Scammer Participants and AI-Assisted Interviews in Qualitative Health Research: An Example From a Study of Expectant Parents With a Rare Genetic Condition

Online interviewing has allowed cost-effective data collection and access to diverse, geographically dispersed populations. However, these benefits come with significant risks to research integrity, particularly in terms of attracting potential scammer participants—individuals who volunteer to take part in research by creating a false identity that fits the eligibility criteria for a research study. This paper explores our experiences in conducting online interviews for a qualitative health research study involving expectant parents with a rare genetic condition. We outline the strategies we implemented to deter fraudulent interest and participation at different stages of recruitment and data collection and share our critical reflections on how we managed these challenges. This paper highlights how Artificial Intelligence (AI)-assisted software may help scammers to evade detection, representing a new external threat to the validity with online qualitative research. We provide practical recommendations for safeguarding qualitative research in the digital age, along with a checklist for the design stage, informed by recent technological advancements and lessons learned from our study.