Exploring teachers’ dispositions toward AI feedback accuracy and the refinement process and their enhancement practices in EFL writing

Abstract

AI chatbots are increasingly used as writing aids and feedback providers; however, their classroom implementation remains limited due to inconsistent feedback accuracy, highlighting the need for teachers to act as feedback curators who refine and contextualize AI-generated feedback. However, no study has systematically examined the scope, nature, and focal areas of teachers’ refinements of AI-generated feedback. The present study documents teacher interventions using color-coded annotations and structured review forms, examines teachers’ evaluations of feedback accuracy through accuracy ratings, and explores their perceptions of classroom usability through interviews. The findings indicated that AI frequently overlooked meaning-level problems and instead focused on surface-level errors; in densely errored papers, major meaning-related issues often remained unresolved. While error-coded feedback was dominated by surface-level corrections, neglecting clarity and structural concerns, content feedback showed limited ability to detect repetitive ideas and organizational flaws, which were readily identified by teachers. Consequently, teachers described the filtering process of error-coded feedback as burdensome, whereas content feedback was perceived as more useful and easier to enhance through teacher-led prioritization adjustments addressing students’ actual needs. The findings highlighted the key areas of teacher refinements and offer insights for improving the accuracy and classroom applicability of AI-generated feedback through teacher prioritization strategies.