Nursing Academic Reviewers’ Perspectives on AI‐Assisted Peer Review: Ethical Challenges and Acceptance
Abstract
Aim This study aimed to explore the perceptions, experiences, and ethical considerations of nursing academic reviewers regarding the integration of artificial intelligence (AI) into the peer review process, with a focus on acceptance dynamics and implications for nursing journal policy. Design A qualitative descriptive design was employed, guided by an interpretivist approach and reported according to the COREQ checklist. Methods Fifteen nursing academic reviewers from four countries (Saudi Arabia, Egypt, Australia, and the United States) were recruited via snowball sampling. Semi-structured interviews were conducted between January and March 2025 using Zoom video conferencing. Interviews were held in Arabic or English, transcribed verbatim, translated as needed, and thematically analyzed using reflexive thematic analysis per Braun and Clarke's six-phase framework. Results Five themes were generated: perceived benefits of AI (efficiency, fairness, and workload reduction), ethical concerns (transparency, bias, and data privacy), risks to reviewer autonomy and judgment, divergent attitudes toward AI adoption, and the need for clear guidelines and training. Participants expressed cautious optimism, emphasizing that while AI tools may enhance consistency and reduce administrative burden, they raise complex ethical questions and must not replace human judgment. Conclusion The integration of AI into peer review processes presents both opportunities and ethical challenges. The nursi…
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Nursing researchers expressed cautious optimism regarding AI integration, recognizing its ability to improve efficiency while stressing the importance of preserving human judgement and ethical oversight.
“Nursing researchers similarly expressed cautious optimism regarding AI integration, recognizing its ability to improve efficiency while stressing the importance of preserving human judgement and ethical oversight (Ali and Shaban 2025).”
From Who Reviews the Reviewer? A Multi‐Agent LLM Architecture With Meta‐Review Synthesis for Editorial Peer Review (2026) · cites this paper as Ali and Shaban 2025
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Ali, Ș., Oprea, S., & Bâra, A. (2026) · Systems Research and Behavioral Science · doi:10.1002/sres.70163
- Authors
- Ali, S. I., & Shaban, M.
- Affiliations
- King Faisal University (SA)
- Cairo University (EG)
- Corresponding author
- Shaban, Mostafa
- Venue
- International Nursing Review
- Publisher
- Wiley
- Published
- 2025-08-20
- Volume
- 72
- Issue
- 3
- Pages
- e70100-e70100
- ISSN
- 0020-8132 (ISSN-L)
- 1466-7657
- OpenAlex
- W4413358687
- Cited by
- 6
- References
- 7
- Citations by year
- 2026: 5 · 2025: 1
- FWCI
- 22.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Nursing Review (publishedVersion)
- PubMed (publishedVersion)
- Funders
- King Faisal University (KFU252685)
- Topics
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Publishing and Scholarly Communication
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- AI-assisted peer review, ethical challenges of AI, algorithmic bias, transparency, data privacy, academic integrity, AI governance framework, reviewer training, nursing journals
- MeSH terms
- Adult, Artificial Intelligence, Attitude of Health Personnel, Australia, Egypt, Faculty, Nursing, Female, Humans, Male, Middle Aged, Peer Review, United States, Nursing Research, Peer Review, Research, Qualitative Research
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