Views: 593
Arab World English Journal (AWEJ) Special Issue on CALL Vol. 17, No.12. July 2026 Pp.400-417
Foreign Systems of Pedagogical and Adult Education Department
Ivan Ziaziun Institute of Pedagogical and Adult Education of the NAES of Ukraine, Kyiv, Ukraine
Department of Foreign Languages, Faculty of Economics
Taras Shevchenko National University of Kyiv, Ukraine
Department of Foreign Languages for Natural Sciences Faculties,
Educational and Scientific Institute of Philology,
Taras Shevchenko National University of Kyiv, Ukraine
Department of Foreign Languages, Faculty of Economics,
Taras Shevchenko National University of Kyiv, Ukraine
Department of Foreign Languages, Faculty of Economics
Taras Shevchenko National University of Kyiv, Ukraine
Task-based language assessment assumes that the learner’s performance reflects the learner’s own developing communicative competence. Generative AI challenges this assumption by allowing students to delegate task completion to an external system, thereby threatening task authenticity, performance authenticity, and the interpretability of form. The study aims to examine how 40 university English for Specific Purposes teachers at three Ukrainian universities experience the impact of generative artificial intelligence on the task-based language teaching and assessment validity chain. This issue is significant since, in the absence of institutional AI policy, teachers are the primary regulators of AI use in assessed contexts, and their decisions are the primary mechanism through which validity is maintained or compromised, yet remain empirically under-examined. The study employed a qualitative approach, including analytical content analysis of open-ended survey responses and structured classroom observations of 12 teachers (a purposive subsample). The results were five-fold: teachers found artificial intelligence useful for task material generation and formative feedback; teachers had consistent validity concerns for summative assessment; formative-summative asymmetry emerged in different disciplinary clusters; teachers independently indicated that students’ output converged towards a single artificial intelligence-modified output; and students would not be able to critically evaluate the artificial intelligence-generated text. Our findings are interpreted using the Task-Based Language Teaching-Assessment-Artificial Intelligence Nexus, a preliminary heuristic model that identifies three interconnected nodes of validity threat. The study argues that this suggests the need for formal AI policy, and for teaching AI-critical genre literacy in English for Specific Purposes education.
Avsheniuk, N., Seminikhyna, N., Lutsenko, O., Svyrydiuk, T., & Ruban, L. (2026). Homogenization and Epistemic Dependency: How Generative AI Threatens Task-Based Language Assessment in ESP Contexts. Arab World English Journal (AWEJ) Special Issue on CALL Vol. 17. (12)400-417.
Avsheniuk, N., Seminikhyna, N., Ruban, L., & Sviatiuk, Y. (2025). Exploring overreliance on AI tools in English for Specific Purposes courses: Challenges and implications for learning and academic integrity. Arab World English Journal (AWEJ) Special Issue on Artificial Intelligence, 3–20. https://dx.doi.org/10.24093/awej/AI.1
Bachman, L. F., & Palmer, A. (2010). Language assessment in practice. Oxford University Press.
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). ACM. https://doi.org/10.1145/3442188.3445922
Bhandari, L. P., Dahal, N., Awasthi, J. R., & Dhungana, S. (2025). Technology-mediated task-based language teaching: A systematic review. Cogent Education, 12(1), Article 2560051. https://doi.org/10.1080/2331186X.2025.2560051
Bhatia, V. K. (1993). Analysing genre: Language use in professional settings. Longman.
Casal, J. E., & Kessler, M. (2023). Can linguists distinguish between ChatGPT-generated AI writing and human writing?: A study of research ethics and academic publishing. Research Methods in Applied Linguistics, 2(3), Article 100068. https://doi.org/10.1016/j.rmal.2023.100068
Case, R., Liu, L., & Mintz, J. (2025). Integrating AI technology into language teacher education: Challenges, potentials, and assumptions. Computers in the Schools, 42(2), 93–99. https://doi.org/10.1080/07380569.2025.2458950
Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28–47. https://www.jstor.org/stable/48647028
Corder, S. P. (1967). The significance of learners’ errors. International Review of Applied Linguistics, 5(4), 161–170. https://doi.org/10.1515/iral.1967.5.1-4.161
Dudley-Evans, T., & St. John, M. J. (1998). Developments in English for specific purposes: A multi-disciplinary approach. Cambridge University Press.
Ellis, R. (2003). Task-based language learning and teaching. Oxford University Press.
Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration? Educational Technology Research and Development, 53(4), 25–39. https://doi.org/10.1007/BF02504683
Hyland, K. (2006). English for academic purposes: An advanced resource book. Routledge.
