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August 25, 2025
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October 13, 2025
Published
December 31, 2025
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Abstract

The integration of Artificial Intelligence (AI) into language education is reshaping the role of Language for Specific Purposes (LSP) educators, prompting a re-evaluation of their responsibilities and professional identity. Through in-depth interviews with university-level LSP teachers, this qualitative research explores how AI tools are transforming instructional practices and influencing teacher well-being. The findings reveal a dual impact: while AI facilitates material development, reduces workload, and enhances pedagogical flexibility, it also introduces challenges such as technostress, reduced creativity, and uncertainty about the future of the profession. The study highlights the importance of targeted professional development and institutional support to ensure that AI serves as a complement to, rather than a replacement for,human educators. These insights contribute to a deeper understanding of the digital transformation of language teaching and its implications for LSP professionals.

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artificial intelligence language for specific purposes teacher well-being technostress

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Copyright (c) 2025 Magdalena Zawiszewska

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How to Cite
Zawiszewska, M. (2025). Navigating the Artificial Intelligence Era: A Qualitative Study on the Impact of Artificial Intelligence on Language for Specific Purposes Teachers’ Well-Being. INSTED: Interdisciplinary Studies in Education & Society, 27(2(98), 43–65. https://doi.org/10.34862/tce.2025.2.8
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References

