Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: Sustainable L2 writing pedagogy in Turkish higher education: Effects of AI-mediated feedback on self-regulated learning and writing performance

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education, Adult & professional learning. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

In response to growing demands for sustainable, scalable, and learner-centered feedback practices in higher education, this study examined the effects of different feedback modalities on Foreign Language (FL) learners’ self-regulated learning (SRL) strategies and writing performance within the Turkish university context. Guided by self-regulated learning theory and national digital transformation priorities, a quasi-experimental design was employed with three groups: an Automated Feedback (AF) group using Grammarly, a Generative AI Feedback (GenAI-F) group using ChatGPT, and an Instructor-Mediated Feedback (IMF) group without automated feedback. Eighty-four preparatory school students participated in a ten-week intervention involving multiple argumentative writing tasks, pre- and post-writing assessments, and a validated SRL strategy questionnaire. Mixed factorial ANOVAs revealed significant main effects of time on cognitive and metacognitive SRL strategies, alongside significant interaction effects between feedback type and time for idea planning and interest enhancement. The GenAI-F group demonstrated the largest gains in metacognitive and motivational regulation strategies and achieved significantly higher improvements in overall writing performance and content quality compared with the AF and IMF groups. Moderation analyses further indicated that CEFR-based proficiency levels shaped feedback effectiveness, with GenAI-F yielding stronger benefits for intermediate-level learners. These findings suggest that generative AI-mediated feedback can support sustainable EFL writing instruction by fostering autonomous learning capacities, with potential implications for reducing instructor workload through scalable feedback support, although this dimension was not directly measured in the present study, and promoting lifelong learning skills aligned with higher education sustainability agendas.

What kind of evidence is this?

Supporting AI learning science · Comparative / pre-post study reported

Stage: Higher education; Adult & professional learning. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

The study was conducted in a credit-bearing compulsory English course offered to preparatory school undergraduate students from non-English majors at a public state university in Türkiye. The course aimed to develop students’ integrated academic English skills, including reading, listening, speaking, and writing, in alignment with the learning outcomes specified by the Council of Higher Education (YÖK). Instruction was delivered in two 90-minute sessions per week over a 14-week semester.

Limitations and uncertainty · source passage

To ensure ethical and pedagogically appropriate use of AI tools, students in both experimental groups participated in structured 20-minute training sessions prior to each writing task. The training focused on understanding the scope and limitations of AI-mediated feedback, generating rubric-aligned prompts, critically evaluating feedback suggestions, and integrating feedback selectively. Sample prompts were provided, and students were encouraged to adapt them as needed. To ensure equity, students in the IMF group were granted access to the same training materials and AI tools after the completion of the study. To minimize potential methodological bias arising from the dual role of generative AI as both an instructional tool and an assessment aid, the scoring process was strictly standardized through rubric-based prompts, and human validation procedures were incorporated. In addition, scoring was conducted independently of the instructional phase, and no iterative feedback loops between scoring outputs and student revisions were allowed.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
  • Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Ahmet Erkam Selvi; Aydan Irgatoğlu; Oğuz Yurttadur; Gürkan Dağbaşı. Sustainable L2 writing pedagogy in Turkish higher education: Effects of AI-mediated feedback on self-regulated learning and writing performance. PLOS One (2026-07-14). https://doi.org/10.1371/journal.pone.0344618

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.