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Publishing Policies on AI-Generated Content in Nursing: An Evidence Base for Shaping Educational Policy and Upholding
1Department of Spine Surgery, Ganzhou People's Hospital, Ganzhou, China.
Background:
The rapid integration of generative AI into scholarly writing has created an inconsistent policy landscape, challenging academic integrity in nursing education.
Purpose:
This study analyzed nursing journals' publishing policies on AI-generated content (AIGC) to create an evidence base for educators.
Methods:
A cross-sectional analysis of nursing journals from the Scimago database was conducted. Author guidelines were systematically screened for AIGC policies, and data on prevalence, authorship, and disclosure were extracted.
Results:
A majority (65.31%) of the 98 included nursing journals had AIGC policies. Broad agreement prohibits AI authorship (85.94%) and mandates human accountability (84.38%), with all requiring disclosure. However, guidance on AI-generated images was sparse (43.88%) and inconsistent.
Conclusion:
Inconsistent and incomplete AIGC policies create an ambiguous landscape for nursing students and faculty. These findings highlight a need for nursing education to proactively develop curricula, shape policies, and advocate for standardized guidelines to ensure the ethical use of generative AI and uphold scholarly integrity.
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