Indonesian Pre-Service EFL Teachers’ Perceptions and Expectations of Generative AI in Teacher Education: A Phenomenological Study
DOI:
https://doi.org/10.33394/jo-elt.v12i2.18032Keywords:
Generative AI, EFL, Pre-service teachersAbstract
The rapid growth of Generative Artificial Intelligence (GenAI) in education has created an urgent need to understand how future teachers perceive its benefits, risks, and required skills, especially in the Indonesian context. This current study aims to explore Indonesian EFL pre-service teachers’ perspectives toward GenAI and their expectations regarding educational content related to GenAI at teacher training programs. Employing a qualitative approach with phenomenology design, the data in this study were obtained through reflective writings and semi-structured interviews involving pre-service teachers from the English Language Education Department at an Indonesian state Islamic university. The findings of thematic analysis reveal that they perceive GenAI benefits teachers by serving as a brainstorming partner for designing learning activities and saving time, while also supporting students through personalized learning experiences and instant feedback that can enhance their performance. Despite its advantages, GenAI poses challenges such as its occasional unreliability for teachers and potential overreliance that may hinder original thinking and professional growth. Meanwhile, overreliance on AI could lower students’ interaction and critical thinking, increase plagiarism risk, and foster the perception that AI is more capable than teachers. Teacher training programs are supposed to address it by focusing on three aspects, namely GenAI literacy, pedagogical knowledge, and ethical considerations. The findings imply that teacher education programs must systematically integrate GenAI literacy, pedagogical application, and ethical guidance to develop competence in leveraging AI effectively while maintaining critical thinking, thoughtful instructional autonomy, and responsible professional practice.
References
Abdulayeva, A., Zhanatbekova, N., Andasbayev, Y., & Boribekova, F. (2025). Fostering AI literacy in pre-service physics teachers: Inputs from training and co-variables. Frontiers in Education, 10, 1505420. https://doi.org/10.3389/feduc.2025.1505420
Alshraah, S. M., Kariem, N., Alshraah, A. M., Aldosemani, T. I., & AlQarni, K. (2024). A critical look at how lecturers in linguistics can leverage generative artificial intelligence in enhancing teaching proficiency and students’ engagement. Journal of Language Teaching and Research, 15(4), 1361–1371. https://doi.org/10.17507/jltr.1504.34
Andewi, W., Waziana, W., Wibisono, D., Putra, K. A., Hastomo, T., & Oktarin, I. B. (2025). From prompting to proficiency: A mixed-methods analysis of prompting with ChatGPT versus lecturer interaction in an EFL classroom. Journal of Studies in the English Language, 20(2), 210–238.
Barbieri, W., & Nguyen, N. (2025). Generative AI as a placement buddy: Supporting pre-service teachers in work-integrated learning, self-management and crisis resolution. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.10035
Birt, L., Scott, S., Cavers, D., Campbell, C., & Walter, F. (2016). Member checking: A tool to enhance trustworthiness or merely a nod to validation? Qualitative Health Research, 26(13), 1802–1811. https://doi.org/10.1177/1049732316654870
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Taylor & Francis.
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
Dell’Erba, C., & Bevilacqua, A. (2025). Embracing or resisting? Exploring pre-service teachers’ engagement with generative artificial intelligence in multimedia representations. In EDULEARN25 Proceedings (pp. 5781–5791). https://doi.org/10.21125/edulearn.2025.1433
Gao, M., Zhang, H., Dong, Y., & Li, J. (2025). Embracing generative AI in education: An experiential study on preservice teachers’ acceptance and attitudes. Educational Studies, 1–20. https://doi.org/10.1080/03055698.2025.2483831
Huang, H.-W., Chen, X., & Sankey, A. (2024). Leveraging multimodal GenAI chatbots in EFL learning: Learning attitudes and user experiences. In Proceedings of the 2024 International Conference on Artificial Intelligence and Teacher Education (pp. 22–29). https://doi.org/10.1145/3702386.3702406
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(1), Article 163. https://doi.org/10.1007/s44163-025-00435-1
Jasper, M. A. (2005). Using reflective writing within research. Journal of Research in Nursing, 10(3), 247–260. https://doi.org/10.1177/174498710501000303
Kim, R. (2025). Generational gap in accepting AI integration in Korean EFL classrooms: Comparing pre-service and in-service teachers within the technology acceptance model. Modern English Education, 26(1), 113–129. https://doi.org/10.18095/meeso.2025.26.1.113
Kurt, G. (2025). A qualitative exploration of pre-service English teachers’ integration of generative artificial intelligence in corpus-based language pedagogy. Computer Assisted Language Learning, 1–34. https://doi.org/10.1080/09588221.2025.2552109
Lee, S., Choe, H., Zou, D., & Jeon, J. (2025). Generative AI (GenAI) in the language classroom: A systematic review. Interactive Learning Environments, 1–25. https://doi.org/10.1080/10494820.2025.2498537
Lee, S., Jeon, J., & Choe, H. (2025). Generative AI (GenAI) and pre-service teacher agency in ELT. ELT Journal, 79(2), 287–296. https://doi.org/10.1093/elt/ccaf005
Lee, S., Jeon, J., McKinley, J., & Rose, H. (2025). Generative AI and English language teaching: A global Englishes perspective. Annual Review of Applied Linguistics, 1–24. https://doi.org/10.1017/S0267190525100184
Lim, F. V., & Xin, T. X. (2025). “If ChatGPT was used … then it shouldn’t be graded.” In G. C. Zapata (Ed.), Generative AI technologies, multiliteracies, and language education (pp. 118–135). Routledge. https://doi.org/10.4324/9781003531685-7
