Students Engagement with Deepseek-generated Feedback in EFL Writing Classes
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Research indicates that interaction with automated writing evaluation (AWE) systems can positively contribute to writing skill development. Compared to AWE systems, AI technologies demonstrate greater potential for providing comprehensive writing feedback. Researchers have called for investigations into how students interact with AI-generated feedback. This study, therefore, aims to examine the feature of DeepSeek’s feedback, students’ behavioral, cognitive and affective engagement with this AI-powered chabot. Adopting a mixed-methods approach, the study involved 103 third-year English majors (CEFR B1-B2 level) at a Chinese university who used DeepSeek to revise their essays. The features of DeepSeek's feedback were analyzed based on students' self-reflective reports. Behavioral engagement was assessed by comparing initial drafts with revised versions, supplemented by self-reflective reports. Cognitive engagement was examined through self-reflective reports, while affective engagement was investigated via semi-structured interviews. Findings revealed that DeepSeek provides effective feedback based on different genres. Behavioral engagement analysis showed students’ successful implementation of lower-order feedback, particularly in sentence structure, verb tense, and lexical choice. Although students generally noticed and comprehended most of the feedback, they showed critical thinking ability to question the inaccurate feedback. Affective engagement revealed high satisfaction, with students favoring DeepSeek for word choice. These findings provide valuable insights for EFL educators seeking to effectively integrate AI writing assistants such as DeepSeek into their pedagogical practices.
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