GenAI-supported peer feedback groups produced more suggestions and explanations and less positive affective feedback than the PF group¶

Claim · All claims
Evidence · 1 study (2 entries) · causal r1–r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Suggestion was the most frequent feedback element in the GenAI-PF and PS-GenAI PF groups. → Chang 2026 q2 i? Positive affective feedback was higher in the PF group, while negative feedback was higher in the PF and PS-GenAI PF groups than in the GenAI-PF group. → Chang 2026 (2)

Evidence¶

Chang 2026¶

Chang, Y., Liu, Q., Lu, Y. & Miao, E. (2026). Leveraging generative AI to facilitate peer feedback in collaborative argumentation learning. International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-026-00586-w

q2 · i? · causal · r2

Coding of 48 intergroup feedback reports (756 meaning units) across the three conditions for feedback elements (description, identification, explanation, suggestion). In the PF group, identification had the highest percentage, followed by description.

"Suggestion had the highest percentage in the GenAI-PF group and the PS-GenAI PF group (48.4% and 39.5% respectively), followed by identification (27.1% and 31.2% respectively)."

Chang 2026 (2)¶

Chang, Y., Liu, Q., Lu, Y. & Miao, E. (2026). Leveraging generative AI to facilitate peer feedback in collaborative argumentation learning. International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-026-00586-w

q2 · i? · causal · r1

Affective coding of the same 48 feedback reports across the three conditions. Neutral feedback accounted for the highest percentage in all three groups; the reported affective proportions are descriptive percentages.

"positive were significantly higher in the PF group (20.6%) than in the other two groups (7.2% and 14.8% respectively), negative were higher in the PF group (25.8%) and the PS-GenAI PF group (24.4%) than in the GenAI-PF group (14.8%)."

Discussion¶

Related Claims¶

• Epistemic network analysis linked the PS-GenAI-PF group to negative emotions and higher-order feedback content associations — related
• Argument quality gains favored the PS-GenAI-PF group over the PF group, while elaboration gains favored the GenAI-PF group — related
• The PS-GenAI-PF group outperformed the PF group on rebuttal data and warrant and on addressing the opposing view — related
• GenAI-supported peer feedback groups achieved higher structural complexity of argumentation artifacts than peer-feedback-only groups — related
• Perceptions of feedback characteristics vary between researcher and teacher coders, with agreement shaped by professional background — related
• Qualitatively, AI-peer integrated feedback was associated with a broader repertoire of revision operations and more agentive cognitive strategies than peer feedback alone — related
• Most individual argument structure and quality dimensions showed no significant differences across the three peer feedback conditions — related
• Direct GenAI-supported feedback improves immediate argument-quality gain over peer feedback, while reflective and hybrid designs outperform direct GenAI feedback — related
• Instructors and LLMs distribute feedback similarly across the five argumentative goals, with Claim receiving the most feedback from both — related
• Academics and students experienced both positive and negative emotions in relation to GenAI. — related
• Across tasks, teacher feedback shifted from basic error correction toward higher-order dimensions while AI-assisted peer feedback reduced content suggestions and increased technical-detail feedback — reports the opposite
• Essay quality correlates positively with the affective feature of ChatGPT feedback and negatively with the affective feature of peer feedback — related
• Some teacher feedback codes commonly co-occur, such as Confirmation with Positive Affect and Mathematical Suggestions with Hints — related