Evidence map›Paper›PMID 40950847›Full record

ArticleResearch ethics2025

Disclosing generative AI use for writing assistance should be voluntary.

Mohammad Hosseini, Bert Gordijn, Gregory E Kaebnick, Kristi Holmes

Abstract read
In one paragraph

Article in Research ethics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Mohammad HosseiniNorthwestern University Feinberg School of Medicine, Chicago, IL, USA.ORCID 0000-0002-2385-985X
Bert GordijnDublin City University, Ireland.ORCID 0000-0002-3686-8659
Gregory E KaebnickThe Hastings Center, Philipstown, NY, USA.
Kristi HolmesNorthwestern University Feinberg School of Medicine, Chicago, IL, USA.ORCID 0000-0001-8420-5254

Funding

NUCATS CTSA UM1 at Northwestern UniversityUM1TR005121 · NCATS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Sara Becker, Richard D'Aquila · 2024 to 2026
$23.4M
NCATS NIH HHS UM1 TR005121
6 · The paper itself

Abstract

Researchers have been using generative artificial intelligence (GenAI) to support writing manuscripts for several years now. However, as GenAI evolves and scientists are using it more frequently, the case for mandatory disclosure of GenAI for writing assistance continues to diverge from the initial justifications for disclosure, namely (1) preventing researchers from taking credit for work done by machines; (2) enabling other researchers to critically evaluate a manuscript and its specific claims; and (3) helping editors determine if a submission satisfies their editorial policies. Our initial position (communicated through previous publications) regarding GenAI use for writing assistance was in favor of mandatory disclosure. Nevertheless, as we show in this paper, we have changed our position and now support instituting a voluntary disclosure policy because currently (1) the credit due to machines for assisting researchers is moving below the threshold of requiring recognition; (2) it is impractical (if not impossible) to accurately specify what parts of the text are human-/GenAI-generated; and (3) disclosures could increase biases against non-native speakers of the English language and compromise the integrity of the peer review system. Consequently, we argue, it should be up to the authors of manuscripts to disclose their use of GenAI for writing assistance. For example, in disciplines where writing is the hallmark of originality, or when authors believe disclosure is beneficial, a voluntary checkbox in manuscript submission systems, visible only after publication (rather than a free-text note in the manuscripts) would be preferable.

Indexed as

artificial intelligencedisclosureeditorial policiespeer reviewpublication ethicswriting

Identifiers

PMID40950847
PMCPMC12425484

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.