Semantic Response Mechanisms of L2 Writing Revision Behavior under Generative AI Feedback

Abstract

Generative AI is changing the way L2 writing feedback is produced and how learners revise their texts. Writing feedback is gradually moving from one way correction to human AI semantic interaction. Based on this change, the study focuses on non English major undergraduates in Chinese universities who have passed CET-4. It builds an experimental process that combines initial drafts, ChatGPT feedback, revised drafts, revision logs, and teacher ratings. Inputlog or Google Docs is used to record the revision process. SBERT is used to calculate semantic distance between initial drafts and revised drafts. Coh Metrix indicators, feedback uptake annotation, and mixed effects models are then used to examine how different feedback types affect writing quality improvement. The results show that grammar and vocabulary feedback are more easily accepted, but their explanatory power for score improvement is limited. Cohesion and argument feedback have lower uptake rates, yet they contribute more clearly to task response, paragraph organization, and coherence. The relationship between semantic distance and writing improvement is nonlinear. Moderate semantic change is most likely to produce effective revision. The findings indicate that the value of generative AI feedback does not depend on revision quantity. It depends on whether learners can make high quality semantic responses while preserving their original writing intentions.