Calibrated Generative AI as Meta-Reviewer: A Systemic Functional Linguistics Discourse Analysis of Reviews of Peer Reviews

This study investigates the use of generative AI to support formative assessment through machine-generated reviews of peer reviews (meta-feedback) in graduate online courses in a public university in the United States. Drawing on Systemic Functional Linguistics and Appraisal Theory, we analyzed 120 meta-reviews to explore how generative AI feedback constructs meaning across ideational, interpersonal, and textual dimensions. The findings suggest that generative AI can approximate key rhetorical and relational features of effective human feedback, offering directive clarity while also maintaining a supportive stance. The reviews analyzed demonstrated a balance of praise and constructive critique, alignment with rubric expectations, and structured staging that foregrounded student agency. By modeling these qualities, AI meta-feedback has the potential to scaffold feedback literacy and enhance leaners’ engagement with peer review. We conclude that calibrated AI, when embedded in thoughtfully designed workflows, can contribute to hybrid human–AI feedback ecosystems, while also requiring critical attention to its limitations and contextual adaptation in educational practice.