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Using Automated Feedback To Improve EFL Students’ Self-Regulated Writing Strategies: A Literature Review

The development of effective writing skills is a crucial aspect of language learning, particularly for English as a Foreign Language (EFL) students. Self-regulated writing strategies are critical for EFL students’ success in writing tasks (Han & Sari, 2022). However, EFL students often struggle to develop these strategies due to various factors, such as a lack of opportunities to practice writing and receive feedback. Automated feedback, a new technology, has been considered a potential solution to these challenges (Tran & Nguyen, 2021). This literature review will examine the research and discussions on using automated feedback to improve EFL students’ self-regulated writing strategies, explore empirical studies on automated feedback in EFL writing, and discuss the resulting criticisms.

Integrating Automated Learning into EFL Writing

Integrating automated learning in EFL writing has gained immense popularity as a means of enhancing students’ writing skills. This approach involves the utilization of technology such as machine learning and artificial intelligence (AI) to provide personalized feedback and support to students during their learning process. Numerous studies over the years have investigated the effectiveness of integrating automated learning into EFL writing, with Lee (2020) exploring how AI-based automated assessment enhances EFL students’ writing skills by producing significant improvements in numerous indicators, including their fluency, coherence, and cohesion.

Additionally, Han and Sari (2022) discovered that the use of automated feedback greatly aids in correcting errors found within students’ approaches towards composition, resulting in subsequently helping improve overall skill sets. Results from further research by Huang and Renandya (2020) comment on how students frequently portray an increased audience understanding due to newfound user-friendly systems that greatly impact one’s levels of understanding.

Self-Regulated Strategies

Self-regulated learning is essential to effective writing as it empowers English as a Foreign Language (EFL) students to guide their writing and make informed decisions when tackling various writing tasks (Rasulova & Otterson, 2022). One study by Kim et al. (2021) investigated the impact of implementing a self-regulated strategy development (SRSD) program on Korean EFL learners. The findings revealed that the SRSD program significantly boosted students’ writing performance and belief in their abilities while increasing their employment of self-regulatory techniques in writing tasks. It was also observed that individuals who participated in the SRSD program exhibited an appreciable improvement in fluency and overall quality compared to those who didn’t participate.

In a recent investigation by Alanazi (2020), the effect of self-regulated learning strategies on Saudi EFL students’ writing abilities was studied. The findings indicated that utilizing such tactics, including goal-setting, self-evaluation, and reflection, significantly positively impacted both writing skills and student engagement levels. Moreover, Palalas & Wark (2020) conducted a systematic review that substantiated these results and emphasized the importance of additional research to assess the effectiveness of various self-regulatory techniques on different EFL populations in varying contexts. Specifically, planning, monitoring, and evaluating were found to improve students’ motivation toward writing tasks while boosting their overall perceived efficacy in mastering successful written communication.

Criticisms of Using Automated Feedback to Improve EFL Students’ Self-regulated Writing Strategies

While the use of automated feedback in improving EFL students’ self-regulated writing strategies has been identified as a promising approach, criticisms of this method need to be considered. One of the primary criticisms of automated feedback is its limited ability to provide personalized feedback that addresses the unique needs of each student. According to Lv et al. (2021), this limitation can lead to students receiving feedback not tailored to their specific writing issues, ultimately hindering their development as writers.

Additionally, Koltovskaia (2020) argues that overreliance on automated feedback can result in students becoming less engaged in the writing process and less willing to seek additional feedback from teachers or peers. Moreover, according to Cheng et al. (2021), automated feedback raises concerns about the ethical implications of technology in writing instruction. Recent research has highlighted the potential bias and lack of transparency in automated feedback systems, which can disproportionately affect certain groups of students, particularly those from non-native English-speaking backgrounds.

In conclusion, this literature review examined the research on using automated feedback to improve EFL students’ self-regulated writing strategies. It can be seen that while automated feedback has shown promise in providing EFL students with instant feedback and helping them develop self-regulated writing strategies, it is important to consider the criticisms and limitations associated with this approach. Additionally, more research is needed to address the limitations of automated feedback tools and ensure their accuracy and effectiveness in EFL writing instruction.


Alanazi, M. H. (2020). The predictive effects of self-regulated writing strategies on writing performance of Saudi EFL university students. Journal of Educational and Psychological Studies [JEPS]14(4), 668-678.

Han, T., & Sari, E. (2022). An investigation on the use of automated feedback in Turkish EFL students’ writing classes. Computer Assisted Language Learning, 1-24.

Huang, S., & Renandya, W. A. (2020). Exploring the integration of automated feedback among lower-proficiency EFL learners. Innovation in language learning and teaching14(1), 15-26.

Kim, D., Jo, I. H., Song, D., Zheng, H., Li, J., Zhu, J., & Xu, Z. (2021). Self-regulated learning strategies and student video engagement trajectory in a video-based asynchronous online course: a Bayesian latent growth modeling approach. Asia Pacific Education Review22(2), 305-317.

Koltovskaia, S. (2020). Student engagement with automated written corrective feedback (AWCF) provided by Grammarly: A multiple case study. Assessing Writing44, 100450.

Lee, S. M. (2020). The impact of using machine translation on EFL students’ writing. Computer-assisted language learning33(3), 157-175.

Lv, X., Ren, W., & Xie, Y. (2021). The effects of online feedback on ESL/EFL writing: a meta-analysis. The Asia-Pacific Education Researcher30(6), 643-653.

Palalas, A., & Wark, N. (2020). The relationship between mobile learning and self-regulated learning: A systematic review. Australasian Journal of Educational Technology36(4), 151-172.

Rasulova, M., & Ottoson, K. (2022). The Impact of Learner Agency and Self-Regulated Learning in EFL Classes. International Journal of Social Science and Human Research712.

Tran, T. M. L., & Nguyen, T. T. H. (2021). The impacts of technology-based communication on EFL students’ writing. AsiaCALL Online Journal12(5), 54-76.


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