Organizations use asynchronous AI interview systems to efficiently manage large applicant pools, enabling quick and uniform evaluations. However, concerns remain about their impact on user agency and the lack of personalization applicants experience with these systems. Although efforts have been made to humanize the interview process, users' expectations are often unmet, especially when compared to the promises made by these systems. To examine how applicants perceive and experience these tools, particularly in the context of their growing familiarity with large language models (LLMs), we conducted a two-phase study. The first phase involved an analysis of 11 subreddit discussions on interview experiences with asynchronous AI interviewers, followed by a semi-structured interview study with 17 participants. Qualitative analysis revealed key issues such as mismatched expectations, amplified by organizational rhetoric and applicant expectations shaped by experiences with LLMs. These factors shaped participants' sense of agency and trust, often leading to workarounds and deceptive practices. In the follow-up study, we designed an interface with two features, response variants and feedback variants, and evaluated it across six groups (N = 180, 30 participants each) to assess whether these features support users' sense of agency, competence, and relatedness. Our analysis suggests that even subtle design changes can enhance user autonomy and that carefully designed feedback can provide meaningful support in high-stakes interview contexts.

翻译：组织采用异步AI面试系统以高效管理大量求职者群体，实现快速且标准化的评估。然而，这些系统对用户自主性的影响以及求职者缺乏个性化体验的问题仍引发担忧。尽管已有人性化面试过程的尝试，但用户期望常与系统承诺存在落差。为探究求职者如何感知和体验这些工具——尤其是在其日益熟悉大语言模型（LLMs）的背景下——我们开展了一项两阶段研究。第一阶段分析11个子论坛中关于异步AI面试官体验的讨论内容，随后对17名参与者进行半结构化访谈研究。定性分析揭示了关键问题：不匹配的期望被组织修辞放大，此外求职者因LLM使用经验形成的期望也加剧了这一矛盾。这些因素影响了参与者的自主感与信任感，常导致他们采取变通手段或欺骗行为。在后续研究中，我们设计了一种集成“回应变体”与“反馈变体”两种功能的界面，并跨六组（N=180，每组30人）进行评估，以验证这些功能能否支持用户的自主感、胜任感与关联感。分析表明，即使是微妙的设计调整也能增强用户自主性，而精心设计的反馈可在高风险面试场景中提供实质性支持。