用AI分析同学互评，帮研究生提升反馈能力。
Calibrated Generative AI as Meta-Reviewer: A Systemic Functional Linguistics Discourse Analysis of Reviews of Peer Reviews
- 基于系统功能语言学分析AI生成的元评论
- AI反馈兼具指导性与支持性，平衡表扬与建议
- 适合想提升互评能力的研究生和教师
本研究探讨生成式AI在美国内地公立大学研究生在线课程中，通过机器生成对同伴评审的元评审来支持形成性评估的应用。基于系统功能语言学与评价理论，分析了120份元评审，探究AI反馈在概念、人际与语篇维度上如何构建意义。结果表明，生成式AI可近似体现有效人类反馈的关键修辞与关系特征，在提供明确指导的同时保持支持性立场。分析显示，这些反馈在表扬与建设性批评间取得平衡，符合评分标准，且结构化设计突出学生主体性。通过模仿这些特质，AI元反馈有望促进反馈素养发展，提升学习者对同伴评审的参与度。
原文摘要 · Abstract (English)
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.
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