How EFL postgraduate students engage with automated written corrective feedback provided by Pigai in their academic writing?

Much research has investigated learners’ engagement with automated written corrective feedback (AWCF). However, few have employed eye-tracking technology, which captures real-time cognitive processes during reading, and even fewer have targeted postgraduates. This case study investigated how two English postgraduates used Pigai’s AWCF to revise their academic writing during the final draft of their doctoral research proposal. A mixed approach based on the established framework was used to collect and analyze data in behavioral, cognitive, and affective dimensions. Eye-tracking technology was utilized to triangulate cognitive engagement. The findings show that each student’s engagement with AWCF differed across three dimensions. Specifically, eye movement data provided objective evidence of cognitive engagement differences: whether their visual attention was guided by color-coded feedback severity. The difference was also reflected in their behavioral and affective engagement, with the student who read top-down but selectively, rather than by color-coded severity, demonstrating higher revision effort and trust. These differences, as well as the observed deviations between the tool’s processing mechanism and the conventions of academic writing, show several key implications: develop more genre adaptive automated writing evaluation (AWE) tools, critically evaluate the consistency of the tool and genre before adoption, distinguish writing guidance, cultivate balanced trust in AWCF, and integrate eye tracking technology for personalized learning.