University Teachers’ Adoption of AI Writing Tools in Teaching English as a Foreign Language Academic Writing: Mixed Methods Research
DOI:
https://doi.org/10.46328/ijte.5422Keywords:
University Teachers, Adoption, AI Writing Tools, English as a foreign language (EFL), FactorsAbstract
Although AI-powered writing tools (such as Grammarly, ProWritingAid, QuillBot, ChatGPT, and DeepSeek) are widely used in English academic writing and have brought new opportunities to teaching, their adoption by Chinese EFL university teachers is influenced by a variety of factors. Therefore, this study uses the Technology Acceptance Model 3 (TAM3) to explore the factors influencing the adoption of AI-powered writing tools by Chinese university teachers. This study used mixed methods research in which the researchers conducted a survey questionnaire on 240 English teachers from 12 public universities in China and selected 12 teachers for semi-structured interviews. The findings demonstrated that teachers’ adoption of AI writing tools was significantly influenced by perceived usefulness, perceived ease of use, and external factors. Furthermore, perceived usefulness has a minor but significant influence on teachers’ behavior intention, while perceived ease of use has the biggest influence, followed by external factors. This study is based on the TAM3, which is helpful in providing a theoretical framework to guide technology design and teaching practice, integrate educational technology policies, and provide a new empirical exploration model.
References
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology Acceptance Model in M-learning context: A systematic review. Computers & Education, 125, 389-412. https://doi.org/10.1016/j.compedu.2018.06.008
Aljohani, N. J. (2022). Longitudinal study of a procedure for training low-proficiency english language students. Asian-Pacific Journal of Second and Foreign Language Education, 7(1), 27. https://doi.org/10.1186/s40862-022-00154-5
Aljuaid, H. (2024). The impact of artificial intelligence tools on academic writing instruction in higher education: A systematic review. Arab World English Journal (AWEJ) Special Issue on ChatGPT. https://ssrn.com/abstract=4814342
Alrishan, A. M. H. (2023). Determinants of intention to use ChatGPT for professional development among Omani EFL pre-service teachers. International Journal of Learning, Teaching and Educational Research, 22(12), 187-209. https://doi.org/10.26803/ijlter.22.12.10
Atasoy, A. (2021). The Relationship Between Writing Self-Efficacy and Writing Skill: A Meta-Analysis Study. Education & Science/Egitim ve Bilim, 46(208). DOI: 10.15390/EB.2021.10024
Castellanos-Gomez, A. (2023). Good practices for scientific article writing with ChatGPT and other artificial intelligence language models. Nanomanufacturing, 3(2), 135-138. https://doi.org/10.3390/nanomanufacturing3020009
Chou, C. M., Shen, T. C., Shen, T. C., & Shen, C. H. (2024). Teachers’ adoption of AI-supported teaching behavior and its influencing factors: Using structural equation modeling. Journal of Computers in Education, 1-44. https://doi.org/10.1007/s40692-024-00332-z
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008
Ferris, D. (2011). Treatment of error in second language student writing. University of Michigan Press.
Ghimire, A., Pather, J., & Edwards, J. (2024, October). Generative AI in education: A study of Educators' awareness, sentiments, and influencing factors. In 2024 IEEE Frontiers in Education Conference (FIE) (pp. 1-9). IEEE. https://doi.org/10.1109/FIE61694.2024.10892891
Giray, O. (2024). Contributions of Intelligence Agencies to Türkiye's Security. BRIQ Belt & Road Initiative Quarterly, 6(1), 74-91. https://nbn-resolving.org/urn:nbn:de:0168-ssoar-98640-6
Guo, S., Shi, L., & Zhai, X. (2024). Validating an Instrument for Teachers' Acceptance of Artificial Intelligence in Education. arXiv preprint arXiv:2406.10506. https://doi.org/10.48550/arXiv.2406.10506
Hao, H. X., & Razali, A. B. (2025). Impact of ChatGPT on English academic writing ability and engagement of Chinese EFL undergraduates. International Journal of Instruction, 18(2), 323-346. https://doi.org/10.29333/iji.2025.18218a
He, Y. (2021, May). Analysis on the comparison between Chinese and Western classroom teaching. In 7th International Conference on Humanities and Social Science Research (ICHSSR 2021) (pp. 63-66). Atlantis Press. https://doi.org/10.2991/assehr.k.210519.012
Holzinger, A., Searle, G., & Wernbacher, M. (2011). The effect of previous exposure to technology on acceptance and its importance in usability and accessibility engineering. Universal Access in the Information Society, 10, 245-260. https://doi.org/10.1007/s10209-010-0212-x
