APPLYING UTAUT2 TO AI-DRIVEN IELTS PREPARATION: A STUDY OF CHATGPT ADOPTION

Vany Fitria, Luqman Hakim, Desika Putri Mardiani, Dita Indra Febryanti, Gilang Maulana Majid

Abstract


This study explores the application of UTAUT2 in adopting ChatGPT for IELTS preparation, identifying key influencing factors. A scenario-based online survey with 168 Indonesian social media users was analyzed using partial least square-structural equation modeling (PLS-SEM). Findings reveal that performance expectancy, effort expectancy, social influence, and habit significantly drive behavioral intention. Users perceive ChatGPT as an effective tool for improving IELTS scores through personalized learning and appreciate its ease of use. Social influence from educators and peers also plays a crucial role, while habitual use reinforces trust in ChatGPT’s reliability. Interestingly, facilitating conditions, hedonic motivation and price value were non-significant. Specifically, cost concerns may be less relevant given ChatGPT’s free-tier accessibility, and hedonic motivation may be secondary in a goal-oriented setting like IELTS preparation. These non-significant results might also be shaped by Indonesia’s collectivist culture, where social influence outweighs individualistic motivations such as enjoyment. These findings suggest that the UTAUT2 model may require contextual adaptation for educational technologies, particularly in settings where functionality and effectiveness outweigh cost considerations. This study highlights the need to prioritize performance, ease of use and social influence to drive AI adoption in education.


Keywords


chatGPT; generative AI; IELTS test; language learning; UTAUT2 model

References


Baskara, R., & Mukarto, M. (2023). Exploring the implications of ChatGPT for language learning in higher education. Indonesian Journal of English Language Teaching and Applied Linguistics, 7(2), 343–358.

Bessadok, A. and Hersi, M. (2023), A structural equation model analysis of English for specific purposes students' attitudes regarding computer-assisted language learning: UTAUT2 model. Library Hi Tech. https://doi.org/10.1108/LHT-03-2023-0124

Bhat, A., Tiwari, C. K., Bhaskar, P., & Khan, S. T. (2024). Examining ChatGPT adoption among educators in higher educational institutions using extended UTAUT model. Journal of Information, Communication and Ethics in Society. https://doi.org/10.1108/JICES-03-2024-0033

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008

Dong, L. (2024). “Brave new world” or not?: A mixed-methods study of the relationship between second language writing learners’ perceptions of ChatGPT, behaviors of using ChatGPT, and writing proficiency. Current Psychology, 43(21), 19481–19495. https://doi.org/10.1007/s12144-024-05728-9

Du, Y., & Gao, H. (2021), Determinants afecting teachers’ adoption of AI‑based applications in EFL context: An analysis of analytic hierarchy process. Education and Information Technologies, 27, 9357–9384. https://doi.org/10.1007/s10639-022-11001-y

Edmett, A., Ichaporia, N., Crompton, H., & Crichton, R. (2023). Artificial intelligence and English language teaching: Preparing for the future.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312

Giray, L. (2023). Prompt engineering with ChatGPT: A guide for academic writers. Ann Biomed Eng, 51, 2629–2633. https://doi.org/10.1007/s10439-023-03272-4

Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008

Habibi, A., Muhaimin, M., Danibao, B. K., Wibowo, Y. G., Wahyuni, S., & Octavia, A. (2023). ChatGPT in higher education learning: Acceptance and use. Computers and Education: Artificial Intelligence, 5, 100190. https://doi.org/10.1016/j.caeai.2023.100190

Hair, J. F., Babin, B. J., Black, W. C., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … 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

Kim, J.-H., & Jang, S. (Shawn). (2014). A scenario-based experiment and a field study: A comparative examination for service failure and recovery. International Journal of Hospitality Management, 41, 125–132. https://doi.org/10.1016/j.ijhm.2014.05.004

Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for Language Teaching and Learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868

Liu, X., & Pei, J. (2023). Effects of IELTS reading education by using new media learning environments effectively. Interactive Learning Environments, 31(8), 4977–4993. https://doi.org/10.1080/10494820.2021.1990086

Muluk, S., Zainuddin, Z., & Dahliana, S. (2022). Flipping an IELTS writing course: Investigating its impacts on students’ performance and their attitudes. Studies in English Language and Education, 9(2), 591–612. https://doi.org/10.24815/siele.v9i2.23314

Oppenlaender, J., Linder, R., & Silvennoinen, J. (2024). Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering. International Journal of Human–Computer Interaction, 1–23. https://doi.org/10.1080/10447318.2024.2431761

Polyportis, A., & Pahos, N. (2024). Understanding students’ adoption of the ChatGPT chatbot in higher education: the role of anthropomorphism, trust, design novelty and institutional policy. Behaviour & Information Technology, 1–22. https://doi.org/10.1080/0144929X.2024.2317364

Sabeh, H. N. (2024). What drives IT students toward ChatGPT? Analyzing the factors influencing students’ intention to use ChatGPT for educational purposes. 2024 21st International Multi-Conference on Systems, Signals & Devices (SSD), 533–539. https://doi.org/10.1109/SSD61670.2024.10548826

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

Strzelecki, A. (2024). Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology. Innovative Higher Education, 49(2), 223–245. https://doi.org/10.1007/s10755-023-09686-1

Sutrisno, D. B. (2023). Practicing IELTS writing for L2 writers with ChatGPT; An exploratory self-study. English Department of UMMU Journal, 3(2).

Teng, M. F. (2024). “ChatGPT is the companion, not enemies”: EFL learners’ perceptions and experiences in using ChatGPT for feedback in writing. Computers and Education: Artificial Intelligence, 7, 100270. https://doi.org/10.1016/j.caeai.2024.100270

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.

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.

Vinzi, V. E., Trinchera, L., & Amato, S. (2010). PLS path modeling: From foundations to recent developments and open issues for model assessment and improvement. In Handbook of Partial Least Squares (pp. 47–82). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-32827-8_3

Wang, L., Xu, S., & Liu, K. (n.d.). Understanding students’ acceptance of ChatGPT as a translation tool: A UTAUT model analysis. ArXiv.

Warsidi, Damayanti, N., & Syurganda, A. (2024). The rhetorical model of IELTS speaking test band 7 or above. Journal of English Education and Applied Linguistics, 13(2).

Woo, D. J., Guo, K., & Susanto, H. (2024). Exploring EFL students’ prompt engineering in human–AI story writing: an activity theory perspective. Interactive Learning Environments, 1–20. https://doi.org/10.1080/10494820.2024.2361381

Xiao, Y., & Zhi, Y. (2023). An exploratory study of EFL learners’ use of ChatGPT for language learning tasks: Experience and perceptions. Languages, 8(3), 212. https://doi.org/10.3390/languages8030212

Xu, S., Chen, P., & Zhang, G. (2024). Exploring Chinese University Educators’ Acceptance and Intention to Use AI Tools: An Application of the UTAUT2 Model. Sage Open, 14(4). https://doi.org/10.1177/21582440241290013

Xu, X., Su, Y., Zhang, H., Zhang, Y., & Hao, S. (2024). Beyond theory: A mixed-methods investigation of postgraduate engagement with ChatGPT for IELTS speaking. Research Square (Preprint).




DOI: 10.15408/ijee.v11i2.41275

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.