Abstract

Conclusion. The study attracts attention to the value of learners’ perceptions in the transition to a digitally geared educational environment. It underscores the need for a prudent approach to integrating AI in teaching and learning experiences. This is why decision-makers need to engage in a careful appraisal of the role AI currently plays and can play in the future.

Keywords: Artificial intelligence, language learning, perceptions, digital transition, technological readiness

Abbad, M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and information technologies, 26(6), 7205-7224. https://doi.org/10.1007/s10639-021-10573-5

Al Shuraiaan, A., Al Bloushi, B., & Al Bloushi, L. (2024). The double-edged sword: Analyzing the influence of technology on English language learning in Kuwait higher education institutions (HEIs). International Journal of Middle Eastern Research, 3(1), 15-23. https://doi.org/10.32996/ijmer.2024.3.1.3

Alenezi, W. (2024). Potentiality and apprehensions of artificial intelligence in education: Perspectives of education staff. International Journal of Education in Mathematics, Science and Technology, 12(4), 942-956. https://www.ijemst.net/index.php/ijemst/index

Alshammari, A., & Al-Enezi, S. (2024). Role of artificial intelligence in enhancing learning outcomes of pre-service social studies teachers. Journal of Social Studies Education Research, 15(4), 163-196. https://doi.org/10.46328/jsser.v15i4.5787

Amoozadeh, M., Daniels, D., Nam, D., Kumar, A., Chen, S., Hilton, M., & Alipour, M. (2024). Trust in Generative AI among students: An exploratory study. The 55th ACM Technical Symposium on Computer Science Education, (pp. 67-73). https://doi.org/10.48550/arXiv.2310.04631

Annamalai, N., Bervell, B., Mireku, O., & Andoh, K. (2025). Artificial intelligence in higher education: Modelling students’ motivation for continuous use of ChatGPT based on a modified self-determination theory. Computers and Education: Artificial Intelligence, 8, 100346.‏ https://doi.org/10.1016/j.caeai.2024.100346

Baker, T., & Smith, L. (2019). Educ-ai-tion rebooted? exploring the future of artificial intelligence in schools and colleges. Technical report, Nesta Foundation. https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf

Bax, S., & Chan, S. (2019). Using eye-tracking research to investigate language test validity and design. System, 83, 64-78. https://doi.org/10.1016/j.system.2019.01.007

Byrne, B. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming. New York: Routledge.

Chan, C., & Tsi, L. (2023). The AI revolution in education: Will AI replace or assist teachers in higher education? Computers and Society. https://arxiv.org/abs/2305.01185

Choung, H., David, P., & Ross, A. (2023). Trust and ethics in AI. Ai & Society, 38(2), 733-745. https://doi.org/10.1007/s00146-022-01473-4

Dolenc, K., & Brumen, M. (2024). Exploring social and computer science students’ perceptions of AI integration in (foreign) language instruction. Computers and Education: Artificial Intelligence, 7. https://doi.org/10.1016/j.caeai.2024.100285

Fang, S., Wu, X., & Lai, I. (2021). Factors affecting Macau undergraduate students' acceptance of hospitality English app: applicability of UTAUT model. International Journal of Innovation and Learning, 29(3), 250-266. https://doi.org/10.1504/IJIL.2021.114510

Fornell, C., & Larcker, D. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.

Francis, N. J., Jones, S., & Smith, D. P. (2025). Generative AI in higher education: Balancing innovation and integrity. British Journal of Biomedical Science, 81, 14048.‏ https://doi.org/10.3389/bjbs.2024.14048

Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, New Jersey: Pearson Educational International.

Hoi, V. (2020). Understanding higher education learners' acceptance and use of mobile devices for language learning: A Rasch-based path modeling approach. Computers & Education, 146. https://doi.org/10.1016/j.compedu.2019.103761

Hsu, L. (2023). EFL learners’ self-determination and acceptance of LMOOCs: The UTAUT model. Computer Assisted Language Learning, 36(7), 1177-1205. https://doi.org/10.1080/09588221.2021.1976210

Huang, F., Teo, T., & Zhao, X. (2023). Examining factors influencing Chinese ethnic minority English teachers’ technology adoption: an extension of the UTAUT model. Computer Assisted Language Learning, 1-23. https://doi.org/10.1080/09588221.2023.2239304

Hyland, K. (2013). Student perceptions of hidden messages in teacher written feedback. Studies in Educational Evaluation, 39(3), 180-187. https://doi.org/10.1016/j.stueduc.2013.06.003

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

Kanber, H., Jassim, K., Al-Taai, S., Ibrahim, M., & Farhan, N. (2025). The Role of Digital Educational Content in Eliminating Traditional Methods in the Educational Process within Iraqi Universities. Educational Process: International Journal, 14. https://doi.org/10.22521/edupij.2025.14.50

Kelly, S., Kaye, S., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 1-33. https://doi.org/10.1016/j.tele.2022.101925

Kirovska-Simjanoska, D. (2021). Designing an ESP course for the 21st century students: The clash of the digital immigrants and natives. Journal of Teaching English for Specific and Academic Purposes, 17-30. https://doi.org/10.22190/JTESAP2101017K

Liu, Y. Z. (2024). A systematic review of research on emotional artificial intelligence in English language education. System, 126, 103478. https://doi.org/10.1016/j.system.2024.103478

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. (2016). Intelligence Unleashed. An argument for AI in Education. https://discovery.ucl.ac.uk/id/eprint/1475756

McStay, A. (2020). Emotional AI and EdTech: serving the public good? Learning, Media and Technology, 45(3), 270-283. https://doi.org/10.1080/17439884.2020.1686016