  1. Aad, S., & Hardey, M. (2025). Generative AI: hopes, controversies and the future of faculty roles in education. Quality Assurance in Education, 33(2), 267–282. https://doi.org/10.1108/qae-02-2024-0043
  2. Asad, M. M., Erum, D., Churi, P., & Moreno Guerrero, A. J. (2023). Effect of technostress on Psychological well-being of post-graduate students: A perspective and correlational study of Higher Education Management. International Journal of Information Management Data Insights, 3(1), 100149. https://doi.org/10.1016/j.jjimei.2022.100149
  3. Azamatova, A., Bekeyeva, N., Zhaxylikova, K., Sarbassova, A., & Ilyassova, N. (2023). The effect of using artificial intelligence and digital learning tools based on project-based learning approach in foreign language teaching on students’ success and motivation. International Journal of Education in Mathematics, Science and Technology, 11(6), 1458–1475. https://doi.org/10.46328/ijemst.3712
  4. Bai, A., Hessari, H., Daneshmandi, F., & Nategh, T. (2024). Human resources strategies: From job satisfaction to innovation in the age of technostress. Journal of Business Management and Economic Development, 2(03), 1031–1045. https://doi.org/10.59653/jbmed.v2i03.652
  5. Bartra-Rivero, K. R., Vásquez-Pajuelo, L., Avila-Sánchez, G. A., Andrade-Díaz, E. M., Méndez-Ilizarbe, G. S., Rodriguez-Barboza, J. R., & Alarcón-Villalobos, Y. J. (2024). How digital competence reduces technostress. Data and Metadata, 3, 303. https://doi.org/10.56294/dm2024303
  6. Belyaeva, A. (2015). English For specific purposes: Characteristic features and curriculum planning steps. Sustainable Multilingualism / Darnioji Daugiakalbystė, 7, 73–91. https://doi.org/10.7220/2335-2027.7.4
  7. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa Bui, H. N., & Duong, C. D. (2024). ChatGPT adoption in entrepreneurship and digital entrepreneurial intention: A moderated mediation model of technostress and digital entrepreneurial self-efficacy. Equilibrium. Quarterly Journal of Economics and Economic Policy, 19(2), 391–428. https://doi.org/10.24136/eq.3074
  8. Bukhari, F., Qutub, M. M. T., Fadel, S. A., & Aljuhani, H. S. A. (2023). The future of English as a foreign language teaching and learning in view of the Fourth Industrial Revolution in the MENA Region. Arab World English Journal, 9, 67–86. https://doi.org/10.24093/awej/call9.5
  9. Buşe, O., & Căbulea, M. (2023). Artificial Intelligence – An ally or a foe of foreign language teaching? Land Forces Academy Review, 28(4), 277–282. https://doi.org/10.2478/raft-2023-0032
  10. Casillano, N. F. B. (2024). Education in the ChatGPT era: A sentiment analysis of public discourse on the role of language models in education. Journal Evaluation in Education, 5(4), 144-154. https://doi.org/10.37251/jee.v5i4.1151
  11. Chang, P.-C., Zhang, W., Cai, Q., & Guo, H. (2024). Does AI-driven technostress promote or hinder employees’ Artificial Intelligence adoption intention? A moderated mediation model of affective reactions and technical self-efficacy. Psychology Research and Behavior Management, 17, 413–427. https://doi.org/10.2147/PRBM.S441444
  12. Chłoń-Domińczak, A., Sławiński, S., Kraśniewski, A., & Chmielecka, E. (2018). Polska Rama Kwalifikacji. Instytut Badań Edukacyjnych. https://prk.men.gov.pl/polska-rama-kwalifikacji-prk/
  13. Churampi-Cangalaya, R. L., Inga-Ávila, M. F., Ulloa-Ninahuamán, J., Inga-Ávila, J. L., Quispe, M. A., Inga-Aliaga, M. Á., Huamán-Pérez, F., & Caballero, E. M. (2024). Technology anxiety (technostress) and academic burnout from online classes in university students. International Journal of Data and Network Science, 8(1), 515-522. https://doi.org/10.5267/j.ijdns.2023.9.005
  14. Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches (3rd ed.). SAGE Publications.
  15. Ertiö, T., Eriksson, T., Rowan, W., & McCarthy, S. (2024). The role of digital leaders’ emotional intelligence in mitigating employee technostress. Business Horizons, 67(4), 399–409. https://doi.org/10.1016/j.bushor.2024.03.004
  16. Fitria, T. N. (2020). Teaching English for Specific Purposes (ESP) to the students in English Language Teaching (ELT). Journal of Teaching English Adi Buana, 5(1), 55–66. https://doi.org/10.36456/jet.v5.n01.2020.2276
  17. Fleischhauer, K., & Friedrich, K. (2024). Factors determining the efficacy of AI-generated word problems for content-specific math language courses in higher education. Scripta Manent, 19(1), 4–24. https://doi.org/10.4312/SM.19.1.4-24
  18. Hilal, A.-R. S., Shakirova, Z. N., Mullasadikova, N. M., Madayeva, M. A., & Askarov, A. M. (2025). Neutrosophic analysis for the future of Artificial Intelligence in language education. International Journal of Neutrosophic Science, 26(2), 251-257. https://doi.org/10.54216/ijns.260219
  19. Kariyeva, D. (2024). Teaching foreign Language for Specific Purposes: Teacher development. Journal of Higher Education and Academic Advancement, 1(11), 42-50. https://doi.org/10.61796/ejheaa.v1i11.939
  20. Khan, A., & Mishra, V. (2024). Empowering English language learners: Harnessing AI for enhanced ESL education. Journal of Advances and Scholarly Researches in Allied Education, 21(3), 208–218. https://doi.org/10.29070/2vrwf279
  21. Liando, N., Tatipang, D., Rorimpandey, R., Kumayas, T., Saudah, K., & Iskandar, I. (2025). AI-powered language learning: A blessing or a curse for English language education? Studies in English Language and Education, 12(1), 301-311. https://doi.org/10.24815/siele.v12i1.34842
  22. Liu, M. (2023). Exploring the application of Artificial Intelligence in foreign language teaching: Challenges and future development. SHS Web of Conferences, 168, 03025. https://doi.org/10.1051/shsconf/202316803025
