Student Engagement and Perceptions of AI-Powered Tools in ESL Learning: Effectiveness in Higher Education

The integration of Artificial Intelligence (AI) in higher education, particularly in English as a Second Language (ESL) contexts, has revolutionized how writing is taught and learned. This study investigates UiTM Diploma and Degree students’ engagement and perceptions of AI-powered writing tools such as Grammarly, QuillBot, ChatGPT, and Google Translate in ESL writing classrooms. Adapted from the methodological frameworks of Phan (2023) and Utami et al. (2023), this mixed-method study combined quantitative (questionnaire) and qualitative (semi-structured interviews) data from 120 UiTM Perak branch, Tapah campus students. Results reveal that students view AI tools as highly accessible, engaging, and effective in improving writing performance, vocabulary range, and grammatical accuracy. However, challenges such as overdependence, limited critical evaluation, and ethical concerns were also identified. Findings suggest that AI tools enhance student engagement and writing motivation when appropriately integrated but should remain supplementary to human teaching. The study offers pedagogical implications for Malaysian ESL lecturers in balancing technological facilitation and academic integrity in the age of AI-assisted learning.

Artificial Intelligence, ESL writing, student engagement

1. Alhalangy, A. G. I., & AbdAlgane, M. (2023). Exploring the impact of AI on the EFL context: A case study of Saudi universities. Journal of Intercultural Communication, 23(2), 41–49. https://doi.org/10.36923/jicc.v23i2.125. (AsiaCALL) [Google Scholar] [Crossref]

2. Bailin, A. (1987). Artificial intelligence and computer-assisted language instruction: A perspective. CALICO Journal, 5(3), 25–45. https://doi.org/10.1558/cj.v5i3.25-45. (AsiaCALL) [Google Scholar] [Crossref]

3. 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. (JSTOR) [Google Scholar] [Crossref]

4. Fitria, T. N. (2021). Grammarly as AI-powered English writing assistant: Students’ alternative for writing English. Metathesis: Journal of English Language, Literature, and Teaching, 5(1), 65–78. https://doi.org/10.31002/metathesis.v5i1.3519. (AsiaCALL) [Google Scholar] [Crossref]

5. Gayed, J. M., Carlon, M. K. J., Oriola, A. M., & Cross, J. S. (2022). Exploring an AI-based writing assistant’s impact on English language learners. Computers & Education: Artificial Intelligence, 3, 100055. https://doi.org/10.1016/j.caeai.2022.100055. (AsiaCALL) [Google Scholar] [Crossref]

6. Ng, C., Wahyuni, D., & Liu, Y. (2022). AI-supported feedback and student writing engagement. (lltjournal.org) [Google Scholar] [Crossref]

7. Nguyen, S., Liu, S., & Yu, G. (Example related LLT article cited for comparison): Liu, S., & Yu, G. (2022). L2 learners’ engagement with automated feedback: An eye-tracking study. Language Learning & Technology, 26(2), 78–105. https://doi.org/10.64152/10125/73480. (lltjournal.org) [Google Scholar] [Crossref]

8. Phan, T. N. L. (2023). Students’ perceptions of the AI technology application in English writing classes. Proceedings of the AsiaCALL International Conference, 4, 45–62. https://doi.org/10.54855/paic.2344. (AsiaCALL) [Google Scholar] [Crossref]

9. Utami, S. P. T., Andayani, R., & Sumarwati, S. (2023). Utilization of artificial intelligence technology in an academic writing class: How do Indonesian students perceive? Contemporary Educational Technology, 15(4), Article ep450. https://doi.org/10.30935/cedtech/13419. (cedtech.net) [Google Scholar] [Crossref]

10. Vo, T. P. (2022). The application of artificial intelligence in teaching English writing. Proceedings of National Scientific Conference (UNC): Research and Teaching Foreign Languages, Linguistics and International Studies in Vietnam (Vol. 2, pp. 577–591). [publisher: Hanoi National University Publishing House]. (AsiaCALL) [Google Scholar] [Crossref]

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