Hsieh, W. M., & Tsai, C. C. (2017). Taiwanese high school teachers' conceptions of mobile learning. Computers & Education, 115, 82-95. https://doi.org/10.1016/j.compedu.2017.07.013
Hu, L., Wang, H., & Xin, Y. (2025). Factors influencing Chinese pre-service teachers’ adoption of generative AI in teaching: an empirical study based on UTAUT2 and PLS-SEM. Education and Information Technologies, 1-23. https://doi.org/10.1007/s10639-025-13353-7
Hwang, G. J. (2014). Definition, framework and research issues of smart learning environments-a context-aware ubiquitous learning perspective. Smart Learning Environments, 1, 1-14. https://doi.org/10.1186/s40561-014-0004-5
Jiang, Y., Zhang, B., Zhao, Y., & Zheng, C. (2022). China’s preschool education toward 2035: Views of key policy experts. Ecnu review of education, 5(2), 345-367. https://doi.org/10.1177/20965311211012705
Khanh, N. T. V., & Gim, G. (2014). Factors influencing mobile-learning adoption intention: An empirical investigation in high education. Journal of Social Sciences, 10(2), 51-62. http://www.thescipub.com/jss.toc
Khatri, B. B., & Karki, P. D. (2023). Artificial intelligence (AI) in higher education: Growing academic integrity and ethical concerns. Nepalese Journal of Development and Rural Studies, 20(01), 1-7. https://doi.org/10.3126/njdrs.v20i01.64134
Kinshuk, Chen, N. S., Cheng, I. L., & Chew, S. W. (2016). Evolution is not enough: Revolutionizing current learning environments to smart learning environments. International Journal of Artificial Intelligence in Education, 26(2), 561-581. https://doi.org/10.1007/s40593-016-0108-x
Li, H. (2023). AI in education: Bridging the divide or widening the gap? Exploring equity, opportunities, and challenges in the digital age. Advances in Education, Humanities and Social Science Research, 8(1), 355-355. https://doi.org/10.56028/aehssr.8.1.355.2023
Raj, R., & Kos, A. (2023). Artificial Intelligence: Evolution, Developments, Applications, and Future Scope. Przeglad Elektrotechniczny, 99(2). DOI: 10.15199/48.2023.02.01
Rizkiani, S., Maulana, A., Resmini, S., & Satriani, I. (2024). EXAMINING THE USE OF AI TOOLS IN ACADEMIC WRITING: EFFECTS ON THE CRITICAL THINKING SKILLS OF EFL LEARNERS. JELA (Journal of English Language Teaching, Literature and Applied Linguistics), 6(2), 111-121. https://doi.org/10.37742/jela.v6i2.155
Salleh, S. (2016). Examining the influence of teachers' beliefs towards technology integration in classroom. The International Journal of Information and Learning Technology, 33(1), 17-35. https://doi.org/10.1108/IJILT-10-2015-0032
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & education, 128, 13-35. https://doi.org/10.1016/j.compedu.2018.09.009
Shofiah, N., & Putera, Z. F. (2024). Examining the user experience of artificial intelligence tools in academic writing: The perceptions lecturers practices. https://doi.org/10.21203/rs.3.rs-3871916/v1
Tajik, A. (2025). Exploring the role of AI-driven dynamic writing platforms in improving EFL learners' writing skills and fostering their motivation. https://doi.org/10.21203/rs.3.rs-5788599/v1
Tekin, Ö. G. (2024). Factors affecting teachers' acceptance of artificial intelligence technologies: Analyzing teacher perspectives with structural equation modeling. Instructional Technology and Lifelong Learning, 5(2), 399-420. https://doi.org/10.52911/itall.1532218
Teo, T., Sang, G., Mei, B., & Hoi, C. K. W. (2019). Investigating pre-service teachers’ acceptance of Web 2.0 technologies in their future teaching: a Chinese perspective. Interactive Learning Environments, 27(4), 530-546. https://doi.org/10.1080/10494820.2018.1489290
Wang, Y., Liu, C., & Tu, Y. F. (2021). Factors affecting the adoption of AI-based applications in higher education. Educational Technology & Society, 24(3), 116-129. https://www.jstor.org/stable/27032860
Weigle, S. C. (2002). Assessing writing. Cambridge University Press.
Wu, H., Wang, Y., & Wang, Y. (2024). “To Use or Not to Use?” A mixed-methods study on the determinants of EFL college learners’ behavioral intention to use AI in the distributed learning context. International Review of Research in Open and Distributed Learning, 25(3), 158-178. https://doi.org/10.19173/irrodl.v25i3.7708
Yu, H., & Yan, P. (2024). The Academic Writing Challenges of Undergraduate Students: An Exploratory Study at a Sino-Foreign University in China. In English for Academic Purposes in the EMI Context in Asia: XJTLU Impact (pp. 15-43). Cham: Springer Nature Switzerland. https://doi.org/10.29333/iji.2025.18218a
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