Nursing Academic Reviewers' Perspectives on AI-Assisted Peer Review: Ethical Challenges and Acceptance.

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 nursing academic reviewers in this study recognize the utility of AI for supporting routine tasks but remain concerned about algorithmic bias, transparency, and its impact on scholarly independence. Ethical AI adoption requires structured policies and capacity-building initiatives.

IMPLICATIONS FOR NURSING PRACTICE AND POLICY

To uphold scholarly integrity, nursing journals and academic institutions should develop transparent AI governance frameworks, invest in reviewer education on responsible AI use, and preserve the central role of human judgment in peer review. These steps are vital to ensuring AI complements rather than compromises research quality and ethics in global nursing scholarship.