AI Acceptance and Future Readiness among Postgraduate English Students: Evidence from Online Learning
Keywords:
AI in education, Technology acceptance, Learner readiness., online learningAbstract
The purpose of this study is to investigate the adoption of artificial intelligence (AI) tools among postgraduate students when they learn online in English and how their experiences impact the use of AI in the future and their readiness to learn independently. Although AI is becoming more and more integrated in the education sector, empirical findings that specifically target postgraduate English learners are sparse. To fill this gap, a mixed-methods approach combining a quantitative survey ( 91 postgraduate students ) through the use of a structured questionnaire and qualitative semi-structured interviews with postgraduate students was carried out to examine relationships existing between perceived benefits, perceived ease of use, AI acceptance, and future readiness. The results indicated high perceived value and acceptance, moderate readiness, and low perceived difficulty. The benefits and ease of use were found to play a significant role in predicting AI acceptance and, subsequently, the effect of AI acceptance on the likelihood of a learner using AI in the future and their willingness to use AI in a more autonomous manner. These findings indicate the effectiveness of positive learning experiences in facilitating the transition of postgraduate learners to long-term and self-sufficient interaction with AI-mediated learning.
References
Baig, M. I., & Yadegaridehkordi, E. (2024). ChatGPT in the higher education: A systematic literature review and research challenges. International Journal of Educational Research, 127, 102411. https://doi.org/10.1016/j.ijer.2024.102411
Barrett, A., & Pack, A. (2023). Not quite eye to A.I.: Student and teacher perspectives on the use of generative AI in the writing process. International Journal of Educational Technology in Higher Education, 20, 59. https://doi.org/10.1186/s41239-023-00427-0
Batista, J. B., Mesquita, A., & Carnaz, G. (2024). Generative AI and higher education: Trends, challenges, and future directions from a systematic literature review. Information, 15(11), 676. https://doi.org/10.3390/info15110676
Benson, P. (2011). Teaching and researching autonomy in language learning (2nd ed.). Routledge. https://doi.org/10.4324/9781315833767
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, 43. https://doi.org/10.1186/s41239-023-00411-8
Chapelle, C. A. (2001). Computer applications in second language acquisition: Foundations for teaching, testing and research. Cambridge University Press. https://doi.org/10.1017/CBO9781139524681
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
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Mekheimer, M. (2025). Generative AI-assisted feedback and EFL writing: A study on proficiency, revision frequency and writing quality. Discover Education, 4(1), Article 170. https://doi.org/10.1007/s44217-025-00602-7
Pham, H. Y., & Hoang, T. T. H. (2023). An investigation of the relationship between students’ self-discipline and their language performance in an online learning context at a Vietnamese university. International Journal of TESOL & Education, 3(2), 32–42. https://doi.org/10.54855/ijte.23323
Pham, V. P. H. (2021). The effects of lecturer’s model e-comments on graduate students’ peer e-comments and writing revision. Computer Assisted Language Learning, 34(3), 324–357. https://doi.org/10.1080/09588221.2019.1609521
Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12, 22. https://doi.org/10.1186/s41039-017-0062-8
Song, D., Rice, M., & Oh, E. (2022). Participation in online courses and AI-supported feedback. Educational Technology Research and Development, 70(1), 209–232. https://doi.org/10.1007/s11423-021-10066-3
Sullivan, M., Kelly, A., & McLaughlan, P. (2023). ChatGPT in higher education: Considerations for academic integrity and student learning. Journal of Applied Learning & Teaching, 6(1), 31–40. https://doi.org/10.37074/jalt.2023.6.1.17
Sun, A., & Chen, X. (2016). Online education and its effective practice: A research review. Journal of Information Technology Education: Research, 15, 157–190. https://doi.org/10.28945/3502
Teng, M. F. (2024). “ChatGPT is the companion, not enemies”: EFL learners’ perceptions and experiences in using ChatGPT for feedback in writing. Computers & Education: Artificial Intelligence, 7, 100270. https://doi.org/10.1016/j.caeai.2024.100270
Tuomi, I. (2018). The impact of artificial intelligence on learning, teaching, and education: Policies for the future. Publications Office of the European Union. https://doi.org/10.2760/12297
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
Downloads
Citations
Published
Issue
Section
License
Copyright (c) 2026 Vo Thi Quynh Nga

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the picte the right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License 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 proceedings' published version of the work (e.g., post it to an institutional repository, in a journal, or publish it in a book), with an acknowledgment of its initial publication in this proceedings.
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.








