Evaluating students’ perceptions, writing outcomes, and challenges in using AI-based feedback systems for english academic writing

Sri Wahyuni; Desi Surlitasari Dewi; Rini Susanti Wulandari; Thohiriyah; Didit Kurniadi; Fani Hindun Muawanah; Nadya Zulfa Chairunnisa; Mega Aulia Ardhi

doi:10.31893/multirev.2026380

Keywords:

automated writing evaluation

student technology acceptance

digital learning tools

generative AI systems

EFL composition

revision practices

Abstract

The increasing prevalence of artificial intelligence (AI) in higher education has transformed student engagement with academic writing, especially via automated feedback systems. This study seeks to investigate undergraduate students' experiences with AI-driven feedback tools by analyzing three primary dimensions: perceived utility, usability, and system dependability. The study employed a mixed-methods approach and included 156 English Education undergraduates from two Indonesian institutions who often utilized multiple AI-assisted platforms, including Grammarly, ChatGPT, Quillbot, and Microsoft Copilot. Quantitative data were collected using a five-point Likert-scale questionnaire, while qualitative insights were derived from open-ended responses aimed at capturing personal perspectives on benefits and obstacles. The results indicate that students predominantly possess favorable views on AI-assisted feedback, with usefulness attaining the highest rating (M = 4.24, SD = 0.67). Students indicated enhancements in grammatical precision (M = 4.32), vocabulary sophistication (M = 4.21), and increased understanding of syntactic structure and lexical selection. The ease of use received a favorable rating (M = 4.18), mostly attributed to the accessibility and immediacy of automatic response. Nonetheless, reliability received a marginally lower grade (M = 3.91), as students observed sporadic mistakes, inconsistent recommendations, and a restricted comprehension of context or rhetorical meaning. Qualitative responses underscored difficulties including excessive dependence on AI corrections, ambiguous error explanations, and inadequate help for advanced writing elements such as idea development and coherence. The study indicates that AI-driven feedback is a beneficial adjunct to writing training, providing efficiency and linguistic assistance; yet, it cannot entirely supplant human feedback in managing content arrangement and more profound conceptual requirements. An equitable integration of AI technologies with instructor facilitation is crucial to optimize their educational influence on students' academic writing advancement.
References
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Wahyuni, S., Dewi, D. S., Wulandari, R. S., Thohiriyah, Kurniadi, D., Muawanah, F. H., Chairunnisa, N. Z., & Ardhi, M. A. (2026). Evaluating students’ perceptions, writing outcomes, and challenges in using AI-based feedback systems for english academic writing. Multidisciplinary Reviews, 9(8), 2026380. https://doi.org/10.31893/multirev.2026380

[1] Barrett, A., & Pack, A. (2023). Not quite eye to A.I.: Student and teacher perspectives on the use of generative artificial intelligence in the writing process. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00427-0

[2] Barrot, J. S. (2023). Using automated written corrective feedback in the writing classrooms: Effects on L2 writing accuracy. Computer Assisted Language Learning, 36(4), 584–607. https://doi.org/10.1080/09588221.2021.1936071

[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

[4] Dewi, D. S., Hartono, R., Wahyuni, S., & Lestari, S. (2025). Fostering learner autonomy in EFL settings through multiliteracy pedagogy: A case in tertiary education. Australian Journal of Teacher Education, 50(2), 1–19. https://doi.org/10.14221/1835-517X.6369

[5] El Shazly, R. (2021). Effects of artificial intelligence on English speaking anxiety and speaking performance: A case study. Expert Systems, 38(3), e12667. https://doi.org/10.1111/exsy.12667

[6] George, D., & Mallery, M. (2019). SPSS for Windows step by step: A simple guide and reference (15th ed.). Routledge.

[7] Grassini, S. (2023). Shaping the future of education with artificial intelligence: Opportunities, challenges, and implications. Education Sciences, 13(7), 692. https://doi.org/10.3390/educsci13070692

[8] Haddadian, G. (2024). Comparing the effects of teacher feedback, automated feedback, and integrative feedback on EFL learners’ writing accuracy and writing apprehension. Computer Assisted Language Learning Electronic Journal (CALL-EJ), 25(3), 124–147. https://callej.org/index.php/journal/article/view/436

[9] Haetami. (2025). AI-driven educational transformation in Indonesia: From learning personalization to institutional management. Al-Ishlah: Jurnal Pendidikan, 17(2), 1819–1832. https://doi.org/10.35445/alishlah.v17i2.7448

