As artificial intelligence (AI) becomes increasingly integrated into educational contexts, understanding how learners engage with AI-generated feedback is critical. Although prior research has examined the effectiveness of automated writing evaluation, less attention has been given to the cognitive and affective processes through which learners interpret and respond to such feedback. This study employed a constructivist grounded theory approach to investigate how Chinese undergraduate EFL learners engaged with AI-supported feedback during L2 writing revision. Participants were 24 English-major undergraduates at a public university in China. Data were generated through six semi-structured small-group interviews and analyzed through iterative open, axial, and selective coding. The analysis identified three interrelated domains—cognitive engagement, contextual evaluation, and affective regulation—which were integrated into an interpretive cognitive–affective dual-path model. When feedback was perceived as transparent, manageable, meaning-preserving, and aligned with task demands, learners demonstrated stronger cognitive engagement, greater willingness to experiment with complex structures, and sustained motivation. In contrast, feedback perceived as opaque, overly evaluative, or misaligned with learner expectations was associated with negative affect, selective uptake, risk avoidance, and strategic simplification. Learner evaluative filtering appeared to shape how noticing developed into revision action. These findings provide a process-oriented account of learner engagement in AI-supported writing environments and suggest that the influence of AI-generated feedback is filtered through learners' evaluative judgement, contextual appraisal, and affective regulation.
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A cognitive-affective dual-path model of learner engagement in AI-supported writing feedback
Semantic Scholar · 2026
Abstract
As artificial intelligence (AI) becomes increasingly integrated into educational contexts, understanding how learners engage with AI-generated feedback is critical. Although prior research has examined the effectiveness of automated writing evaluation, less attention has been given to the cognitive and affective processes through which learners interpret and respond to such feedback. This study employed a constructivist grounded theory approach to investigate how Chinese undergraduate EFL learners engaged with AI-supported feedback during L2 writing revision. Participants were 24 English-major undergraduates at a public university in China. Data were generated through six semi-structured small-group interviews and analyzed through iterative open, axial, and selective coding. The analysis identified three interrelated domains—cognitive engagement, contextual evaluation, and affective regulation—which were integrated into an interpretive cognitive–affective dual-path model. When feedback was perceived as transparent, manageable, meaning-preserving, and aligned with task demands, learners demonstrated stronger cognitive engagement, greater willingness to experiment with complex structures, and sustained motivation. In contrast, feedback perceived as opaque, overly evaluative, or misaligned with learner expectations was associated with negative affect, selective uptake, risk avoidance, and strategic simplification. Learner evaluative filtering appeared to shape how noticing developed into revision action. These findings provide a process-oriented account of learner engagement in AI-supported writing environments and suggest that the influence of AI-generated feedback is filtered through learners' evaluative judgement, contextual appraisal, and affective regulation.