Adapting peer review for the future: Digital disruptions and trust in peer review
Open accessAbstract
Key points: Academia is undergoing transformation with rapid technological advancements, global collaboration, and the rise of AI. Maintaining research integrity is paramount in the face of new challenges and opportunities. The peer review process, vital for research integrity, is strained due to the high volume of academic papers. Cactus Communication's (CACTUS) global survey during Peer Review Week 2022 gathered insights from 685 participants on research integrity and peer review. Survey participants emphasized the importance of peer review in detecting various issues, including plagiarism and questionable research practices. Actions taken in response to integrity concerns varied, with some participants seeking guidance from colleagues or informing journal editors. Digital innovations, especially AI‐driven tools, promise to revolutionize peer review processes, from manuscript screening to reviewer selection and evaluation. AI tools like Chat GPT can aid authors in improving writing quality and assist reviewers in generating detailed feedback. Concerns about data privacy and the need for a balance between human expertise and AI assistance are raised in the context of peer review. AI plagiarism detection tool scan complement human efforts, providing an additional layer of scrutiny. Increased digital innovation allows for flexible review formats, international collaboration, and heightened transparency in the review process. The integration of AI into peer review holds the potential to enhance efficiency and objectivity but raises concerns about bias and transparency. Guidelines, bias mitigation measures, accountability mechanisms, and ongoing training are essential for the responsible use of AI in peer review. Encouraging peer review systems to embrace innovation and ethical AI is crucial for adapting to changing dynamics while upholding core principles of research integrity.
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AI-assisted systems may enhance flexibility, transparency and consistency in peer review while reducing reviewer burden.
“AI-assisted systems may enhance flexibility, transparency and consistency in peer review while reducing reviewer burden”
From Who Reviews the Reviewer? A Multi‐Agent LLM Architecture With Meta‐Review Synthesis for Editorial Peer Review (2026) · cites this paper as Calamur and Ghosh 2024
In “The Intersection of AI and Research Integrity” (page unavailable), the paper says AI may make evaluations more consistent. “The Promise of Digital Innovation in Academia” (page unavailable) says AI can help reviewers write reports and focus on critical aspects, supporting a potential reduction in burden. It does not attribute greater flexibility or transparency to AI-assisted systems; instead, it identifies AI opacity as a concern. (LLM, conf=0.87)
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Research works printed but not linked: 4
- CACTUS. (2023). Peer Review Week 2022 Research Integrity Survey Report. https://cdn.editage.com/insights/editagecom/production/ 2023-09/CACTUS-Peer-Review-Week-2022-Research-Integrity- Survey-Report.pdf
- Hall, P., & Ellis, D. (2023). A systematic review of socio-technical gender bias in AI algorithms. Online Information Review, 47(7), 1264-1279. https://doi.org/10.1108/OIR-08-2021-0452 · DOI
- Tenorio-Fornés, Á., Pérez Tirador, E., Sánchez-Ruiz, A. A., & Hassan, S. (2021). Decentralizing science: Towards an interoperable open peer review ecosystem using blockchain. Information Processing & Manage- ment, 58(6), 102724. https://doi.org/10.1016/j.ipm.2021.102724 · DOI
- Varsha, P. S. (2023). How can we manage biases in artificial intelli- gence systems -A systematic literature review. International Jour- nal of Information Management Data Insights, 3(1), 100165. https://doi.org/10.1016/j.jjimei.2023.100165 · DOI · Paper page
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Ali, Ș., Oprea, S., & Bâra, A. (2026) · Systems Research and Behavioral Science · doi:10.1002/sres.70163
- Authors
- Calamur, H., & Ghosh, R.
- Affiliations
- Researcher Outreach Department Cactus Communications (CACTUS) Princeton Texas USA
- Corresponding author
- Ghosh, Roohi
- ORCID
- Ghosh, Roohi
- Venue
- Learned Publishing
- Publisher
- Wiley
- Published
- 2024-01
- Volume
- 37
- Issue
- 1
- Pages
- 49-54
- ISSN
- 0953-1513 (ISSN-L)
- 1741-4857
- OpenAlex
- W4390885104
- Cited by
- 16
- References
- 0
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 2
- FWCI
- 23.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Learned Publishing (publishedVersion, other-oa)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Publishing and Scholarly Communication
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- peer review, research integrity, artificial intelligence in peer review, ChatGPT, plagiarism detection, questionable research practices, reviewer bias, peer review transparency, data privacy, reviewer training, AI ethics, scholarly publishing
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