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Cited by 31Student Assessment and FeedbackInnovative Teaching and Learning MethodsOnline Learning and Analytics

Incorporating Training, Self-monitoring and AI-Assistance to Improve Peer Feedback Quality

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Darvishi, Ali, Khosravi, Hassan, Abdi, Solmaz, Sadiq, Shazia, Gašević, Dragan·2022·Proceedings of the Ninth ACM Conference on Learning @ Scale
DOI 10.1145/3491140.3528265
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Abstract

Peer review has been recognised as a beneficial approach that promotes higher-order learning and provides students with fast and detailed feedback on their work. Still, there are some common concerns and criticisms associated with the use of peer review that limits its adoption. One of the main points of concern is that feedback provided by students may be ineffective and of low quality. Previous works supply three explanations for why students may fail to provide effective feedback: They lack (1) the ability to provide high-quality feedback, (2) the agency to monitor their work or (3) the incentive to invest the required time and effort as they think the quality of the reviews are not reviewed. To help mitigate these shortcomings, this paper presents a complementary peer review approach that integrates training, self-monitoring and AI quality-control assistance to improve peer feedback quality. In particular, informed by higher education research, we built a set of training materials and a self-monitoring checklist for students to consider while writing their reviews. Also, informed by work from natural language processing, we developed quality control functions that automatically assess feedback submitted and prompt students to improve, if necessary. A between-subjects field experiment with 374 participants was conducted to investigate the approach's efficacy. Findings suggest that offering training, self-monitoring, and quality control functionalities to students assigned to the complementary peer review approach resulted in longer feedback that was perceived as more helpful than those who utilised the regular peer review interface. However, this complementary approach does not seem to affect students judgement (leniency or harshness) or confidence in grading. Directions are suggested to further evaluate and refine peer review systems.

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  • More commonly, systems display suggestions in a separated interface.

    “More commonly, systems display suggestions in a separated interface [29,43,75,89].”

    From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [29]

    Section 3.2 describes training material accessed through a button and a checklist placed above the comment box; the Automatic Quality Control section also describes prompts shown to students. These passages support the existence of distinct interface elements for guidance, but they do not establish that systems more commonly display suggestions in a separated interface. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Dhillon et al.'s research demonstrated that while next-paragraph suggestions improved writing quality, longer AI text completions decreased satisfaction by undermining writers' independence.

    “Similarly, Dhillon et al. 's [29] research demonstrated that while next-paragraph suggestions improved writing quality, longer AI text completions decreased satisfaction by undermining writers' independence.”

    From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [29]

    The AI-assistance and RQ2 sections discuss improving student peer-review feedback, not next-paragraph suggestions or longer AI text completions. Searches for Dhillon, text completions, writer independence, and satisfaction found no passage addressing the attributed finding. Page numbers were not available in our copy. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Studies found that suggestion length is inversely associated with perceived ownership of the text.

    “Other studies have examined the impact of suggestion length or quantity of suggestions on user experience and acceptance [14,29,36], finding that suggestion length is inversely associated with perceived ownership of the text.”

    From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [29]

    Section 4.2 reports the length of students’ feedback comments, and the “Specific” section reports how often those comments included improvement suggestions. Neither addresses whether the length of suggestions is inversely associated with perceived ownership of text. Searches for perceived ownership, suggestion length, and acceptance found no passage establishing that relationship. (LLM, conf=0.75)

    Experimental — accuracy not yet measured

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Authors
Darvishi, A., Khosravi, H., Abdi, S., Sadiq, S., & Gašević, D.
Affiliations
  • The University of Queensland (AU)
  • Monash University (AU)
ORCID
Venue
Proceedings of the Ninth ACM Conference on Learning @ Scale
Publisher
ACM
Published
2022-06
Pages
35-47
DOI
10.1145/3491140.3528265
OpenAlex
W4281687378
Cited by
31
References
68
Citations by year
2026: 4 · 2025: 7 · 2024: 11 · 2023: 5 · 2022: 4
FWCI
53.18
Type
conference paper
Language
en
Open access
Licence
Locations
  • ACM Conference on Learning @ Scale (L@S) (publishedVersion, cc-by)
  • Monash University Research Portal (Monash University) (submittedVersion, other-oa)
  • HAL (submittedVersion)
Funders
The Australian Research Council's Industrial Transformation Training Centre for Information Resilience (CIRES) (IC200100022)
Topics
  • Social Sciences › Education › Student Assessment and Feedback
  • Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
peer review, higher education learning

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