The Correlation among AI-Assisted English Learning, Technology Acceptance, and Willingness to Use English among English Education Alumni Graduates from UIN Raden Mas Said Surakarta
DOI:
https://doi.org/10.71282/jurmie.v2i12.1337Keywords:
AI-Assisted English Learning, Technology Acceptance, Willingness to Use English, English Education AlumniAbstract
This quantitative correlational study was conducted to find out the relationship between AI-Assisted English Learning, Tech Acceptance, and Willingness to Use English in the AI Learning, with the target population being the graduates of the English Language Education program at UIN Raden Mas Said Surakarta. There were 30 respondents taken for this quantitative correlation analysis, with the respondents completing an online questionnaire designed in relation to the AI usage scale, Tech Acceptance Model, and Willingness to Communicate. The data revealed that both predictors were found to be at high levels, denoting the involvement of the graduates in learning English and their positive attitude towards technology. AI-supported learning in English was revealed to be positively significant with Willingness to Use English (ρ =.386, p =.035), indicating that regular use of AI technologies boosts alumni confidence and drive when speaking English. In contrast, Willingness to Use English did not significantly correlate with Technology Acceptance (ρ =.155, p =.414). Nonetheless, the combination of the two variables explained 23.2% of the variance (R² =.232) and significantly contributed to the variation in Willingness to Use English (F = 4.085, p =.028). This result implied that the integration of AI with English language learning could enhance the quality of graduates’ communication preparedness, especially when they possessed an optimistic attitude towards technology. Future studies involving more factors, such as affective elements, should be conducted to further explore English language usage behavior post-graduation.
Downloads
References
Cohen, L., Manion, L., & Morrison, K. (2018). Research Methods in Education (8th ed.). Routledge.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. Management Information Systems Center, University of Minnesota.
Earle, R. S. (2002). The Integration of Instructional Technology into Public Education: Promises and Challenges. Educational Technology, 42(1), 5–13.
Egbert, J., & Shahrokni, A. S. (2018). CALL Principles and Practices. Open Text. Washington State University.
Graddol, D. (2006). English Next: Why Global English May Mean the End of “English as a Foreign Language”. British Council, 128 pp.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education Promises and Implications for Teaching and Learning. Encyclopedia of Education and Information Technologies.
Hwang, M., Lee, E., & Lee, H.-K. (2025). Exploring EFL learners’ acceptance of ChatGPT: Application of the extended Technology Acceptance Model. English Teaching, 80(1), 45–69.
Liu, G., & Ma, C. (2024). Measuring EFL Learners’ Use of ChatGPT in Informal Digital Learning of English Based on the Technology Acceptance Model. Innovation in Language Learning and Teaching, 18(2), 125–138.
Macintyre, P. D., Clément, R., Dornyei, Z. & Noels, K. A. (1998). Conceptualizing willingness to communicate in a L2: A Situational Model of L2 Confidence and Affiliation. Modern Language Journal, 82(4), 545–562.
Mahapatra, S. (2024). Impact of ChatGPT on ESL Students’ Academic Writing Skills: A Mixed Methods Intervention Study. Smart Learning Environments, 11(9), 1–18.
Peng, J. E., & Woodrow, L. (2010). Willingness To Communicate in English: A Model in the Chinese EFL Classroom Context. Language Learning, 60(4), 834–876.
Putri, I. C. (2025). Using Chatgpt to Support EFL Writing: Student Insights and Experiences. Eltin Journal of English Language Teaching in Indonesia, 13(2), 163–176.
Seemiller, C., & Grace, M. (2016). Generation Z goes to college. San Francisco, CA: Jossey-Bass.
Teo, T. (2010). A Path Analysis of Pre-Service Teachers’ Attitudes to Computer Use: Applying and Extending the Technology Acceptance Model in an Educational Context. Interactive Learning Environments, 18(1), 65–79.
Turner, A. (2015). Generation Z: Technology and Social Interest. The Journal of Individual Psychology, 71(2), 103–113.
UNESCO. (2021). AI and education_ guidance for policy-makers. UNESCO Digital Library.
Venkatesh, V., & Davis, F. D. (2000). Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204.
Zadorozhnyy, A., & Lee, J. S. (2025). Informal Digital Learning of English and Willingness to Communicate in a Second Language: Self-Efficacy Beliefs as a Mediator. Computer Assisted Language Learning, 38(4), 669–689.
Zhao, D., Jablonkai, R. R., & Sandoval-Hernandez, A. (2024). Enhancing Willingness to Communicate in English Among Chinese Students in the UK: The Impact of MALL With Duolingo and Hellotalk. Journal of China Computer-Assisted Language Learning, 4(1), 42–73.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Adam Ghivari, Sujito (Author)

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










