The Blurred Thresholds of AI-Use Disclosure: Health Professions Education Journal Editors’ Expectations of Necessity and Sufficiency
Bibliographic record
Abstract
Introduction: 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 study explores journal editors' experiences and expectations of AI-use disclosure, to assist journals to clarify expectations and authors to satisfy them. Methods: In this descriptive qualitative study, editors were interviewed between January 6, 2025, and May 7, 2025 using Zoom. Eligible participants were identified through journal webpages and snowball sampling. A purposive sampling strategy prioritized HPE journals and included a limited sample of general medical 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 worked in HPE journals, four in general medical journals. The analysis revealed 4 themes: 1) the basics of disclosure, made up of content expectations and process knowledge; 2) the necessity threshold, regarding which circumstances require disclosure; 3) the sufficiency threshold, regarding how much detail to include; and 4) the factors blurring these thresholds, which included the speed of change, the co-construction of standards, and the uneasy fit of some scientific principles with the AI-use context. Conclusions: While editors shared basic disclosure expectations, these were complicated by blurred thresholds of sufficiency and necessity that may exacerbate uncertainty in the scholarly community. By attending to these thresholds and the factors blurring them, and by working to articulate shared disclosure standards, HPE journals can help authors safely navigate the shifting norms of AI-use disclosure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".