Calibrated Generative AI as Meta-Reviewer: A Systemic Functional Linguistics Discourse Analysis of Reviews of Peer Reviews
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908545854734336 |
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| author | Zapata, Gabriela C. Cope, Bill Kalantzis, Mary Searsmith, Duane |
| author_facet | Zapata, Gabriela C. Cope, Bill Kalantzis, Mary Searsmith, Duane |
| contents | This study investigates the use of generative AI to support formative assessment through machine generated reviews of peer reviews in graduate online courses in a public university in the United States. Drawing on Systemic Functional Linguistics and Appraisal Theory, we analyzed 120 metareviews 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 metafeedback has the potential to scaffold feedback literacy and enhance leaner engagement with peer review. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15035 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Calibrated Generative AI as Meta-Reviewer: A Systemic Functional Linguistics Discourse Analysis of Reviews of Peer Reviews Zapata, Gabriela C. Cope, Bill Kalantzis, Mary Searsmith, Duane Artificial Intelligence Human-Computer Interaction This study investigates the use of generative AI to support formative assessment through machine generated reviews of peer reviews in graduate online courses in a public university in the United States. Drawing on Systemic Functional Linguistics and Appraisal Theory, we analyzed 120 metareviews 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 metafeedback has the potential to scaffold feedback literacy and enhance leaner engagement with peer review. |
| title | Calibrated Generative AI as Meta-Reviewer: A Systemic Functional Linguistics Discourse Analysis of Reviews of Peer Reviews |
| topic | Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.15035 |