在沉默中倾听：利用情境感知节奏设计人与对话代理的主动倾听
Hear You in Silence: Designing for Active Listening in Human Interaction with Conversational Agents Using Context-Aware Pacing
AI总结：
本文提出通过情境感知节奏策略提升对话代理的主动倾听能力，通过实验验证其在人际关系和职业支持场景中的有效性。
摘要：
在人类对话中，共情对话需要细微的时间线索表明对话伙伴是否在倾听。这种类型的'主动倾听'在对话代理（CAs）的设计中被忽视，因为CAs在一次对话中使用相同的时间节奏。为了建模人类对话中的时间线索，我们需要能够根据用户输入动态调整响应节奏的CAs。我们对十个主动倾听案例进行了定性分析，提炼出五种情境感知节奏策略：反思沉默、促进沉默、共情沉默、保持空间和即时响应。在一项被试间研究（N=50）中，涉及两种对话场景（人际关系和职业支持），情境感知代理在感知的人性、流畅性和互动性方面均优于静态节奏对照组，支持更深入的自我披露和更高的参与度。在职业支持场景中，CA在感知的倾听质量和情感信任方面表现更高。这项工作展示了如何将人类对话中的情境感知节奏洞察应用于更 empathic 的人机交流设计。
英文摘要：
In human conversation, empathic dialogue requires nuanced temporal cues indicating whether the conversational partner is paying attention. This type of "active listening" is overlooked in the design of Conversational Agents (CAs), which use the same pacing for one conversation. To model the temporal cues in human conversation, we need CAs that dynamically adjust response pacing according to user input. We qualitatively analyzed ten cases of active listening to distill five context-aware pacing strategies: Reflective Silence, Facilitative Silence, Empathic Silence, Holding Space, and Immediate Response. In a between-subjects study (N=50) with two conversational scenarios (relationship and career-support), the context-aware agent scored higher than static-pacing control on perceived human-likeness, smoothness, and interactivity, supporting deeper self-disclosure and higher engagement. In the career support scenario, the CA yielded higher perceived listening quality and affective trust. This work shows how insights from human conversation like context-aware pacing can empower the design of more empathic human-AI communication.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