  23. Madjid, A. (2022). Towards a new era of language learning: Predicting trends and challenges of AI integration in the future. Transformational Language Literature And Technology Overview In Learning (Transtool), 2(1), 1–9. https://doi.org/10.55047/transtool.v2i1.1369
  24. Maity, S., & Deroy, A. (2024). The future of learning in the age of generative AI: Automated question generation and assessment with large language models. arXiv.Org. https://doi.org/10.48550/arxiv.2410.09576
  25. Matukhin, D. L., & Gorkaltseva, E. N. (2015). Teaching Foreign Language for Specific Purposes in terms of professional competency development. Mediterranean Journal of Social Sciences, 6(1), 525. https://doi.org/10.5901/mjss.2015.v6n1p525
  26. Mehmood, K., Suhail, A., Kautish, P., Hakeem, M. M., & Rashid, M. (2024). Turning lemons into lemonade: Social support as a moderator of the relationship between technostress and quality of life among university students. Psychology Research and Behavior Management, 17, 989–1006. https://doi.org/10.2147/PRBM.S448989
  27. Mwakapina, J. W. (2024). The role of artificial intelligence in the future of language teaching and learning practices in higher education. Pan-African Journal of Education and Social Sciences, 5(2), 106–122. https://doi.org/10.56893/pajes2024v05i02.08
  28. Nascimento, L., Correia, M. F., & O’Sullivan, G. (2024). The upside of teachers’ technostress: Adaptation and validation of a Techno-eustress Scale. International Journal of Instruction, 17(4), 1–18. https://doi.org/10.29333/iji.2024.1741a
  29. Okolo, C. J., Chinyere, G. E., Chioma, I. B., & Ugwu, N. J. (2024). Personalized language education in the age of AI: Opportunities and challenges. Newport International Journal of Research in Education, 4(1), 39-44. https://doi.org/10.59298/nijre/2024/41139448
  30. Pagán-Garbín, I., Méndez, I., & Martínez-Ramón, J. P. (2024). Exploration of stress, burnout and technostress levels in teachers. Prediction of their resilience levels using an artificial neuronal network (ANN). Teaching and Teacher Education, 148, 104717. https://doi.org/10.1016/j.tate.2024.104717
  31. Pansini, M., Buonomo, I., De Vincenzi, C., Ferrara, B., & Benevene, P. (2023). Positioning technostress in the JD-R model perspective: A systematic literature review. Healthcare, 11(3), 446. https://doi.org/10.3390/healthcare11030446
  32. Rane, N. (2024). Enhancing the quality of teaching and learning through ChatGPT and similar large language models: Challenges, future prospects, and ethical considerations in education. TESOL and Technology Studies, 5(1), 1-6. https://doi.org/10.48185/tts.v5i1.1000
  33. Saleem, F., Chikhaoui, E., & Malik, M. I. (2024). Technostress in students and quality of online learning: Role of instructor and university support. Frontiers in Education, 9, 1309642. https://doi.org/10.3389/feduc.2024.1309642
  34. Sanjeeva Kumar, P. (2024). TECHNOSTRESS: A comprehensive literature review on dimensions, impacts, and management strategies. Computers in Human Behavior Reports, 16, 100475. https://doi.org/10.1016/j.chbr.2024.100475
  35. Shalash, M. J. (2024). English for specific purposes: A specialization in tailoring language instruction. Journal of Asian Multicultural Research for Educational Study, 5(1), 10–18. https://doi.org/10.47616/jamres.v5i1.486
  36. Shalatska, H. M., Zotova-Sadylo, O. Y. & Muzyka, I. O. (2020). Moodle course in teaching English Language for Specific Purposes for masters in mechanical engineering. CTE Workshop Proceedings, 7, 416-434. https://doi.org/10.55056/cte.378
  37. Son, J.-B., Ružić, N. K., & Philpott, A. (2023). Artificial intelligence technologies and applications for language learning and teaching. Journal of China Computer-Assisted Language Learning, 5(1), 94-112. https://doi.org/10.1515/jccall-2023-0015
  38. Tarafdar, M., Cooper, C. L., & Stich, J.-F. (2019). The technostress trifecta ‐ techno eustress, techno distress and design: Theoretical directions and an agenda for research. Information Systems Journal, 29(1), 6–42. https://doi.org/10.1111/isj.12169
  39. Toscano F., Galanti T., Giffi V., Di Fiore T., Cortini M., & Fantinelli S. (2024). The mediating role of technostress in the relationship between social outcome expectations and teacher satisfaction: evidence from the COVID-19 pandemic in music education. Research in Learning Technology, 32. https://doi.org/10.25304/rlt.v32.3086
  40. Trace, J., Hudson, T., & Brown, J. D. (2015). An overview of language for specific purposes. In J. Trace, T. Hudson, & J. D. Brown, Developing courses in Languages for Specific Purposes (pp. 1–23) University of Hawai‘i. http://hdl.handle.net/10125/14573
  41. Urbaite, G. (2025). Adaptive learning with AI: How bots personalize foreign language education. Luminis Applied Science and Engineering, 2(1), 13-18. https://doi.org/10.69760/lumin.20250001002
  42. Whyte, S. (2019). Revisiting communicative competence in the teaching and assessment of language for specific purposes. Language Education & Assessment, 2(1), 1–19. https://doi.org/10.29140/lea.v2n1.33
  43. Yunina, O. (2023). Artificial intelligence tools in foreign language teaching in higher education institutions. The Modern Higher Education Review, (8), 77–90. https://doi.org/10.28925/2617-5266.2023.85
  44. Zimotti, G., Frances, C., & Whitaker, L. (2024). The future of language education: Teachers’ perceptions about the surge of AI writing tools. Technology in Language Teaching & Learning. 6(2), 1136. https://doi.org/10.29140/tltl.v6n2.1136