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.

中文翻译：

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不确定性下的言语表达：求职者如何体验并与人工智能面试官沟通？

人机通信（HMC）的理论和研究表明，机器在取代人类作为通信伙伴时，会改变通信的过程和结果。随着人工智能（AI）越来越多地用于面试和评估求职者，雇主在基于人工智能的面试中应考虑人工智能对求职者心理和表现的影响。这项研究考察了人工智能评估求职者的经验和言语流畅性。在一项三条件受试者间实验（N = 134）中，大学生进行了在线模拟工作面试，他们的印象是他们的表现将由人类招聘人员、人工智能系统或具有类人界面的人工智能系统进行评估。与人类评估条件相比，参与者在人工智能评估条件下报告了更高的不确定性和更低的社会存在感，并且表达率更高。通过降低社交存在感，人工智能评估提高了语速并减少了无声停顿。研究结果为 HMC 理论以及自动化招聘和专业培训的实践提供了信息。