This study aims to systematically synthesize empirical evidence on the effects of GenAI-mediated feedback on EFL writing, students’ uptake of such feedback, and the ethical issues associated with its use.
Following PRISMA, this review synthesized English-language, peer-reviewed studies published between 2023 and 2025. Searches were conducted in Scopus, ERIC and selected publisher platforms. Two reviewers independently screened records and extracted data using a piloted form, while risk of bias was assessed using RoB 2, ROBINS-I and JBI tools. Random-effects meta-analyses were conducted when at least three comparable effect sizes were available; otherwise, the evidence was synthesized narratively with harvest plots.
The review shows that GenAI-mediated feedback is most effective when implemented as a dialogic, multi-draft and criteria-aligned practice moderated by teachers. Students tend to adopt local corrections more readily than global revisions unless tasks explicitly require justification and verification. Human–AI agreement appears sufficient for low-stakes screening and formative diagnosis, but construct coverage remains uneven and subgroup differences emerge in some contexts, indicating the need for human oversight, transparent criteria and routine bias audits.
The evidence base remains fragmented across contexts, designs and reporting practices, which limits comparability and cumulative interpretation. Future research should standardize reporting, examine fairness across learner groups and evaluate the long-term pedagogical effects of GenAI-mediated feedback at scale.
EFL programs should develop prompt templates that elicit explanations and examples, incorporate verification routines, use public rubrics and exemplars and position teacher-curated co-feedback as a central component of implementation. Clear boundaries are also needed between formative and summative uses, together with disclosure requirements for AI-supported feedback.
This review provides a timely synthesis of recent evidence on GenAI-mediated feedback in EFL writing by integrating learning outcomes, uptake patterns and assessment fairness considerations within a single analytical framework.
