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LIU Yuwei, HUANG Jingwen, ZENG Xiaofeng, LIN Yumin, LIU Binlu, CHEN Jitong
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DOI:10.17265/2161-623X/2026.10.003
Zhaoqing University, Zhaoqing, China
This study examined cognitive, behavioural, and affective engagement with DeepSeek-generated feedback among six Chinese university EFL learners at different proficiency levels across two argumentative writing-revision tasks. Data included learners’ first and second drafts, DeepSeek feedback points and interaction records, screen recordings, post-task verbal reports, semi-structured interviews, and a supplementary questionnaire. Qualitative content analysis was supplemented by descriptive statistics on feedback uptake and self-reported revision strategies. The findings revealed variation in learners’ understanding, evaluation and emotional responses to DeepSeek’s feedback. Cognitively, learners understood most of the feedback points but applied different criteria when deciding whether to adopt feedback or not. Behaviourally, 251 of 272 feedback points were incorporated into revised drafts (92.3%), with much feedback partially adopted or adapted. Learners also reported using dictionaries, follow-up inquiries with DeepSeek, online searching, and proofreading. Affectively, participants generally reported positive attitudes and confidence, although confusion or irritation arose when feedback was unclear or considered inappropriate. Engagement appeared to vary with learners’ self-perceived linguistic competence, their writing goals, previous task experiences, and perceptions of feedback quality. The findings suggest that teachers should help learners assess whether AI suggestions match their intended meaning, verify uncertain feedback, and adapt suggestions to learners’ writing goals and linguistic abilities during L2 writing revision.
learner engagement, DeepSeek-generated feedback, L2 writing and revision, generative artificial intelligence, different English proficiency levels
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