Integrating Artificial Intelligence into Scholarly Peer Review: A Framework for Enhancing Efficiency and Quality

Richard Wynne and Vijaya B Kolachalama

No s764u_v1, OSF Preprints from Center for Open Science

Abstract: Scholarly publishing is a multibillion-dollar industry that evaluates research outputs and helps curate researcher reputation. These functions are underpinned by more than 100 million hours of volunteer peer reviewer time, representing an estimated $1.5 billion just in the United States . However, the status quo is under pressure from multiple forces: a growing number of research publications requiring peer review, questions about research integrity and reproducibility , business model changes and legal challenges from unpaid peer reviewers. The emergence of AI has exacerbated the problems, but AI also represents an opportunity to ameliorate the peer review experience and improve standards. We propose a framework for integrating AI into scholarly peer review that leverages the strengths of AI and human expertise. The framework offers a structured approach for evaluating the application of AI, with the aim of improving efficiency, consistency, comprehensiveness, and quality in the evaluation of scholarly contributions.

Date: 2025-05-16
References: Add references at CitEc
Citations:

Downloads: (external link)
https://osf.io/download/6825d4cb318bac7468eadfbd/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:s764u_v1

DOI: 10.31219/osf.io/s764u_v1

Access Statistics for this paper

More papers in OSF Preprints from Center for Open Science
Bibliographic data for series maintained by OSF ().