Liu, Q., Chou, J., & Feng, H. (2025). Effect of teachers’ self-efficacy with generative AI and reflection on students’ second language achievement. Computer Assisted Language Learning, 1–22. https://doi.org/10.1080/09588221.2025.2498095
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). https://doi.org/10.1145/3313831.3376727
Meegan, J., & Young, K. (2025). It’s a tool not a crutch: A pilot generative AI intervention to enhance pre-service teachers’ self-efficacy and AI literacy. Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-025-09875-1
Moorhouse, B. L., & Kohnke, L. (2024). The effects of generative AI on initial language teacher education: The perceptions of teacher educators. System, 122, 103290. https://doi.org/10.1016/j.system.2024.103290
Moustakas, C. (1994). Phenomenological research methods. SAGE Publications. https://doi.org/10.4135/9781412995658
Nair, H. B. (2025). Building teacher capacity for effective integration of GenAI into classroom. In M. Elkhodr & E. Gide (Eds.), Generative artificial intelligence empowered learning (pp. 192–206). Chapman and Hall/CRC. https://doi.org/10.1201/9781003422433-9
Negara, I. K. A., Adnyani, M. A. A., Santosa, M. H., & Rewa, G. A. B. (2025). Challenges and effectiveness of novice teachers’ technology integration in an Islamic secondary school in North Bali. Jo-ELT (Journal of English Language Teaching), 12(1), Article 25. https://doi.org/10.33394/jo-elt.v12i1.14414
Nyaaba, M., Shi, L., Nabang, M., Zhai, X., Kyeremeh, P., Ayoberd, S. A., & Akanzire, B. N. (2024). Generative AI as a learning buddy and teaching assistant: Pre-service teachers’ uses and attitudes. arXiv. https://doi.org/10.48550/arXiv.2407.11983
Parviz, M., & Arthur, F. (2025). AI anxiety in English language education: A study of Iranian EFL teachers’ perceptions and demographic influences. International Journal of Computer-Assisted Language Learning and Teaching, 15(1), 1–21. https://doi.org/10.4018/IJCALLT.386135
Qian, Y. (2025). Pedagogical applications of generative AI in higher education: A systematic review of the field. TechTrends. https://doi.org/10.1007/s11528-025-01100-1
Uralova, N. (2024). Exploring ChatGPT integration in ELT pre-service teacher education curriculum: Perception and pedagogical implications. In Proceedings of the 8th International Conference on Future Networks & Distributed Systems (pp. 981–985). https://doi.org/10.1145/3726122.3726265
Van Wyk, M. M. (2025). Student teachers’ leveraging GenAI tools for academic writing, design, and prompting in an ODeL course. Open Praxis, 17(1), 95–107. https://doi.org/10.55982/openpraxis.17.1.711
Wang, K., Ruan, Q., Zhang, X., Fu, C., & Duan, B. (2024). Pre-service teachers’ GenAI anxiety, technology self-efficacy, and TPACK: Their structural relations with behavioral intention to design GenAI-assisted teaching. Behavioral Sciences, 14(5), Article 373. https://doi.org/10.3390/bs14050373
Weng, Z., & Fu, Y. (2025). Generative AI in language education: Bridging divide and fostering inclusivity. International Journal of Technology in Education, 8(2), 395–420. https://doi.org/10.46328/ijte.1056
Wu, R., Wang, X., Nie, Y., Lv, P., & Luo, X. (2025). Exploring factors influencing pre-service teachers’ intention to use GenAI for instructional design: A grounded theory study. Behavioral Sciences, 15(9), Article 1169. https://doi.org/10.3390/bs15091169
Yang, H., & Markauskaite, L. (2025). Fostering language student teachers’ transformative agency for embracing GenAI: A formative intervention. Teaching and Teacher Education, 159, 104980. https://doi.org/10.1016/j.tate.2025.104980
Yang, S., & Appleget, C. (2024). An exploration of preservice teachers’ perceptions of generative AI: Applying the technology acceptance model. Journal of Digital Learning in Teacher Education, 40(3), 159–172. https://doi.org/10.1080/21532974.2024.2367573
You, Y., & Zhang, Y. (2025). Developing AI literacies and negotiating professional identities: A study of pre-service English teachers in a ChatGPT-facilitated pedagogy. Journal of China Computer-Assisted Language Learning. https://doi.org/10.1515/jccall-2025-0007
Zaimoğlu, S., & Dağtaş, A. (2025). Teacher cognition and practices in using generative AI tools to support student engagement in EFL higher-education contexts. Behavioral Sciences, 15(9), Article 1202. https://doi.org/10.3390/bs15091202
Zheng, W., Ma, Z., Sun, J., Wu, Q., & Hu, Y. (2025). Exploring factors influencing continuance intention of pre-service teachers in using generative artificial intelligence. International Journal of Human–Computer Interaction, 41(16), 10325–10338. https://doi.org/10.1080/10447318.2024.2433300
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Zainal Arifin, Atin Kurniawati
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
License and Publishing Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities “tacitly or explicitly“ of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and publishing agreement.
Copyright
Authors who publish with Jo-ELT (Journal of English Language Teaching) Fakultas Pendidikan Bahasa dan Seni Program Studi Pendidikan Bahasa Inggris IKIP agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Licensing for Data Publication
Jo-ELT (Journal of English Language Teaching) Fakultas Pendidikan Bahasa dan Seni Program Studi Pendidikan Bahasa Inggris IKIP use a variety of waivers and licenses, that are specifically designed for and appropriate for the treatment of data:
- Open Data Commons Attribution License, http://www.opendatacommons.org/licenses/by/1.0/
Other data publishing licenses may be allowed as exceptions (subject to approval by the editor on a case-by-case basis) and should be justified with a written statement from the author, which will be published with the article.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
1.jpg)
