With the growing popularity of intelligent assistants (IAs), evaluating IA quality becomes an increasingly active field of research. This paper identifies and quantifies the feedback effect, a novel component in IA-user interactions: how the capabilities and limitations of the IA influence user behavior over time. First, we demonstrate that unhelpful responses from the IA cause users to delay or reduce subsequent interactions in the short term via an observational study. Next, we expand the time horizon to examine behavior changes and show that as users discover the limitations of the IA's understanding and functional capabilities, they learn to adjust the scope and wording of their requests to increase the likelihood of receiving a helpful response from the IA. Our findings highlight the impact of the feedback effect at both the micro and meso levels. We further discuss its macro-level consequences: unsatisfactory interactions continuously reduce the likelihood and diversity of future user engagements in a feedback loop.

翻译：随着智能助手的日益普及，评估智能助手质量成为一个日益活跃的研究领域。本文识别并量化了反馈效应，这是智能助手-用户交互中的一个新组成部分：智能助手的能力和局限性如何随时间影响用户行为。首先，通过一项观察性研究，我们证明智能助手无帮助的回应会导致用户在短期内延迟或减少后续互动。接着，我们扩展时间范围以考察行为变化，并表明随着用户发现智能助手理解能力和功能能力的局限性，他们会学习调整请求的范围和措辞，以增加从智能助手获得有用回应的可能性。我们的发现强调了反馈效应在微观和中观层面的影响。我们进一步讨论了其宏观层面的后果：不令人满意的互动会在反馈循环中持续降低未来用户参与的可能性和多样性。