Exploring the Challenges of Automated Writing Feedback in EFL Writing: Learners’ Experiences with Write & Improve
DOI:
https://doi.org/10.61987/jelp.v1i1.44Keywords:
Write & Improve, Automated Feedback, EFL Writing, Writing SkillsAbstract
This study aims to explore the challenges experienced by learners when using Write & Improve with Cambridge to develop writing skills, with attention to feedback interpretation, idea development, revision, and students’ willingness to continue using automated feedback. The study employed a qualitative case study design involving 31 students. Data were collected through a seven-question questionnaire administered via Google Forms and analyzed using thematic analysis to identify patterns in students’ experiences and perceptions. The findings show that Write & Improve was useful for identifying grammatical errors, providing immediate feedback, and supporting writing practice. However, students also experienced limitations because the feedback was perceived as insufficiently personalized and less helpful for developing ideas, sentence variety, coherence, organization, and understanding the reasons behind corrections. The study concludes that automated feedback can support writing development but cannot replace human guidance and student engagement. The implication is that Write & Improve should be integrated as a complementary tool alongside teacher feedback, reflection, and revision activities. This approach can promote meaningful, independent, and reflective writing development.
References
Arias Valencia, M. M. (2022). Principles, scope, and limitations of the methodological triangulation. Investigacion y Educacion En Enfermeria, 40(2). https://doi.org/10.17533/udea.iee.v40n2e03
Byrne, D. (2022). A worked example of Braun and Clarke's approach to reflexive thematic analysis. Quality & Quantity, 56(3), 1391–1412. https://doi.org/10.1007/s11135-021-01182-y
Chen, S.-Y. R. (2026). Two voices, one draft: a diachronic comparison of human and AI feedback in EFL essay writing. Australian Journal of Applied Linguistics, 9, 103200. https://doi.org/10.29140/ajal.2026.103200
Chung, E. S., & Ahn, S. (2022). The effect of using machine translation on linguistic features in L2 writing across proficiency levels and text genres. Computer Assisted Language Learning, 35(9), 2239–2264. https://doi.org/10.1080/09588221.2020.1871029
Deepshikha, D. (2025). A comprehensive review of AI-powered grading and tailored feedback in universities. Discover Artificial Intelligence, 5(1), 251. https://doi.org/10.1007/s44163-025-00517-0
Henderson, M., Bearman, M., Chung, J., Fawns, T., Buckingham Shum, S., Matthews, K. E., & de Mello Heredia, J. (2026). Comparing generative AI and teacher feedback: student perceptions of usefulness and trustworthiness. Assessment & Evaluation in Higher Education, 51(5), 863–878. https://doi.org/10.1080/02602938.2025.2502582
Hoang, D. T. N., & Hoang, T. (2024). Enhancing EFL students' academic writing skills in online learning via Google Docs-based collaboration: a mixed-methods study. Computer Assisted Language Learning, 37(7), 1504–1526. https://doi.org/10.1080/09588221.2022.2083176
Jiang, L., & Yu, S. (2022). Appropriating automated feedback in L2 writing: experiences of Chinese EFL student writers. Computer Assisted Language Learning, 35(7), 1329–1353. https://doi.org/10.1080/09588221.2020.1799824
Jitpanich, T., Leong, L. M., & Ismail, S. A. M. M. (2022). Assessing the English writing needs of undergraduate business administration students for ESP writing course development: a case study in Thailand. LEARN Journal: Language Education and Acquisition Research Network, 15(2), 104–128.
Lee, A. V. Y. (2023). Supporting students' generation of feedback in large-scale online course with artificial intelligence-enabled evaluation. Studies in Educational Evaluation, 77, 101250. https://doi.org/10.1016/j.stueduc.2023.101250
Li, L., & Kim, M. (2024). It is like a friend to me: critical usage of automated feedback systems by self-regulating English learners in higher education. Australasian Journal of Educational Technology, 40(1), 1–18. https://doi.org/10.14742/ajet.8821
Link, S., Mehrzad, M., & Rahimi, M. (2022). Impact of automated writing evaluation on teacher feedback, student revision, and writing improvement. Computer Assisted Language Learning, 35(4), 605–634. https://doi.org/10.1080/09588221.2020.1743323
Mao, Z., Lee, I., & Li, S. (2024). Written corrective feedback in second language writing: a synthesis of naturalistic classroom studies. Language Teaching, 57(4), 449–477. https://doi.org/10.1017/S0261444823000393
Morgan, H. (2024). Using triangulation and crystallization to make qualitative studies trustworthy and rigorous. The Qualitative Report, 29(7), 1844–1856. https://doi.org/10.46743/2160-3715/2024.6071
Mundiri, A., & Furqon, M. (2023). From Digital Schools to Intelligent Schools: Exploring AI Governance Practices Among Educational Leaders in Managing Responsible Innovation. Journal of Educational Management Research, 2(2), 147-162.
