Building Prompt Literacy: Empowering EFL Teachers with A Model-Based Approach to Prompt Writing
DOI:
https://doi.org/10.52340/tuw.2025.38.01.28Keywords:
AI in language teaching, teacher training, prompt writing, -service teachersAbstract
This paper shares the lived experiences of English teachers participating in a part of a teacher training initiative financed by the Regional English Language Office (RELO) of the U.S. Department of State in Turkiye. Within the scope of the program, forty volunteer EFL teachers from various educational levels participated in an extensive training program focused on the integration of artificial intelligence into language instruction. Among other parts of the program, this study is concerned with “Model-Supported Prompt Writing Training," part in which participants were introduced with PARTS and TATOO prompt writing models which help teachers write prompts that are tailored to their language teaching settings. During the course, teachers did hands-on activities where they made AI prompts that fit with their teaching goals to make educational materials. Immediately after the training, the participants received an online survey with open ended questions regarding their prompt writing experience to reveal participants’ experiences and perceptions on prompt writing with a model. The data obtained from the survey was subjected to thematic content analysis. Emergent themes were found to be aware of prompt specificity, confidence in integrating AI into education, and the transformative impact of structured prompt models. The results indicate a significant shift in teachers' views on artificial intelligence prompts, underscoring the necessity for targeted training in prompt literacy. Therefore, this study’s implications have potential to contribute literature on the integration of AI tool into teacher education.
Downloads
References
Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K–12 settings. AI & Ethics, 2(3), 431–440. https://doi.org/10.1007/s43681-021-00096-7.
Baytas, C., & Ruediger, D. (2025, May 1). Making AI Generative for Higher Education: Adoption and Challenges Among Instructors and Researchers. https://doi.org/10.18665/sr.322677
Carolus, A., Binder, J., & Muench, F. (2024). Measuring AI literacy skills (MAILS): Development and validation of a scale. Computers & Education, 206, 104903. https://doi.org/10.1016/j.compedu.2023.104903
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), 1–10. https://doi.org/10.1016/j.chbah.2023.100014
Carolus, J., Schmidt, A., & Novak, T. (2023). AI prompt literacy in secondary schools: Needs, practices, and challenges. Computers in the Schools, 40(3), 200–220. https://doi.org/10.1080/07380569.2023.1987654
Chiu, T. K. F. (2021). A holistic approach to the design of artificial intelligence (AI) education for K–12 schools. TechTrends, 65(5), 796–807. https://doi.org/10.1007/s11528-021-00637-1
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE.
Daher, R. (2025). Integrating AI literacy into teacher education: A critical perspective. AI and Education Journal. https://doi.org/10.1007/s44163 025 00475 7
Davila-Moran, R. C., Sanchez Soto, J. M., Lopez Gomez, H. E., Silva Infantes, M., Arias Lizares, A., Huanca Rojas, L. M., & Cama Flores, S. J. (2025). Brief prompt-engineering clinic substantially improves AI literacy and reduces technology anxiety in first-year teacher-education students: A pre–post pilot study. Education Sciences, 15(8), 1010. https://doi.org/10.3390/educsci15081010
Dickey, E., & Bejarano, A. (2023). GAIDE: A framework for using generative AI to assist in course content development . arXiv. https://doi.org/10.48550/arXiv.2308.12276
Frøsig, T. B., & Romero, M. (2024). Teacher agency in the age of generative AI: Towards a framework of hybrid intelligence for learning design [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2407.06655
Giannakos, M., Azevedo, R., Brusilovsky, P., Cukurova, M., Dimitriadis, Y., Hernández-Leo, D., Järvelä, S., Mavrikis, M., & Rienties, B. (2024). The promise and challenges of generative AI in education. Behaviour & Information Technology. Advance online publication. https://doi.org/10.1080/0144929X.2024.2394886
Karaman, M., & Göksu, I. (2024). Are lesson plans created by ChatGPT more effective? An experimental study. International Journal of Technology in Education, 7(1), 107–127. https://doi.org/10.46328/ijte.607
Lee, J., & Palmer, C. (2025). Prompting the future: Supporting students’ engagement with generative AI tools. Journal of Digital Learning, 18(1), 33–49.
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 (pp. 1–16). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727
Mikeladze, T., Meijer, P. C., & Verhoeff, R. P. (2024). A comprehensive exploration of artificial intelligence competence frameworks for educators: A critical review. European Journal of Education, 59(3), e12663. https://doi.org/10.1111/ejed.12663
Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2023). 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., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023a). Teachers’ AI digital competencies and twenty first century skills in the post pandemic world. Educational Technology Research and Development, 71, 137–161. https://doi.org/10.1007/s11423 023 10203 6
Ng, D. T. K., Luo, W., Chan, H. M. Y., & Chu, S. K. W. (2022). Using digital story writing as a pedagogy to develop AI literacy among primary students. Computers & Education: Artificial Intelligence, 3, Article 100054. https://doi.org/10.1016/j.caeai.2022.100054
Ng, D. T. K., Smith, J. A., & Lee, C. (2023b). Conceptualizing teacher AI competencies: Towards a comprehensive framework for AI literacy in education. Educational Technology Research and Development.
Ng, W., Ali, N., & Goh, T. (2023c). Rethinking prompt writing for AI education: Ethical and practical considerations. International Journal of Artificial Intelligence in Education, 33(4), 413–430. https://doi.org/10.1007/s40593-023-00322-0
Park, J., & Choo, S. (2024). Generative AI prompt engineering for educators: Practical strategies including PARTS and IDEA frameworks. Journal of Special Education Technology, 40(3), 411-417. https://doi.org/10.1177/01626434241298954
Rahman, A., & Lee, Y. (2024). Teachers as prompt designers: Training educators to use LLMs in language education. Journal of Educational Technology & Society, 27(2), 88–104.
Rana, V., Verhoeven, B., & Sharma, M. (2025). Generative AI in design thinking pedagogy: Enhancing creativity, critical thinking, and ethical reasoning in higher education. Journal of University Teaching and Learning Practice, 22(4). https://doi.org/10.53761/tjse2f36
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International Journal of Educational Technology in Higher Education, 18(1), 1–23. https://doi.org/10.1186/s41239-021-00292-9
Sijing, L., & Lan, W. (2018). Artificial intelligence education ethical problems and solutions. In 2018 13th International Conference on Computer Science & Education (ICCSE) (pp. 1–5). IEEE.
Stake, R. E. (1995). The art of case study research. SAGE.
UNESCO. (2024, August 8). AI competency framework for teachers. United Nations Educational, Scientific and Cultural Organization (UNESCO). https://unesdoc.unesco.org/ark:/48223/pf0000391104
Velasquez-Henao, J., Smith, R., & Liu, T. (2023). Prompt engineering practices for educators: A systematic review. Computers & Education: Artificial Intelligence, 4, 100095. https://doi.org/10.1016/j.caeai.2023.100095
Zhai, X. (2025). Transforming teachers’ roles and agencies in the era of generative AI: Perceptions, acceptance, knowledge, and practices, arXiv preprint arXiv:2410.03018.
Zhou, Y., Tran, M., & Liang, X. (2025). Structuring prompts to reduce bias in LLM outputs: A pedagogical perspective. AI and Ethics, 6(1), 90
Published
How to Cite
Issue
Section
