🧠 AI Ed Wiki

Peer review — the practice in which students read, evaluate, and provide feedback on one another's work, most often writing. In writing pedagogy, peer review is a long-standing best practice: students learn both from receiving feedback and from providing criteria-based feedback to others, and interactions with peers about their writing correlate with deeper learning, audience awareness, and personal and social development. In the AI era, peer review is being re-examined as a human complement to AI-generated feedback, with models such as PAIRR pairing the two.

Peer review is valued because it gives students an authentic audience, develops their evaluative judgment through criteria-based responding, and builds the social and relational context that supports engagement and motivation. However, its quality depends heavily on scaffolding — how well it is structured, and whether students are given clear criteria and training. This is precisely where AI feedback is positioned as a complement: AI can provide consistent, rubric-driven, actionable feedback on organization, focus, and structure, while peers offer specific, context-aware feedback rooted in their shared understanding of the assignment, an authentic audience, and emotional support.

How peer review appears in the research

  • Peer + AI feedback (PAIRR): The PAIRR model combines peer review with AI review in a human-centered process, finding students value the similarity of the two (as reassurance) and their complementarity (AI's broad, rubric-driven feedback vs. peers' specific, contextual feedback), while critically assessing AI outputs builds AI Literacy and writerly agency.
  • AI peer feedback systems: AI peer feedback research examines how AI tools support or mediate peer-feedback workflows, and how their design affects the quality of feedback students give and receive.
  • Authentic assessment and collaboration: Peer review features in authentic assessment redesign and collaborative learning contexts, where it supports Self Regulated Learning and the metacognitive development of students' evaluative judgments.
  • Learning through giving feedback: Research consistently shows students learn from providing criteria-based feedback (evaluative-judgment development), an insight that informs how AI-supported peer review is designed to preserve this learning.
  • Peer review vs. AI feedback

    A key finding in the wiki's research is that AI and peer feedback are best understood as complementary rather than substitutable. PAIRR research found only 6% of students preferred AI feedback alone, while 58% preferred combined feedback — AI utility is experienced in the context of human feedback. Peers bring contextual knowledge, authentic audience awareness, and human connection that AI lacks; AI brings consistency, immediacy, and actionable rubric-driven revision strategies. The quality of both depends on scaffolding, and the emphasis on reflection — students articulating why they accept or reject feedback — is what builds transferable writing knowledge and agency.

    Connections to related concepts

    Peer review connects to Writing Education and Formative Assessment as a core instructional practice, and to AI Feedback Quality and AI Literacy as it evolves in the AI era. It supports Self Regulated Learning and Metacognition through students' reflection on feedback, and relates to Student Experience and Collaborative Learning through the social and relational context it creates. Its role in giving and evaluating feedback ties to evaluative judgment and to Academic Integrity as institutions re-examine assessment in the presence of generative AI.

    Connected Concepts

  • Writing Education
  • Formative Assessment
  • AI Feedback Quality
  • AI Literacy
  • Self Regulated Learning
  • Metacognition
  • Student Experience
  • Collaborative Learning
  • Academic Integrity
  • Connected Articles

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  • GenAI Linguistic Diversity Academic Writing — Generative AI and Linguistic Diversity in Academic Writing