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Cited by 59Student Assessment and FeedbackArtificial Intelligence in EducationEFL/ESL Teaching and Learning

Peer and AI Review + Reflection (PAIRR): A human-centered approach to formative assessment

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Sperber, Lisa, MacArthur, Marit, Minnillo, Sophia, Stillman, Nicholas, Whithaus, Carl·2025·Computers and Composition
DOI 10.1016/j.compcom.2025.102921
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Abstract

Cycles of drafting and revising are crucial for student writers' growth, and formative assessment plays an important role. However, many teachers lack the time or resources to provide feedback on drafts. While research suggests that AI feedback is high enough quality to be used for draft feedback, especially when assignment-specific criteria are used (Steiss et al., 2024), it must be used in a human-centered process. AI has the potential to reduce educational equity gaps in writing support (Warschauer et al., 2023), but when narrowly implemented, technologies can deepen divides (Stornaiuolo, et al., 2023). Peer and AI Review + Reflection (PAIRR) combines the best practice of peer review with AI review in an approach that emphasizes student agency and reflection. Using a mixed methods approach, this study examined student perceptions of AI utility in the context of peer review. Results indicate that AI tools offer useful feedback when combined with peer review. Students found the similarity between AI and peer feedback reassuring, while also valuing their complementary perspectives. Moreover, by evaluating AI outputs, students developed AI literacy, gaining familiarity with AI feedback's affordances and limitations while learning ethical ways to use AI in their writing processes.

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  • Studies investigating Peer and AI Review+Reflection (PAIRR) frameworks revealed that students viewed AI-generated feedback as complementary rather than competitive to human peer review.

    “Studies investigating Peer and AI Review+Reflection (PAIRR) frameworks revealed that students viewed AI-generated feedback as complementary rather than competitive to human peer review (Sperber et al. 2025).”

    From Who Reviews the Reviewer? A Multi‐Agent LLM Architecture With Meta‐Review Synthesis for Editorial Peer Review (2026) · cites this paper as Sperber et al. 2025

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Research works printed but not linked: 4

  1. Bai, X., Wang, A. Sucholutsky, I. and Griffiths, T.L. (2024). Measuring implicit bias in explicitly unbiased large language models. arXiv. https://arxiv.org/abs/2402. 04105.
  2. Buck, A. M. (2023). Writing on the social network: digital literacy practices in social media ' s first decade . Utah State University Press. https://doi.org/10.7330/ 9781646424023
  3. Mayer, C. (2024). Navigating the new frontier of generative AI in peer review and academic writing. In B. Buyserie, & T. N. Thurston (Eds.), Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions . Utah State University. http://refhub.elsevier.com/S8755-4615(2500008-8/sbref0046)
  4. O ' Neill, P. (2015). All writers have more to learn. In L. Adler-Kassner & E. Wardle (Eds.), Naming what we know: threshold concepts of writing studies (pp. 59 -70). University Press of Colorado. http://www.jstor.org/stable/j.ctt15nmjt7.11.

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Authors
Sperber, L., MacArthur, M., Minnillo, S., Stillman, N., & Whithaus, C.
Affiliations
University of California, Davis (US)
Corresponding author
Sperber, Lisa
ORCID
Venue
Computers and Composition
Publisher
Elsevier BV
Published
2025-06
Volume
76
Pages
102921
ISSN
  • 1873-2011 (ISSN-L)
  • 8755-4615
DOI
10.1016/j.compcom.2025.102921
OpenAlex
W4409725439
Cited by
29
References
70
Citations by year
2026: 20 · 2025: 9
FWCI
89.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers & composition/Computers and composition (publishedVersion, cc-by-nc-nd)
  • SSRN Electronic Journal (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by-nd)
Funders
University of California Davis
Topics
  • Social Sciences › Education › Student Assessment and Feedback
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Arts and Humanities › Language and Linguistics › EFL/ESL Teaching and Learning
Keywords
AI writing feedback, peer review, formative assessment, student agency, AI literacy, student reflection, writing instruction, student perceptions of AI, human-AI collaboration

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