Candidate Generative AI Use in Pre‐Hire Employment Assessments: Self‐Reported Incidence and the Impact of Warnings

ABSTRACT The rapid rise of generative artificial intelligence (GenAI) poses new challenges for the validity and fairness of pre‐hire employment assessments. Across two applied studies using large applicant samples completing a standardized pre‐hire assessment (which includes both cognitive and non‐cognitive components), we examined the self‐reported incidence of GenAI assistance, and the impact of warning statements designed to deter such behavior. In Study 1 ( N = 5675), conducted in Q3 2024, fewer than 3% of applicants reported using GenAI, though up to 19% reported using GenAI in combination with algorithmic resources (e.g., search engines). All three warning statements (consequences, educational, and reasoning) reduced reported use relative to the control condition, with limited evidence favoring the consequences‐based warning. Study 2 ( N = 3356), conducted in Q3 2025, focused exclusively on the consequences‐based warning. Self‐reported GenAI use increased from 2024 to 2025. Warnings significantly reduced the incidence of GenAI use but did not alter motivations, contexts, perceived effectiveness, or applicant reactions. Finally, analyses of potential job fit scores indicated that GenAI use per se was not systematically related to potential job fit, though stress‐related motivations for GenAI use showed a small negative association with lower potential job fit. These findings highlight both the likely growing prevalence of GenAI in selection contexts and the utility of warnings as a potential deterrence strategy.