Augmented Peer Review: A Framework for AI-Supervised Academic Publishing
The traditional academic peer review process is facing a sustainability crisis characterized by 6–18 month publication delays, persistent geographic bias, and a reproducibility crisis.
Background:
The traditional academic peer review process is facing a sustainability crisis characterized by 6–18 month publication delays, persistent geographic bias, and a reproducibility crisis. Current AI interventions are fragmented and often lack a unified governance framework.
Proposed Framework:
We present the Augmented Peer Review Framework (APRF), an end-to-end workflow that integrates AI tools under strict human supervision. Central to this framework is the "AI Board Chair," a novel trained evaluation entity that scores manuscripts on methodological rigor, transparency, and novelty against an "ideal paper" corpus. Unlike autonomous agents, the AI Board Chair functions as a decision-support tool, providing structured recommendations while human editors retain final authority.
Implementation Pathway:
To ensure credibility and accreditation, we propose a "Shadow Review Pilot" model where the APRF runs in parallel with existing review processes without affecting editorial decisions until validated.
Conclusion:
By shifting from manual processing to AI-augmented human judgment, the APRF offers a pragmatic pathway to reduce time-to-decision to 4–8 weeks, mitigate institutional bias, and enhance the consistency of scientific publishing.