2026/01/19 by Sam Quinn, Naomi Richards, Joanne Williams +4 · 1 voice
Social Sciences · #Focus Groups and Qualitative Methods #Qualitative Research Methods and Applications #Data Analysis and Archiving
paper · doi:10.1177/16094069261431990
openalex publication_date 2026/01/19 · openalex created_date 2026/03/13 · openalex updated_date 2026/07/30
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.