Calibrated Generative AI as Meta-Reviewer: A Systemic Functional Linguistics Discourse Analysis of Reviews of Peer Reviews
Authors: Gabriela C. Zapata, Bill Cope, Mary Kalantzis, Duane Searsmith
Organizations: School of Education, University of Nottingham, Nottingham, United Kingdom · Department of Education Policy, Organization & Leadership, University of Illinois, Urbana-Champaign and Chicago, United States
This study investigates the use of generative AI to support formative assessment through machine-generated reviews of peer reviews in graduate online courses at a public university in the United States. The analysis draws on Systemic Functional Linguistics and Appraisal Theory to examine how 120 metareviews construct meaning across ideational and interpersonal dimensions as well as the textual dimension. The findings suggest that generative AI can approximate key rhetorical and relational features of effective human feedback by providing clear direction while maintaining a supportive stance. The reviews demonstrated a balance between praise and constructive critique alongside alignment with rubric expectations and structured staging that foregrounded student agency. By modeling these qualities AI metafeedback has the potential to scaffold feedback literacy and enhance learner engagement with peer review. We conclude that carefully calibrated AI can contribute to hybrid human AI feedback ecosystems when embedded in thoughtfully designed workflows. However its effective integration into educational practice requires critical attention to its limitations and adaptation to specific educational contexts.