The blurred threshold of AI-use disclosure: International journal editors’ expectations of sufficiency and necessity
Bibliographic record
Abstract
Abstract Purpose Generative AI is a powerful resource for health professions education (HPE) researchers publishing their work. However, questions remain about its use and guidance about disclosure is inconsistent. This situation is both confusing and potentially perilous for researchers, who risk their reputations if they disclose AI use inappropriately. This study explores HPE and general medical journal editors’ experiences and expectations of AI-use disclosure, in order to assist journals to clarify expectations and authors to satisfy them. Methods In this descriptive qualitative study, journal editors were interviewed between January 6, 2025, and May 7, 2025 using an online Zoom platform. Eligible participants were identified through journal webpages and snowball sampling. A purposive sampling strategy prioritized HPE research journals and included a limited sample of general medical research journals to explore transferability. Data collection and thematic analysis proceeded iteratively. Results Eighteen participants, including 9 chief editors and 9 associate/deputy editors were interviewed. Fourteen participants worked in HPE journals, four in general medical journals. The analysis revealed 4 key themes: 1) the basics of disclosure, made up of content and location expectations shared by participants; 2) the sufficiency threshold, regarding how much detail to include; 3) the necessity threshold, regarding which circumstances require disclosure; and 4) the factors blurring these two thresholds, which included the speed of change, the co-construction of disclosure standards, and the uneasy fit of scientific principles such as reproducibility and transparency with the AI-use context. Conclusions While editors shared basic disclosure expectations, they also provided insight into blurred thresholds of sufficiency and necessity that complicate disclosure. By attending to these thresholds and the factors blurring them, and by using these insights to apply recent AI-use and disclosure frameworks, journals can develop enhance their guidelines, which will assist authors in HPE in navigating the shifting norms of AI-use disclosure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.099 | 0.305 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".