Navigating the Rise of Artificial Intelligence and Imposter Participants in Qualitative Health Research

Quinn, S ORCID: 0000-0002-6266-0819, Richards, N ORCID: 0000-0002-3029-5132, Williams, J ORCID: 0000-0001-7298-5820, Mogan, C ORCID: 0000-0002-4485-3633, Myall, M ORCID: 0000-0001-8733-7412, Lloyd-Williams, M ORCID: 0000-0002-9590-5288 and Calman, L ORCID: 0000-0002-9964-6017 (2026) Navigating the Rise of Artificial Intelligence and Imposter Participants in Qualitative Health Research. International Journal of Qualitative Methods, 25. ISSN 1609-4069

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Abstract

Artificial intelligence enables people to create convincing online identities and narratives, which poses a growing threat to qualitative health research conducted online. We report on a UK-wide interview study on financial insecurity and serious illness that, during open recruitment across three university sites, received hundreds of false expressions of interest generated or assisted by large language models. Suspected false expressions of interest and screening call notes were retained and anonymised for qualitative content analysis, following Schreier’s (2012) approach. Analysis of emails and screening calls exposed repetitive templated phrasing, vague accounts of serious illnesses, postcode anomalies, and resistance to brief video verification. A layered authentication workflow, postcode checks, UK telephone confirmation, and short introductory calls helped to filter suspected imposters while maintaining accessibility for participants at risk of digital exclusion. This experience highlights the ethical tension between vigilance and trust, and demonstrates the hidden labour and time costs of mitigating imposter participants. By sharing observable red flags, practical screening steps, and their resource implications, we contribute methodological guidance to emerging debates on protecting data integrity in the face of AI-assisted imposter participants. Our case demonstrates that proportionate, manual checks can safeguard authenticity without imposing undue barriers, provided teams remain reflexive about inclusivity and communicate checks clearly in study materials. We outline decision points that researchers and research governance teams can adapt to context, including when to escalate from email screening to telephone or video, how to document anomalies, and how to record exclusions. We argue that journals and funders should recognise verification effort in methods reporting and budgets. The article offers immediate, implementable safeguards for qualitative researchers, and sets priorities for benchmarking detection tools and integrating AI literacy into qualitative methods training.

Item Type:	Article
Uncontrolled Keywords:	qualitative research; imposter participants; digital sociology; participant recruitment; research ethics; artificial intelligence; 46 Information and Computing Sciences; 4608 Human-Centred Computing; 42 Health Sciences; Machine Learning and Artificial Intelligence; 8.3 Policy, ethics, and research governance; 1110 Nursing; 1607 Social Work
Subjects:	Q Science > QA Mathematics > QA76 Computer software R Medicine > RT Nursing T Technology > T Technology (General) > T58.5 Information Technology
Divisions:	Nursing and Advanced Practice
Publisher:	SAGE Publications
Date of acceptance:	18 February 2026
Date of first compliant Open Access:	27 July 2026
Date Deposited:	27 Jul 2026 08:36
Last Modified:	27 Jul 2026 08:36
DOI or ID number:	10.1177/16094069261431990
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29061

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