Promoting student engagement with GenAI feedback through self-regulatory scaffolding: evidence from an intervention study

Purpose Given the importance of engagement in feedback processes and the potential of self-regulation in fostering engagement, this study aimed to examine the role of self-regulatory scaffolding in supporting L2 learners’ engagement with GenAI feedback.Design/methodology/approach A quasi-experimental mixed-methods design was employed, with an experimental group receiving an eight-week, three-phase intervention and a control group receiving no such instruction. Quantitative data were collected through a questionnaire, whereas qualitative data were collected through students’ chat logs with the GenAI tool, revision task documents, reflections, and semi-structured interviews.Findings Quantitative results showed a significant multivariate group difference in post-intervention feedback engagement, with significant differences in the cognitive and behavioural dimensions, while no significant group differences emerged in emotional or ethical engagement after Bonferroni correction. The qualitative findings showed that, cognitively, students in the intervention condition employed various cognitive strategies and follow-up prompts to process feedback and demonstrated emerging GenAI-aware evaluative judgement. Behaviourally, they strategically crafted prompts to request targeted feedback and achieved a relatively high feedback uptake rate. Affective responses were mixed, with students valuing the usefulness of GenAI feedback while remaining alert to its limitations. Ethically, some students expressed awareness of authorship and over-reliance, with a small number also questioning the potential cultural and linguistic biases in GenAI-generated feedback.Originality/value This study shifts the focus from examining learner feedback engagement patterns to exploring instructional approaches to support feedback engagement in GenAI-mediated contexts. The findings provide pedagogical insights into instructional approaches to support learner engagement with feedback in an increasingly GenAI-driven feedback landscape.