Investigating students’ engagement with GenAI-supported peer feedback generation in EFL writing
Year: 2026
Type: article
Abstract: Adopting an exploratory mixed-methods design, this study investigates students’ behavioural, affective, and cognitive engagement with GenAI-supported peer feedback generation in English as a foreign language (EFL) writing. Forty-eight Chinese undergraduate students participated in peer review tasks using a ChatGPT-powered chatbot to assess their comments on peer essays and offer improvement suggestions. The data comprised chat logs, screen recordings, written reflections, and survey responses. The findings revealed a prevalent tendency among students to incorporate GenAI feedback into their comment revisions, with approximately 62% of the GenAI suggestions being implemented by students to enhance their comment drafts. They exhibited five cognitive strategies, including (1) focusing on improvement areas, (2) contextualizing, (3) critically assessing, (4) selectively incorporating GenAI suggestions, and (5) continually seeking GenAI feedback on revisions. The participants reported a generally positive attitude toward using GenAI support, perceiving the experience as useful and enjoyable. These findings highlight that integrating GenAI into the feedback process fosters an engaging and reflective learning environment. This study contributes to the theoretical understanding of AI literacy by contextualizing it within EFL writing, offering insights for educators seeking to incorporate GenAI into assessment practices.
Authors: Kai Guo, Danling Li, Mengru Pan, Chun Lai
Language: English
Subfield: Language and Linguistics
Field: Arts and Humanities
Domain: Social Sciences
SDG: Quality education
Open Access status: hybrid
Award: G2621502187
OpenAlex ID: W7165043081