Generative AI as an Automated Written Corrective Feedback Provider in EFL Academic Writing: A Mixed-Methods Investigation of Accuracy Gains, Feedback Quality, and Learner Engagement

Abstract

The integration of generative artificial intelligence (GenAI) and large language models (LLMs) into language technology has reopened long-standing questions about the role of automated written corrective feedback (AWCF) in second-language (L2) writing development. While early enthusiasm has positioned conversational LLMs such as GPT-4 as scalable substitutes for the labour-intensive provision of teacher feedback, the empirical base remains fragmented, and few controlled studies examine accuracy outcomes, feedback quality, and learner engagement together. This study addresses that gap through a controlled investigation conducted in an under-represented South Asian English-as-a-foreign-language (EFL) setting. Adopting a sequential explanatory mixed-methods design, ninety undergraduate EFL learners were stratified and randomly assigned to three conditions—GPT-4 AWCF, expert teacher-written corrective feedback, and a self-revision control—across an eight-week, six-cycle argumentative writing programme. Quantitative strands comprised error-rate analysis, an analytic rubric, and a validated engagement questionnaire; qualitative strands comprised corpus coding of 1,860 feedback moves, stimulated-recall interviews, and reflective journals, integrated through a joint display. Analysis of covariance indicated that the GPT-4 group significantly outperformed the control on both accuracy and rubric scores, with gains statistically comparable to, and on local accuracy marginally exceeding, the teacher group, and with most gains retained at delayed post-testing. Feedback coding revealed that GPT-4 favoured metalinguistic explanation and reformulation but showed reduced precision on discourse-level concerns, where redundancy and occasional hallucinated corrections appeared. Engagement was high—particularly on the affective dimension—but cognitively demanding, and learners expressed concern about over-reliance and loss of authorial voice. The study contributes controlled, integrated evidence on the pedagogical value and limitations of LLM-based language technology and argues for a principled human-in-the-loop model of AI-assisted feedback rather than wholesale automation.