GenAI-mediated feedback for EFL writing: effects, student uptake and ethics: a systematic review

形成性评价 终结性评价 印为红字的 可比性 心理学 构造(python库) 医学教育 实证研究 经验证据 数学教育 计算机科学 匹配(统计) 检查表 应用心理学 组分(热力学) 社会心理学 循证实践 英语 考试(生物学)
作者
Urip Sulistiyo,Desti Angraini,Habib Maulana Fikri
出处
期刊: [Emerald Publishing Limited]
卷期号:: 1-16 被引量:2
标识
DOI:10.1108/lfet-04-2026-0052
摘要

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Originality/value 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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
竹枝词完成签到,获得积分20
刚刚
林橙发布了新的文献求助10
刚刚
Coco发布了新的文献求助10
1秒前
脑洞疼的应助被时刻保持质疑采纳,获得10
1秒前
2秒前
开心的秋完成签到,获得积分10
2秒前
2秒前
Rain发布了新的文献求助10
2秒前
可爱的函函的应助被daying采纳,获得10
3秒前
3秒前
ttchen的应助被xigualu采纳,获得10
4秒前
6666发布了新的文献求助10
4秒前
4秒前
5秒前
所所的应助被林橙采纳,获得10
6秒前
Mr_Cavan完成签到,获得积分10
6秒前
Lune7发布了新的文献求助10
6秒前
livesey完成签到 ,获得积分10
6秒前
小蘑菇的应助被keyandalao采纳,获得10
7秒前
Hello的应助被RUINNNO采纳,获得10
7秒前
Rain完成签到,获得积分10
7秒前
丰富青文完成签到 ,获得积分10
8秒前
chenwang发布了新的文献求助10
8秒前
思源的应助被wy采纳,获得10
9秒前
白羊完成签到,获得积分10
9秒前
9秒前
Yes完成签到,获得积分10
10秒前
小蘑菇的应助被汪博士采纳,获得30
10秒前
RADSUN完成签到 ,获得积分10
10秒前
张含静发布了新的文献求助20
10秒前
11秒前
11秒前
Eurus发布了新的文献求助10
11秒前
2992i发布了新的文献求助10
12秒前
13秒前
zzzssshhh完成签到,获得积分10
14秒前
香蕉觅云的应助被科研通管家采纳,获得10
14秒前
14秒前
orixero的应助被科研通管家采纳,获得20
14秒前
bkagyin的应助被科研通管家采纳,获得10
14秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
拉伸高分子熔体中缠结行为的模拟和小角中子散射研究 1080
Encyclopedia of Modern Artificial Intelligence 1000
Особенности технологий и методов информационно-психологических войн СССР с Великобританией и США в период 1939-1953 гг 1000
Analyses de reconnaissances de dettes passées devant les échevins d'Ypres (1249-1291) 600
Cellulose and Cellulose Derivatives: Synthesis, Modification and Applications 600
Experimentelle und numerische Modellierung des dynamischen Trag- und Verformungsverhaltens von hochflexiblen Schutzsystemen gegen Steinschlag 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 工程类 有机化学 化学工程 生物化学 物理 复合材料 内科学 催化作用 光电子学 人工智能 细胞生物学 无机化学 物理化学 数学
热门帖子
关注 科研通微信公众号,转发送积分 8150430
求助须知:如何正确求助?哪些是违规求助? 9547421
关于积分的说明 21681059
捐赠科研通 7737790
什么是DOI,文献DOI怎么找? 3424358
关于科研通互助平台的介绍 2549312
邀请新用户注册赠送积分活动 2467682