-->PONE-D-26-07893-->-->Sustainable L2 Writing Pedagogy in Turkish Higher Education: Effects of AI-Mediated Feedback on Self-Regulated Learning and Writing Performance-->-->PLOS One

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-->Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #2: Yes

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-->2. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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-->5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #1: This manuscript reports on a quasi-experimental study investigating the effects of automated feedback (Grammarly), generative AI feedback (ChatGPT), and instructor-mediated feedback on Turkish EFL learners' SRL strategies and writing performance. The study is methodologically sound, the analysis is thorough, and the findings are theoretically and pedagogically significant. However, several areas require clarification and revision to enhance the manuscript's clarity, transparency, and overall impact.

Major Comments

1. Operationalization of the "Instructor-Mediated Feedback" (IMF) Condition

The Issue: The description of the IMF group is the weakest point in the methodology. It is currently defined only by what it is not ("without automated feedback"). For the findings to be interpretable, readers need a clear, detailed description of what this control condition did receive.

Suggested Revision: Please provide a detailed account of the IMF condition. Crucially, what kind of feedback did these students receive from the instructor? Was it holistic scores only (as implied on Page 12, line 136), written marginal comments, a general rubric, or whole-class oral feedback? How often did they receive it? Without this, it's impossible to know if the observed effects are due to the presence of AI or the absence of meaningful, individualized instructor feedback. This is critical for the study's internal validity and for positioning the findings in the broader literature. Check this study: A systematic review of ChatGPT for English as a foreign language writing: Opportunities, challenges, and recommendations.

2. Confirmation of the "No AI-Mediated Feedback" Condition

The Issue: The study took place over a 10-week intervention in a real-world university setting. It is highly plausible that students in the IMF group, despite being instructed not to, may have independently used freely available AI tools like Grammarly or ChatGPT for their writing tasks.

Suggested Revision: How did the researchers ensure or control for this threat to internal validity? Was any self-report data collected post-intervention to check for this? A brief mention of how this potential confound was addressed (e.g., "Students in all groups were asked to report any additional tools used, and a post-hoc check revealed no significant cross-contamination") would significantly strengthen the study's claims.

3. Literature understanding of metacognition and regulation. Check the following articles how to improve self-regualtion in AI. 1. Understanding EFL student writers’ metacognitive awareness in utilizing ChatGPT. 2. Metacognitive Awareness and EFL Learners' Writing Performance in ChatGPT‐Assisted Writing Context. 3. Metacognitive awareness and EFL learners' perceptions and experiences in utilising ChatGPT for writing feedback

3. The Sustainability Argument

The Issue: The paper's framing around "sustainable" pedagogy is a key strength. However, the argument is currently focused on learner sustainability (autonomy, lifelong learning). While important, sustainability in higher education also encompasses instructor and institutional workload.

Suggested Revision: The manuscript could be significantly strengthened by explicitly discussing the instructor workload implications. The abstract and conclusion mention "reducing instructor workload," but the data doesn't directly support this claim. The discussion (Page 29, line 450) mentions scalability "without increasing instructor workload." Consider addressing this more concretely. For example: Did the instructor spend less time on the GenAI-F group? Could the freed-up time be reallocated to higher-value pedagogical activities? Adding a note about this as a crucial area for future research would ground the sustainability argument in a more practical, institutional reality.

4. Clarification of Proficiency Moderation Analysis

The Issue: The moderation analysis is a valuable part of the study. However, the description of how CEFR levels were assigned is missing from the Methods section.

Reviewer #2: I commend the authors’ effort in conducting this interesting study. The research examines the effects of automated, generative AI, and instructor-mediated feedback on L2 learners’ self-regulated learning strategies and writing performance, and reports differential gains across feedback types. However, I have some concerns regarding the conceptual framing, methodological alignment, and analytical approach.

Before proceeding to read my comments, the author should know that comments do not hold authoritative status. Therefore, they submit constructive rebuttals in instances where they disagreed, of course, with strong justifications.

The authors claimed that the capacity of automated feedback "to address higher-order such as content development, organization, and argumentation remains limited." I am not quite sure if this is correct. Since the emergence of GenAI tools, many studies have been conducted, including automated corrective feedback, and many more. Please see a recent study by Luo and Yusuf (2025), Alharbi (2023), and related ones. You may wish to acknowledge the presence of these, but critically discuss what they lack that you want to advance.

Ref:

Luo, L., & Yusuf, A. (2025). Bridging AI and pedagogy: how AI-adaptive feedback shapes Chinese EFL students’ writing engagement, metacognitive writing strategies, and writing performance. Assessment & Evaluation in Higher Education, 1–24. https://doi.org/10.1080/02602938.2025.2548919

Alharbi, W. 2023. “AI in the Foreign Language Classroom: A Pedagogical Overview of Automated Writing Assistance Tools.” Education Research International 2023: 1–15. https://doi.org/10.1155/2023/4253331.

