Speech production under uncertainty: how do job applicants experience and communicate with an AI interviewer?

Abstract

Abstract Theories and research in human–machine communication (HMC) suggest that machines, when replacing humans as communication partners, change the processes and outcomes of communication. With artificial intelligence (AI) increasingly used to interview and evaluate job applicants, employers should consider the effects of AI on applicants’ psychology and performance during AI-based interviews. This study examined job applicants’ experience and speech fluency when evaluated by AI. In a three-condition between-subjects experiment (N = 134), college students had an online mock job interview under the impression that their performance would be evaluated by a human recruiter, an AI system, or an AI system with a humanlike interface. Participants reported higher uncertainty and lower social presence and had a higher articulation rate in the AI-evaluation condition than in the human-evaluation condition. Through lowering social presence, AI evaluation increased speech rate and reduced silent pauses. Findings inform theories of HMC and practices of automated recruitment and professional training.