What Happens When Reviewers Receive AI Feedback in Their Reviews?
Revolutionizing Peer Review with AI: Opportunities and Challenges
AI is reshaping academic research, yet its role in peer review remains polarising and contentious. Advocates see its potential to reduce reviewer burden and improve quality, while critics warn of risks to fairness, accountability, and trust.
Executive Impact Summary
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Reviewers willing to consider revision
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Reviewers who actually revised
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Average perceived revision intention (1-7 scale)
Deep Analysis & Enterprise Applications
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Perceptions of AI Feedback
AI's Role in Peer Review
Design Implications & Future Visions
Reviewers' initial reactions and attitudes towards AI-generated feedback were mixed, revealing both appreciation for clarity and resistance to perceived criticism.
4.51
Average perceived relevance of AI feedback (1-7 scale)
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The study highlights a role reversal: AI prompts reviewers, challenging traditional notions of authorship and expertise in the peer review process.
AI-Augmented Review Process Flow
Reviewer Submits Draft
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AI Generates Feedback
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Reviewer Revises (Optional)
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Final Review Posted
ICLR 2025 AI Feedback Deployment
ICLR 2025 deployed an official AI feedback tool to provide post-review suggestions to reviewers. This was a live, high-stakes environment where AI offered optional suggestions to improve clarity, tone, and specificity. Reviewers could revise their reviews based on this feedback, but were not obligated to do so. The deployment offered unique insights into human-AI interaction in a critical academic process.
Participants envision AI as a collaborative partner and workload reliever, emphasizing human judgment remains paramount for ethical and effective AI integration.
4.49
Average willingness to use AI feedback in the future (1-7 scale)
| Enhancing Reviewer Capabilities | Ensuring Fairness & Accountability |
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Advanced ROI Calculator
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