Muñoz, B. C. M., Nassaji, H., & Carrillo, F. I. B. (2025). ChatGPT-generated versus human direct corrective feedback on L2 writing. System, 134, 103805. https://doi.org/10.1016/j.system.2025.103805
Musthofa, A. J., Mulyadi, M., Asrori, M., Furqon, M., & Rohmad, R. (2026). Quality Culture Management in Islamic Educational Institutions: Balancing Religious Identity and Global Standards. AL-ISHLAH: Jurnal Pendidikan, 18(2). https://doi.org/10.35445/alishlah.v18i2.9890
Normawati, A., & Nugrahaeni, D. A. (2024). Grammar teaching and learning in English language class: students' view. English Education: Journal of English Teaching and Research, 9(1), 41–48. https://doi.org/10.29407/jetar.v9i1.21536
Nunes, A., Cordeiro, C., Limpo, T., & Castro, S. L. (2022). Effectiveness of automated writing evaluation systems in school settings: a systematic review of studies from 2000 to 2020. Journal of Computer Assisted Learning, 38(2), 599–620. https://doi.org/10.1111/jcal.12635
Octaviani, R. P., Jannah, L. M., Sebrina, M., & Arochman, T. (2024). The impacts of first language on students' English pronunciation. IJIET (International Journal of Indonesian Education and Teaching), 8(1), 164–173. https://doi.org/10.24071/ijiet.v8i1.6758
Owojori, O. M., Mulaudzi, R., & Edokpayi, J. N. (2022). Student's knowledge, attitude, and perception (KAP) to solid waste management: a survey towards a more circular economy from a rural-based tertiary institution in South Africa. Sustainability, 14(3), 1310. https://doi.org/10.3390/su14031310
Pinto-Llorente, A. M., & Izquierdo-Álvarez, V. (2024). Digital learning ecosystem to enhance formative assessment in second language acquisition in higher education. Sustainability, 16(11), 4687. https://doi.org/10.3390/su16114687
Rad, H. S., Alipour, R., & Jafarpour, A. (2024). Using artificial intelligence to foster students' writing feedback literacy, engagement, and outcome: a case of Wordtune application. Interactive Learning Environments, 32(9), 5020–5040. https://doi.org/10.1080/10494820.2023.2208170
Rahimi, M., Fathi, J., & Zou, D. (2025). Exploring the impact of automated written corrective feedback on the academic writing skills of EFL learners: an activity theory perspective. Education and Information Technologies, 30(3), 2691–2735. https://doi.org/10.1007/s10639-024-12896-5
Rintaningrum, R. (2023). Technology integration in English language teaching and learning: benefits and challenges. Cogent Education, 10(1), 2164690. https://doi.org/10.1080/2331186X.2022.2164690
Shadiev, R., & Feng, Y. (2024). Using automated corrective feedback tools in language learning: a review study. Interactive Learning Environments, 32(6), 2538–2566.
Shen, C., Shi, P., Guo, J., Xu, S., & Tian, J. (2023). From process to product: writing engagement and performance of EFL learners under computer-generated feedback instruction. Frontiers in Psychology, 14, 1258286. https://doi.org/10.3389/fpsyg.2023.1258286
Shi, H., & Aryadoust, V. (2024). A systematic review of AI-based automated written feedback research. ReCALL, 36(2), 187–209. https://doi.org/10.1017/S0958344023000265
Shoufan, A. (2023). Exploring students' perceptions of ChatGPT: thematic analysis and follow-up survey. IEEE Access, 11, 38805–38818. https://doi.org/10.1109/ACCESS.2023.3268224
Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students' academic development. Education Sciences, 15(3), 343. https://doi.org/10.3390/educsci15030343
Wahyudin, A., Prabowo, S. L., Octaberlina, L. R., Furqon, M., & Cahyani, O. D. (2025). Digital Public Relations in Islamic Boarding Schools: The SANtren Pro-Media Model for Value-Governed Stakeholder Engagement. JUMPA: Jurnal Manajemen Pendidikan, 6(2), 453-468.
Wale, B. D., & Kassahun, Y. F. (2024). The transformative power of AI writing technologies: enhancing EFL writing instruction through the integrative use of Writerly and Google Docs. Human Behavior and Emerging Technologies, 2024(1), 9221377. https://doi.org/10.1155/2024/9221377
Zahroh, E. A., & Furqon, M. (2025). Social Construction of FOMO (Fear of Missing Out) for Psychological Well-Being among Generation Z. Journal of Psychological Insight, 1(1), 41-50. https://doi.org/10.61987/jpi.v1i1.674
Zhao, D. (2025). The impact of AI-enhanced natural language processing tools on writing proficiency: an analysis of language precision, content summarization, and creative writing facilitation. Education and Information Technologies, 30(6), 8055–8086. https://doi.org/10.1007/s10639-024-13145-5
Zou, B., Liviero, S., Ma, Q., Zhang, W., Du, Y., & Xing, P. (2024). Exploring EFL learners' perceived promise and limitations of using an artificial intelligence speech evaluation system for speaking practice. System, 126, 103497. https://doi.org/10.1016/j.system.2024.103497
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Rifqi Naufal Ramadhan, Panji Jovan Ramadhani

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




