Unravelling the relationship between self-efficacy and self-regulated strategies in GenAI-assisted writing of postgraduates: the mediating role of feedback engagement

Abstract

As generative artificial intelligence (GenAI) becomes increasingly embedded in academic writing, understanding how L2 writers engage with AI-generated feedback and regulate their writing processes is critical. While prior research has focused on learners’ perceptions of GenAI and writing outcomes, little is known about the processes through which learners’ beliefs are enacted in GenAI-assisted writing contexts. Drawing on social cognitive theory and self-regulated learning (SRL) frameworks, this study proposes a process-oriented mediation model in which engagement with GenAI feedback links GenAI writing self-efficacy and writing SRL strategies. Survey data were collected from 564 Chinese non-English-major postgraduate students using GenAI for English academic writing. Structural equation modeling revealed that GenAI writing self-efficacy significantly predicted feedback engagement, which in turn predicted writing SRL strategies. The direct relationship between self-efficacy and SRL strategies became non-significant when engagement was included, indicating full mediation, except for the direct significant relationship between self-efficacy and cognitive strategies. The findings position feedback engagement as a central self-regulatory mechanism in GenAI-assisted writing.