In the introduction section, the author clearly highlights how AF and GenAI-F can enhance L2 writing. They further critique AF, arguing that it relies on predefined algorithms and therefore cannot address higher-order concerns. Building on this, they explain that GenAI-F can support ideation. With this, the distinction between what both can do is already clear. My question, then, is: why do Research Questions 1 and 2 matter if we already know this difference?

In fact, the claim that AF cannot address higher-order concerns appears somewhat exaggerated. A recent meta-analysis (see Fleckenstein et al.) reports a moderate overall effect (g = 0.55) of automated feedback on students’ writing performance, suggesting that its impact may extend beyond surface-level corrections. In addition, a study by Bellhäuser and colleagues found that AF enhances better average goal setting, planning, and adherence to time schedule, as well as higher self-efficacy and satisfaction.

Ref:

Bellhäuser H, Dignath C and Theobald M (2023). Daily automated feedback enhances self-regulated learning: a longitudinal randomized field experiment. Front. Psychol. 14:1125873. doi: 10.3389/fpsyg.2023.1125873

Fleckenstein J, Liebenow LW, Meyer J. Automated feedback and writing: a multi-level meta-analysis of effects on students' performance. Front Artif Intell. 2023 Jul 3;6:1162454. doi: 10.3389/frai.2023.1162454.

I recommend that the authors clearly show what this study is contributing to the literature, given that many studies have found enhanced writing performance and self-regulated learning.

There appears to be a discrepancy in the study timeline. The manuscript states that instruction was delivered over a 14-week semester, yet data were collected over 10 weeks. It is unclear how these two timeframes align. The authors should clarify whether data collection covered only a portion of the instructional period and specify the exact timing of measurements.

The description of the writing task is somewhat limited. It is unclear whether the task involved integrated writing (e.g., based on source texts) or independent writing. In addition, the essay prompt or topic should be provided for transparency, as this is essential for understanding task demands and interpreting the findings.

The methodology section is quite dense, which affects readability. I recommend structuring into subsections such as design and participants, research context, instruments, procedure, and data analysis.

The use of ChatGPT-4 for initial essay scoring raises some concerns. While the L2 Composition Profile is an established rubric, ChatGPT itself is not a validated scoring instrument, and the majority of essays appear to have been rated solely by the AI system. Although interrater reliability with a human rater is reported for a subset of scripts, this may not be sufficient to ensure scoring validity across the full dataset. In addition, given that generative AI is also used as an intervention tool in the study, the potential for methodological bias should be more explicitly acknowledged and addressed.

The study does not provide sufficient evidence regarding treatment fidelity or actual engagement with the feedback tools. It remains unclear how consistently and effectively participants used Grammarly and ChatGPT, which may influence the observed effects.

The choice of analytical approach raises concerns about its alignment with the research questions. The use of a mixed-design ANOVA suggests an interest in changes over time and interaction effects. However, RQ1 and RQ2 are framed in terms of differences between feedback types, without explicitly referencing change over time. If the intention is to examine improvement, this should be made clearer in the research questions. Alternatively, if the focus is on post-test differences, MANCOVA or ANCOVA will be more appropriate to control for baseline performance.

In fact, the authors claimed that Bonferroni was computed to control inflated Type I error, but then went ahead to run separate ANOVAs instead of MANOVA or MANCOVA, as the case may be.

The manuscript reports Bonferroni-adjusted pairwise comparisons even in cases where the corresponding main or interaction effects are non-significant (see Table 3). This raises concerns about the appropriateness of these post hoc tests, as they are generally warranted only following significant omnibus effects.

The overall result section is too dense, and I often miss which analysis is addressing which research question. I recommend re-stating the research question or appropriate headings before presenting the corresponding results.

There appears to be an inconsistency in the analytical approach for RQ3. While earlier analyses incorporate time to capture development, the use of a two-way ANOVA here excludes the temporal dimension, despite the research question referring to writing development. Clarification is needed regarding whether development is operationalised as change over time or as post-test performance.

Some interpretations appear to extend beyond the statistical results, particularly where non-significant interaction effects are discussed as indicative of differential impact. Greater caution in interpreting non-significant findings is recommended.

Given these concerns, I will recommend a major revision.

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Reviewer #1: No

Reviewer #2: No

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Sustainable L2 Writing Pedagogy in Turkish Higher Education: Effects of AI-Mediated Feedback on Self-Regulated Learning and Writing Performance

PONE-D-26-07893R1

Dear Dr. IRGATOĞLU,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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-->Comments to the Author

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Reviewer #2: All comments have been addressed

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-->2. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #2: Yes

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-->3. Has the statistical analysis been performed appropriately and rigorously? -->

Reviewer #2: Yes

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The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #2: Yes

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-->5. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #2: Yes

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-->6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)-->

Reviewer #2: Thank you for addressing my comments. The manuscript has improved significantly and I recommend its acceptance.

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Reviewer #2: Yes:  Abdullahi Yusuf

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