journal of English Education and Linguistics Studies), 13(1), 155–186. https://doi.org/10.30762/jeels.v13i1.6293

Issue

Vol. 13 No. 1 (2026): JEELS May 2026

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Articles

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