Hyland, K. (2019). Genre and second language academic writing. Language Teaching, 52(2), 149–163. https://doi.org/10.1017/S0261444818000429
Li, Y., Shadiev, R., & Chiu, T. K. F. (2025). Applying generative artificial intelligence to task-based language teaching and learning: A systematic review and meta-analysis. TechTrends, 70(1). https://doi.org/10.1007/s11528-025-01140-7
Liu, W. (2025). Language teacher AI literacy: Insights from collaborations with ChatGPT. Journal of China Computer-Assisted Language Learning. Advance online publication. https://doi.org/10.1515/jccall-2024-0030
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). ACM. https://doi.org/10.1145/3313831.3376727
Long, M. H. (1996). The role of the linguistic environment in second language acquisition. In W. C. Ritchie & T. K. Bhatia (Eds.), Handbook of second language acquisition (pp. 413–468). Academic Press.
Long, M. H. (2015). Second language acquisition and task-based language teaching. Wiley-Blackwell.
Meniado, J. C. (2023). The impact of ChatGPT on English language teaching, learning, and assessment: A rapid review of literature. Arab World English Journal, 14(4), 3–18. https://dx.doi.org/10.24093/awej/vol14no4.1
Messick, S. (1989). In R. L. Linn (Ed.), Educational measurement (3rd ed., pp. 13–103). American Council on Education.
Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). SAGE.
Ministry of Education and Science of Ukraine [MES Ukraine]. (2024). Recommendations for the use of artificial intelligence in the educational process of general secondary education institutions. https://mon.gov.ua/ua/news/rekomendaciyi-shodo-vikoristannya-shtuchnogo-intelektu-v-osviti
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualising AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041
Norris, J. M. (2009). Task-based teaching and testing. In M. H. Long & C. J. Doughty (Eds.), The handbook of language teaching (pp. 578–594). Wiley-Blackwell.
Norris, J. M. (2016). Current uses for task-based language assessment. Annual Review of Applied Linguistics, 36, 230–244. https://doi.org/10.1017/S0267190516000027
Samuda, V., & Bygate, M. (2008). Tasks in second language learning. Palgrave Macmillan.
Schreier, M. (2012). Qualitative content analysis in practice. SAGE.
Swales, J. M. (1990). Genre analysis: English in academic and research settings. Cambridge University Press.
Tondeur, J., van Braak, J., Sang, G., Voogt, J., Fisser, P., & Ottenbreit-Leftwich, A. (2012). Preparing pre-service teachers to integrate technology in education: a synthesis of qualitative evidence. COMPUTERS & EDUCATION, 59(1), 134–144. https://doi.org/10.1016/j.compedu.2011.10.009
UNESCO. (2023). Guidance for generative AI in education and research. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000386693
Valerio, M. T. B. (2025). Enhancing Argumentative Essay Skills: Evaluating the Efficacy of AI-Powered Tools Versus the Traditional Approach Among Learners. Arab World English Journal (AWEJ) Special Issue on Artificial Intelligence: 270- 289. https://dx.doi.org/10.24093/awej/AI.15
D.Sc., Prof. Nataliia Avsheniuk is a distinguished scholar in education, Professor and Head of the Foreign Systems of Pedagogical and Adult Education Department at the Ivan Ziaziun Institute of Pedagogical and Adult Education of the National Academy of Educational Sciences (NAES) of Ukraine. Her research interests focus on comparative education, adult education, quality assurance in higher education, lifelong learning policies, and pedagogical innovations in global educational contexts.
ORCID ID: https://orcid.org/0000-0001-6246-4132
Nataliia Seminikhyna, PhD in Education, Associate Professor. Her research interests include English for Specific Purposes (ESP) teaching methods, language pedagogy, comparative pedagogy, teacher education, teacher professional development, and the integration of artificial intelligence into language teaching and assessment. ORCID ID: https://orcid.org/0000-0001-6246-4132
Olena Lutsenko, PhD in Education, Associate Professor. Her research interests include English for Specific Purposes (ESP) teaching methods, language pedagogy, and teacher education. She has contributed to various publications in the field of education and ESP teaching, focusing on innovative approaches to foreign language instruction and professional development in higher education. ORCID ID:https://orcid.org/0000-0002-6864-908X
Tetiana Svyrydiuk, PhD in Linguistics, Associate Professor. Her research interests involve cognitive linguistics, applied linguistics, cross-cultural communication, and ESP teaching. She has extensive experience in foreign language instruction, with a focus on developing communicative competence and cross-cultural awareness. ORCID ID: https://orcid.org/0000-0001-9482-8228
Larysa Ruban, PhD in Education, Associate Professor. Her research interests involve English for Specific Purposes (ESP), teacher education, and teacher professional development. She has extensive experience in ESP teaching and has contributed to various publications in the field of education and foreign language instruction. ORCID ID: https://orcid.org/0000-0001-5539-931X