A vulnerable space: Health professions education scholars' reflections on AI-use disclosure
- 1Western University
Abstract
BackgroundAI use is growing by HPE scholars, and disclosure of that use is required by scientific ethics and journal policy. In spite of growing unease about the meaning, and meaningfulness, of AI-use disclosures in published manuscripts, little is known about how authors represent their AI use in published disclosure statements. Without such understanding, we are ill-equipped to redirect disclosure practices for increased sufficiency and sincerity, undermining efforts towards transparency in the era of AI-supported scholarship
MethodsIn this descriptive qualitative study, 18 HPE researchers with experience disclosing generative AI use in publications from 2025-2026 were interviewed using Zoom. Data collection and thematic analysis proceeded iteratively, exploring participants experiences with AI use, their disclosure decisions, and evolving disclosure practices.
ResultsParticipants reported a wide range of experience writing AI-use disclosures (from 1-16 disclosures/participant); in total 31 published disclosures were shared by participants and discussed during the interviews. Our analysis identified four themes: A spectrum of AI uses; Multi-factorial disclosure decisions; Discrepancies between actual and disclosed use; and Emotions around disclosure. These are described below and illustrated with representative excerpts from the dataset.
DiscussionAI-disclosure practices take place at the intersection of shared ethical principles and situated pragmatic concerns. Disclosure is both an intellectual and an emotional practice, changing over time as AI use evolves and journal expectations develop. More generative uses may provoke divisive responses and are related to uncertainty about how to disclose, concern about disclosure penalties, and periodic use-disclosure discrepancies. Understanding how researchers are navigating these complexities may help our HPE community articulate clearer guidance for transparent and meaningful disclosure of AI-use in scholarly work. Reflective methodologies can help bring these issues to the surface of our fields ongoing conversations about AI use.
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BibTeX
@misc{lingard2026vulnerable,
title = {{A vulnerable space: Health professions education scholars' reflections on AI-use disclosure}},
author = {L. A. Lingard and L. A. Maggio and M. Ans and E. Driessen},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.01.26364502},
url = {https://www.medrxiv.org/content/10.64898/2026.10.01.26364502v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - A vulnerable space: Health professions education scholars' reflections on AI-use disclosure
AU - L. A. Lingard
AU - L. A. Maggio
AU - M. Ans
AU - E. Driessen
PY - 2026
DA - 2026/10/01
PB - medRxiv
DO - 10.64898/2026.10.01.26364502
UR - https://www.medrxiv.org/content/10.64898/2026.10.01.26364502v1
AB - BackgroundAI use is growing by HPE scholars, and disclosure of that use is required by scientific ethics and journal policy. In spite of growing unease about the meaning, and meaningfulness, of AI-use disclosures in published manuscripts, little is known about how authors represent their AI use in published disclosure statements. Without such understanding, we are ill-equipped to redirect disclosure practices for increased sufficiency and sincerity, undermining efforts towards transparency in the era of AI-supported scholarship MethodsIn this descriptive qualitative study, 18 HPE researchers with experience disclosing generative AI use in publications from 2025-2026 were interviewed using Zoom. Data collection and thematic analysis proceeded iteratively, exploring participants experiences with AI use, their disclosure decisions, and evolving disclosure practices. ResultsParticipants reported a wide range of experience writing AI-use disclosures (from 1-16 disclosures/participant); in total 31 published disclosures were shared by participants and discussed during the interviews. Our analysis identified four themes: A spectrum of AI uses; Multi-factorial disclosure decisions; Discrepancies between actual and disclosed use; and Emotions around disclosure. These are described below and illustrated with representative excerpts from the dataset. DiscussionAI-disclosure practices take place at the intersection of shared ethical principles and situated pragmatic concerns. Disclosure is both an intellectual and an emotional practice, changing over time as AI use evolves and journal expectations develop. More generative uses may provoke divisive responses and are related to uncertainty about how to disclose, concern about disclosure penalties, and periodic use-disclosure discrepancies. Understanding how researchers are navigating these complexities may help our HPE community articulate clearer guidance for transparent and meaningful disclosure of AI-use in scholarly work. Reflective methodologies can help bring these issues to the surface of our fields ongoing conversations about AI use.
ER -