Student perceptions of hybrid feedback: Using Gen-AI to enhance engagement with EAP writing feedback
Jie Zhang, Jiashi Wang · The JALT CALL Journal · 2025
Feedback is crucial in the learning process, yet many students struggle to effectively engage with teacher feedback. This study explores the potential of Generative Artificial Intelligence (Gen-AI) to enhance student engagement in EAP writing feedback. Using a mixed-methods approach that includes surveys and interviews with Year 1 and Year 2 students at a Sino-foreign EMI university, the research examines students’ cognitive, behavioral, and affective engagement with feedback, focusing on how Gen-AI tools like ChatGPT can help clarify and implement feedback. The findings suggest that integrating Gen-AI is perceived as effective by students, with improvements observed in cognitive, behavioral, and affective engagement, though behavioral engagement was slightly less pronounced. Year 1 students generally showed a more positive perception of Gen-AI’s effectiveness, while both groups demonstrated distinct approaches and perceptions in using the tool. While the study highlights the potential benefits, it also identifies a range of concerns, indicating that a comprehensive approach is needed to address challenges related to integrating Gen-AI in feedback processes. This research provides timely insights into integrating Gen-AI into feedback processes to foster student engagement and enhance writing outcomes.