Cited by 159Academic Writing and PublishingAcademic integrity and plagiarismAcademic Publishing and Open Access

The importance of transparency: Declaring the use of generative artificial intelligence ( AI ) in academic writing

Open access
Tang, Arthur, Li, Kin‐Kit, Kwok, Kin On, Cao, Liujiao, Luong, Stanley, Tam, Wilson·2024·Journal of Nursing Scholarship

Abstract

The integration of generative artificial intelligence (AI) into academic research writing has revolutionized the field, offering powerful tools like ChatGPT and Bard to aid researchers in content generation and idea enhancement. We explore the current state of transparency regarding generative AI use in nursing academic research journals, emphasizing the need for explicitly declaring the use of generative AI by authors in the manuscript. Out of 125 nursing studies journals, 37.6% required explicit statements about generative AI use in their authors' guidelines. No significant differences in impact factors or journal categories were found between journals with and without such requirement. A similar evaluation of medicine, general and internal journals showed a lower percentage (14.5%) including the information about generative AI usage. Declaring generative AI tool usage is crucial for maintaining the transparency and credibility in academic writing. Additionally, extending the requirement for AI usage declarations to journal reviewers can enhance the quality of peer review and combat predatory journals in the academic publishing landscape. Our study highlights the need for active participation from nursing researchers in discussions surrounding standardization of generative AI declaration in academic research writing.

Integrity signals

Registry

Crossref · 10.1111/jnu.12938

Cites

13 of 13 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Tang, A., Li, K., Kwok, K. O., Cao, L., Luong, S., & Tam, W.
Affiliations
  • RMIT Vietnam (VN)
  • City University of Hong Kong (HK)
  • Chinese University of Hong Kong (HK)
  • Sichuan University (CN)
  • National University of Singapore (SG)
Corresponding author
Kwok, Kin On
Venue
Journal of Nursing Scholarship
Publisher
Wiley
Published
2023-10-31
Volume
56
Issue
2
Pages
314-318
ISSN
  • 0363-2792 (ISSN-L)
  • 1527-6546
  • 1547-5069
OpenAlex
W4388035208
Cited by
159
References
13
Citations by year
2026: 44 · 2025: 81 · 2024: 34
FWCI
143.78
Type
journal article
Language
en
Open access
Locations
  • Journal of Nursing Scholarship (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • CityU Scholars (submittedVersion, cc-by)
Topics
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
Keywords
generative AI, research transparency, nursing journals, author guidelines, peer review, predatory journals, ChatGPT, AI in academic writing, research integrity
MeSH terms
Artificial Intelligence, Humans, Peer Review, Publishing, Writing, Nursing Research

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review