Hear You in Silence: Designing for Active Listening in Human Interaction with Conversational Agents Using Context-Aware Pacing

TL;DR

This paper explores how conversational agents can improve empathic dialogue by dynamically adjusting response pacing based on context, leading to more human-like and engaging interactions.

Contribution

It introduces five context-aware pacing strategies for conversational agents and demonstrates their effectiveness through a user study.

Findings

Higher perceived human-likeness and smoothness with context-aware pacing

Increased user engagement and self-disclosure

Enhanced perceived listening quality and trust in career scenarios

Abstract

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.