Milad, M., & Fayez, F. (2024). Your Way AI Way: Let‟ s Meet Half-Way: Paradigm Shift in TEFL. World Journal of English Language, 14(5), 468-468. https://doi.org/10.5430/wjel.v14n5p468

Mushthoza, D., Syariatin, N., Tahalele, O., Telussa, S., Rasmi, R., & Mokodenseho, S. (2023). Analyzing the impact of artificial intelligence (AI) on the future of English language teaching and learning. Journal on Education, 6(1), 1549-1557. https://doi.org/10.31004/joe.v6i1.3115

Niemand, T., & Mai, R. (2018). Flexible cutoff values for fit indices in the evaluation of structural equation models. (https://doi.org/10.1007/s11747-018-0602-9, Ed.) Journal of the Academy of Marketing Science, 46(6), 1148–1172. https://doi.org/10.1007/s11747-018-0602-9

Norton, B., & Toohey, K. (2011). Identity, language learning, and social change. Language teaching, 44(4), 412-446. https://doi.org/10.1017/S0261444811000309

Pedró, F. (2020). Applications of Artificial Intelligence to higher education: possibilities, evidence, and challenges. IUL Research, 1(1), 61-76. https://doi.org/10.57568/iulres.v1i1.43

Qin, F., & Yan, J. (2020). Understanding user trust in artificial intelligence-based educational systems: Evidence from China. British Journal of Educational Technology, 51, 1693-1710. https://doi.org/10.1111/bjet.12994

Raffaghelli, J., Rodríguez, M., Guerrero-Roldán, A., & Bañere, D. (2022). Applying the UTAUT model to explain the students' acceptance of an early warning system in Higher Education. Computers & Education, 182. https://doi.org/10.1016/j.compedu.2022.104468

Reiss, M. (2021). The Use of Al in Education: Practicalities and Ethical Considerations. London Review of Education, 19(1), 1-14. https://doi.org/10.14324/LRE.19.1.05

Schroeder, N., & Gotch, C. (2015). Persistent Issues in Pedagogical Agent Research. Journal of Educational Computing Research, 53(2), 183–204. https://doi.org/10.1177/0735633115597625

Sousa, V., & Rojjanasrirat, W. (2010). Translation, adaptation and validation of instruments or scales for use in cross‐cultural health care research: a clear and user‐friendly guideline. (https://doi.org/10.1111/j.1365-2753.2010.01434.x, Ed.) Journal of Evaluation in Clinical Practice, 17(2), 268–274. https://doi.org/10.1111/j.1365-2753.2010.01434.x

Southgate, E. (2020). Artificial intelligence, ethics, equity and higher education:A ‘beginning-of-the-discussion’ paper. Technical Report. National Centre for Student Equity in Higher Education, 1-20.

Steponenaite, A., & Barakat, B. (2023). Plagiarism in AI empowered world. International Conference on Human-Computer Interaction (pp. 434-442). Switzerland: Springer Nature. https://doi.org/10.1007/978-3-031-35897-5_31

Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive learning environments, 1-14. https://doi.org/10.1080/10494820.2023.2209881

Taber, K. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. (https://doi.org/10.1007/s11165-016-9602-2, Ed.) Research in science education, 48, 1273–1296. https://doi.org/10.1007/s11165-016-9602-2

Taktak, M., Bellibaş, S., & Özgenel, M. (2024). Use of ChatGPT in Education: Future Strategic Road Map with SWOT Analysis. Educational Process: International Journal, 13(3), 7-21. https://doi.org/10.22521/edupij.2024.133.1

Trewin, S., Basson, S., Muller, M., Branham, S., & Treviranus, J. (2019). Considerations for AI fairness for people with disabilities. AI Matters, 5(3), 40-63. https://doi.org/10.1145/3362077.3362086

Tu, Y., & Hwang, G. (2023). University students’ conceptions of ChatGPT-supported learning: a drawing and epistemic network analysis. Interactive Learning Environments, 1-25. https://doi.org/10.1080/10494820.2023.2286370

Venkatesh, M. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Wang, B., Rau, P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour & information technology, 24(9), 1324-1337. https://doi.org/10.1080/0144929X.2022.2072768

Wang, X., Pang, H., Wallace, P., Wang, Q., & Chen, W. (2024). Learners’ perceived AI presences in AI-supported language learning: A study of AI as a humanized agent from community of inquiry. Computer Assisted Language Learning, 37(4), 814-840. https://doi.org/10.1080/09588221.2022.2056203

White, J. (2022). Artificial Intelligence and People with Disabilities: a Reflection on Human–AI Partnerships. In F. Chen, & J. Zhou, Humanity Driven AI: Productivity, Well-being, Sustainability and Partnership (pp. 279-310). Springer. https://doi.org/10.1007/978-3-030-72188-6_14

Zaim, M., Arsyad, S., Waluyo, B., Ardi, H., Al Hafizh, M., Syafitri, W., . . . Hardiah, M. (2024). AI-powered EFL pedagogy: integrating generative AI into university teaching preparation through UTAUT and activity theory. Computers and Education: Artificial Intelligence, 7. https://doi.org/10.1016/j.caeai.2024.100335

Zhang, W. (2022). The Role of technology-based education and teacher professional development in English as a Foreign Language Classes. Frontiers in Psychology, 13, 1-7. https://doi.org/10.3389/fpsyg.2022.910315

Zheldibayeva, R. (2025). GenAI as a Learning Buddy for Non-English Majors: Effects on Listening and Writing Performance. Educational Process: International Journal, 14, e2025051. https://doi.org/10.22521/edupij.2025.14.51

Zhu, M. (2022). Factors influencing analysis for level of engineering english education based on artificial intelligence technology. Mathematical Problems in Engineering, 2022, 1-11. https://doi.org/10.1155/2022/4447209