[10] Hayder, M., & Mahdi, M. A. (2025). The effect of artificial intelligence in the educational process of the English language. American Journal of Research in Humanities and Social Sciences, 33, 21–33. https://americanjournal.org/index.php/ajrhss/article/view/2728

[11] Hellín, C. J., Calles-Esteban, F., Valledor, A., Gómez, J., Otón-Tortosa, S., & Tayebi, A. (2023). Enhancing student motivation and engagement through a gamified learning environment. Sustainability, 15(19), 14119. https://doi.org/10.3390/su151914119

[12] Jeong, K. O. (2018). Developing EFL learners’ communicative competence through multimedia-assisted language learning. Journal of Theoretical and Applied Information Technology, 96(5), 1367–1376.

[13] Kacena, M. A., Plotkin, L. I., & Fehrenbacher, J. C. (2024). The use of artificial intelligence in writing scientific review articles. Current Osteoporosis Reports, 22(1), 115–121. https://doi.org/10.1007/s11914-023-00852-0

[14] Kim, J., Yu, S., Detrick, R., & Li, N. (2025). Exploring students’ perspectives on generative AI-assisted academic writing. Education and Information Technologies, 30(1), 1265–1300. https://doi.org/10.1007/s10639-024-12878-7

[15] Kundu, A., & Bej, T. (2025). Transforming EFL teaching with AI: A systematic review of empirical studies. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-025-00470-0

[16] Lestari, I., Dewi, D. S., & Shalehoddin, S. (2024). Gamifying vocabulary learning: The effects on students’ acquisition. ELT Echo, 9(2), 130–142. https://doi.org/10.24235/eltecho.v9i2.19077

[17] Nazari, N., Shabbir, M. S., & Setiawan, R. (2021). Application of artificial intelligence-powered digital writing assistant in higher education: A randomized controlled trial. Heliyon, 7(5), e07014. https://doi.org/10.1016/j.heliyon.2021.e07014

[18] Peng, Y., & Barrot, J. S. (2023). Post-writing form-focused instruction in process-genre-oriented writing classrooms: Effects on second language learners’ writing accuracy. Porta Linguarum, 39, 249–264. https://doi.org/10.30827/portalin.vi39.24276

[19] Pratama, H., Putri, S. P., & Bahri, S. (2025). Potential risks of ChatGPT-assisted essay writing on knowledge retention among EFL learners in Indonesia. Language and Education Journal, 10(1), 148–168. https://doi.org/10.52237/1j0f2165

[20] Putri, A., & Dewi, D. S. (2025). Gamifying vocabulary learning: Students’ perceptions of Kahoot! in a junior high school. ELT Echo, 9(1), 71–86. https://doi.org/10.36597/jellt.v9i1.19327

[21] Salvagno, M., Taccone, F. S., & Gerli, A. G. (2023). Can artificial intelligence help for scientific writing? Critical Care, 27(1), 1–5. https://doi.org/10.1186/s13054-023-04380-2

[22] Scherer, R., Tondeur, J., Siddiq, F., & Baran, E. (2018). The importance of attitudes toward technology for pre-service teachers’ technological, pedagogical, and content knowledge: Comparing structural equation modeling approaches. Computers in Human Behavior, 80, 67–80. https://doi.org/10.1016/j.chb.2017.11.003

[23] Sparks, J. R., Song, Y., Brantley, W., & Liu, O. L. (2014). Assessing written communication in higher education: Review and recommendations for next-generation assessment. ETS Research Report Series, 2014(2), 1–52. https://doi.org/10.1002/ets2.12035

[24] Su, L., Noordin, N., & Jeyaraj, J. J. (2023). Implementation of strategy instruction in teaching English as a foreign language: A systematic review. International Journal of Learning, Teaching and Educational Research, 22(7), 156–172. https://doi.org/10.26803/ijlter.22.7.9

[25] Yenduri, G., Ramalingam, M., Selvi, G. C., Supriya, Y., Srivastava, G., Maddikunta, P. K. R., Raj, G. D., Jhaveri, R. H., Prabadevi, B., Wang, W., Vasilakos, A. V., & Gadekallu, T. R. (2024). GPT (generative pre-trained transformer): A comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions. IEEE Access, 12, 54608–54649. https://doi.org/10.1109/ACCESS.2024.3389497

[26] Yu, S., Di Zhang, E., & Liu, C. (2022). Assessing L2 student writing feedback literacy: A scale development and validation study. Assessing Writing, 53, 100643. https://doi.org/10.1016/j.asw.2022.100643

Abstract

References

How to cite