Exploring High School Students’ Preferences for English Writing Feedback: Non-Native English Teacher vs. Native English Teacher vs. ChatGPT

This is the authors' abstract. We don't add key points for this paper.

This study investigates high school students’ preferences for English writing feedback from three sources: non-native English teachers, native English teachers, and generative AI (ChatGPT), exploring which source students prefer and identifying factors influencing these preferences. A mixed-methods approach was employed with 20 high school students from an English newspaper club in Korea. Participants received feedback from all three providers based on a standardized rubric. Data collection included semi-structured interviews and Likert-scale surveys. Qualitative data were analyzed thematic analysis using constant comparative method, while quantitative data were analyzed using descriptive statistics, Kruskal–Wallis tests and Dunn’s post hoc analysis. Students rated teacher–ChatGPT collaborative feedback highest, followed by native English teacher feedback, ChatGPT feedback (unaware of AI source), non-native English teacher feedback, and ChatGPT feedback (aware of AI source). Native teachers were valued for linguistic naturalness and cultural insights, non-native teachers for empathetic guidance and shared backgrounds, and ChatGPT for systematic and accessible feedback. Yet concerns emerged regarding AI’s limited contextual sensitivity, emotional depth, and data privacy. Findings highlight the potential of teacher–AI collaboration, suggesting that AI’s technical precision and teachers’ contextual expertise can complement each other. The study extends the concept of “more knowledgeable others” to AI and proposes hybrid feedback models that balance efficiency, reliability, and personalization in EFL contexts.