EFL Students’ Attitudes Toward Generative AI in English Writing

Baby Farhana L Tunggay(1*), Najwa Hanna Habibah(2), Mitsni Uswatun Hasanah(3), Mohammad Ade Rifki(4), Zahroh Arofah(5),


(1) UIN Siber Syekh Nurjati Cirebon
(2) UIN Siber Syekh Nurjati Cirebon
(3) UIN Siber Syekh Nurjati Cirebon
(4) UIN Siber Syekh Nurjati Cirebon
(5) UIN Siber Syekh Nurjati Cirebon
(*) Corresponding Author

Abstract


EFL students often face significant difficulties in English writing, particularly regarding vocabulary limitations, grammatical accuracy, idea organization, and coherence. The rapid development of generative artificial intelligence (AI) has significantly influenced educational practices, particularly in English writing instruction. Tools such as ChatGPT, Grammarly, and QuillBot are increasingly used by students to support various writing activities. This study aimed to investigate EFL students’ attitudes toward the use of generative AI in English writing activities, including their habits, perceived effects, and challenges associated with its use. The study employed a quantitative descriptive survey design involving 54 students from the English Language Teaching Department of a university in Indonesia. Data were collected through an online questionnaire consisting of 16 items grouped into four dimensions: attitudes, habits, effects, and challenges. The data were analyzed using descriptive statistics, including frequency, percentage, mean, median, mode, range, and standard deviation. The findings revealed that students generally demonstrated positive attitudes toward the use of generative AI in English writing activities. Among the four dimensions, the effects of generative AI obtained the highest mean score (M = 3.25), followed by habits (M = 3.21), attitudes (M = 3.17), and challenges (M = 3.10). The findings indicate that students perceived generative AI as beneficial, practical, and supportive in improving their writing activities. Students reported using AI tools to generate ideas, improve grammar and vocabulary, paraphrase sentences, and complete writing assignments more efficiently. However, concerns regarding dependency, reduced independent thinking, and the accuracy of AI-generated content were also identified.

Keywords


attitudes English writing EFL students generative artificial intelligence

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References


Ali, M., Ahmed, N., & Hassan, R. (2024). Students’ perceptions of generative artificial intelligence in higher education: Opportunities and challenges. Journal of Educational Technology and Innovation, 12(1), 45–60.

Chyung, S. Y., Roberts, K., Swanson, I., & Hankinson, A. (2017). Evidence-based survey design: The use of a midpoint on the Likert scale. Performance Improvement, 56(10), 15–23. https://doi.org/10.1002/pfi.21727

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.

Cukurova, M., Luckin, R., & Kent, C. (2024). Generative artificial intelligence and education: Student perceptions and implications for learning. Computers and Education: Artificial Intelligence, 6, 100210.

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

Dawson, P. (2024). Defending assessment in the age of artificial intelligence. Assessment & Evaluation in Higher Education, 49(1), 1–15.

Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). McGraw-Hill.

Gao, X., & Yu, H. (2023). University students’ attitudes toward generative AI tools in academic writing. Journal of Educational Computing Research, 61(7), 1452–1471.

Hyland, K. (2003). Second language writing. Cambridge University Press.

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., Pfeiffer, F., 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. (2023). Artificial intelligence-assisted writing and EFL learners’ writing development. Computer Assisted Language Learning, 36(5–6), 1124–1143.

Li, Y., & Hafner, C. A. (2022). Artificial intelligence and second language writing: Emerging opportunities and challenges. Language Learning & Technology, 26(3), 1–12.

Ref, D., Bridgeman, B., & Adler, R. M. (2023). Ethical concerns and challenges of AI-generated writing in higher education. Educational Technology Research and Development, 71(4), 1561–1579.

OpenAI. (2026). ChatGPT (June 2026 version) [Large language model]. https://chatgpt.com/

Pallant, J. (2020). SPSS survival manual: A step-by-step guide to data analysis using IBM SPSS (7th ed.). McGraw-Hill Education.

Perkins, M. (2023). Academic integrity considerations of AI-generated content. International Journal for Educational Integrity, 19(1), 1–16.

Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach. Computers & Education, 128, 13–35.

Shoufan, A. (2023). Exploring students’ perceptions of ChatGPT in higher education: Benefits and challenges. Education and Information Technologies, 28(11), 14463–14480.

Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 15.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.




DOI: 10.24235/eltecho.v10i2.25677

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