AI-Driven Feedback for University Students’ ESL/EFL Academic Writing: A Scoping Review of Pedagogical Implementation and Learning Outcomes

Abstract

This review maps the recent findings on the AI-driven feedback given to undergraduate-level ESL/EFL academic writing, examining how it is implemented, what learning outcomes it produces, and what shapes those outcomes. Drawing on 39 peer-reviewed empirical studies retrieved from Web of Science, Scopus, and ERIC, and following the PRISMA extension for Scoping Reviews, this review covers 2,796 participants across 18 countries and regions. The findings show that 27 of the 39 studies in the full corpus reported measurable improvement in ESL/EFL writing. Lower-proficiency students showed larger surface-level gains but higher rates of passive uptake, while higher-proficiency learners engaged more critically when appropriately scaffolded. This review also identifies a persistent local-global revision gap: grammar and vocabulary improvements were reliably documented, but gains in argumentation, genre awareness, and critical reasoning remained weak and variable across the corpus. While AI-driven feedback shows genuine promise for addressing the feedback deficit in large-scale ESL/EFL writing instruction — a challenge with direct relevance to SDG 4 (Quality Education) and its advocation for inclusive, equitable access to quality learning at all levels - the conditions that produce durable learning gains remain poorly understood. The field's methodological development has not yet caught up with its empirical output.