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Leveraging generative AI to facilitate peer feedback in collaborative argumentation learning.

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Title: Leveraging generative AI to facilitate peer feedback in collaborative argumentation learning.
Authors: Chang, Yubei1,2 (AUTHOR) changyb@mails.ccnu.edu.cn, Liu, Qingtang1,2 (AUTHOR) liuqtang@mail.ccnu.edu.cn, Lu, Yingxue1,2 (AUTHOR) 2679717351@qq.com, Miao, Enhui1,2 (AUTHOR) enhuimiao1114@gmail.com
Source: International Journal of Educational Technology in Higher Education. 3/24/2026, Vol. 23 Issue 1, p1-35. 35p.
Subject Terms: *Generative artificial intelligence, *Collaborative learning, *Academic support programs, *Instructional systems design, *Psychological feedback, Epistemics, Persuasion (Rhetoric)
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. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Leveraging generative AI to facilitate peer feedback in collaborative argumentation learning.
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  Data: <searchLink fieldCode="AR" term="%22Chang%2C+Yubei%22">Chang, Yubei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> changyb@mails.ccnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Qingtang%22">Liu, Qingtang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> liuqtang@mail.ccnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Lu%2C+Yingxue%22">Lu, Yingxue</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> 2679717351@qq.com</i><br /><searchLink fieldCode="AR" term="%22Miao%2C+Enhui%22">Miao, Enhui</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> enhuimiao1114@gmail.com</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Educational+Technology+in+Higher+Education%22">International Journal of Educational Technology in Higher Education</searchLink>. 3/24/2026, Vol. 23 Issue 1, p1-35. 35p.
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  Data: *<searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Collaborative+learning%22">Collaborative learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Academic+support+programs%22">Academic support programs</searchLink><br />*<searchLink fieldCode="DE" term="%22Instructional+systems+design%22">Instructional systems design</searchLink><br />*<searchLink fieldCode="DE" term="%22Psychological+feedback%22">Psychological feedback</searchLink><br /><searchLink fieldCode="DE" term="%22Epistemics%22">Epistemics</searchLink><br /><searchLink fieldCode="DE" term="%22Persuasion+%28Rhetoric%29%22">Persuasion (Rhetoric)</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Educational Technology in Higher Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1186/s41239-026-00586-w
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      – SubjectFull: Academic support programs
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      – SubjectFull: Instructional systems design
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              Text: 3/24/2026
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