AI feedback literacy in higher education: understanding, measuring, and predicting student feedback uptake

As generative AI tools become increasingly integrated into higher education, understanding how students engage with AI-generated feedback is essential for rethinking and redesigning English writing practices. This study introduces AI feedback literacy (AIFL) to describe EFL learners’ ability to interpret, evaluate, and apply automated feedback effectively. Drawing on feedback literacy theory and motivational frameworks, such as self-determination and cost-value models, a psychometric scale was developed and validated with data from 486 university students. Analyses using confirmatory factor analysis, mediation modelling, and multiple regression revealed that AIFL significantly predicts students’ uptake of AI-generated feedback, both directly and indirectly through motivational appraisals. Behavioural engagement emerged as a stronger predictor than attitudinal disposition, emphasising the importance of situated practice. AIFL was shaped not by demographic background but by frequency of AI use, raising equity concerns about access and opportunity. Overall, the findings suggest that meaningful engagement with AI feedback depends less on technological access and more on learners’ readiness, confidence, and experience, offering implications for designing assessment environments that promote feedback literacy, and for equipping teachers to mediate AI tools effectively.