Developing teacher feedback literacy in AI educational context—an online professional training program

Teacher feedback literacy is defined as the knowledge, expertise, and dispositions to design feedback processes in ways which enable student uptake of feedback and seed the development of student feedback literacy. Teacher feedback literacy should develop in tandem with student feedback literacy. Although conceptual and empirical studies have steadily enriched the literature, research on how teacher feedback literacy can be developed, particularly in the context of emerging AI technologies in higher education, remains limited. Drawing upon a professional training program intervention, this study explores its development in dimensions of AI feedback design, relational concerns, and capacities for addressing pragmatic constraints. The 16-week intervention involved 12 university teachers in the English discipline who participated in four thematically structured sessions. These four sessions included 1) student feedback literacy, 2) peer feedback and self-feedback, 3). feedback and emotions, 4). frameworks of teacher feedback literacy. In these online workshops, teachers and the researcher discussed and negotiated how AI can be embedded in diverse feedback contexts and their theoretical foundations. Mix methods were employed in this study. Data were collected through semi-structured interviews with participants, self-reflection forms and student surveys and interviews. Qualitative data were analyzed using reflective thematic analysis to trace the development trajectories of teacher feedback literacy. Quantitative data on student feedback literacy development were used to triangulate the qualitative findings on teacher feedback literacy development. Our findings revealed that teachers shifted their conceptualization of feedback from a transmissive approach to a learner-centered approach, and became aware of the emotional concerns in feedback processes. Importantly, they also adapted how AI can be integrated into their feedback design to address pragmatic constraints. This study contributes insights into how professional training can shift teachers’ feedback epistemologies, equip them with relational and design skills, and open up innovative use of AI to address real-world constraints, ultimately benefiting students’ feedback engagement and lifelong learning.