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Cited by 4Student Assessment and FeedbackArtificial Intelligence in EducationInnovative Teaching and Learning Methods

Leveraging generative AI to facilitate peer feedback in collaborative argumentation learning

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Chang, Yubei, Liu, Qingtang, Lu, Yingxue, Miao, Enhui·2026·International Journal of Educational Technology in Higher Education
DOI 10.1186/s41239-026-00586-w
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Abstract

Peer feedback is crucial for promoting deeper thinking and interaction. However, various challenges exist in conducting high-quality peer feedback in collaborative argumentation learning. Generative AI (GenAI) has shown potential for supporting peer feedback, but how GenAI can be efficiently applied to support peer feedback needs to be further explored. In addition, the effect of GenAI-supported peer feedback on argumentation performance and feedback quality remains unclear. To address these gaps, this study compared three peer feedback conditions: peer feedback (PF), peer feedback with GenAI (GenAI-PF), and peer feedback with GenAI supported by prompt scaffolding (PS-GenAI-PF). Forty-five student teachers were assigned to 12 groups of 3–4 students. All groups were randomly assigned to three peer feedback conditions, each including four groups. Kruskal-Wallis H test results showed that the GenAI-supported peer feedback groups performed better in argumentation performance. In particular, the PS-GenAI PF group performed better on “Rebuttal data and warrant” and “Addressing the opposing view”. For feedback quality, the GenAI-supported peer feedback group showed more explanation, suggestions, as well as neutral and negative feedback. The epistemic network analysis revealed that the PS-GenAI PF group had strong associations with negative emotions and higher-order feedback content, such as identification-suggestion and explanation-suggestion. Implications for the instructional design and implementation of GenAI-supported peer feedback in higher education are discussed.

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  • Many studies compare a single AI condition with a single non-AI condition, leaving underexplored how alternative feedback designs may distribute evaluative work differently across learners, peers, and GenAI.

    “many studies compare a single AI condition with a single non-AI condition, leaving underexplored how alternative feedback designs may distribute evaluative work differently across learners, peers, and GenAI”

    From Human-centered GenAI feedback design in higher education: a multisite experiment on direct, reflective, and hybrid approaches to scientific argumentation (2026) · cites this paper as Chang et al., 2026

    The Introduction (page unavailable) says that how to use GenAI effectively for peer feedback remains underexplored, and the Research design section compares three feedback conditions. The paper does not say that many prior studies used only one AI and one non-AI condition, or frame the gap specifically as how evaluative work is distributed among learners, peers, and GenAI. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Chang, Y., Liu, Q., Lu, Y., & Miao, E.
Affiliations
Central China Normal University (CN)
Corresponding author
Liu, Qingtang
ORCID
Venue
International Journal of Educational Technology in Higher Education
Publisher
Springer Science and Business Media LLC
Published
2026-03-24
Volume
23
Issue
1
ISSN
2365-9440 (ISSN-L)
DOI
10.1186/s41239-026-00586-w
OpenAlex
W7140085081
Cited by
4
References
27
Citations by year
2026: 4
FWCI
84.82
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Educational Technology in Higher Education (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China ‘Research on Instructional Interaction Behavior Recognition and Pattern Mining in Informationization Classroom Integrating Multimodal Data’ (62277021)
  • Humanities and Social Science Fund of Ministry of Education of China (22YJAZH067)
Topics
  • Social Sciences › Education › Student Assessment and Feedback
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
Keywords
generative AI, peer feedback, collaborative argumentation, feedback quality, epistemic network analysis, pre-service teachers, higher education

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