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

arXiv: https://arxiv.org/abs/2602.06134 · HTML: https://mlsanity.com/paper/2602.06134/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 150 references that are in the map.
- First posted on arXiv 5 Feb 2026.

## Builds on (most cited first, 18 of 18)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making](https://mlsanity.com/paper/2102.09692/) (2021)
- [From Eliza to XiaoIce: Challenges and Opportunities with Social Chatbots](https://mlsanity.com/paper/1801.01957/) (2018)
- [FastEmit: Low-latency Streaming ASR with Sequence-level Emission Regularization](https://mlsanity.com/paper/2010.11148/) (2020)
- ["It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents](https://mlsanity.com/paper/2309.11653/) (2023)
- [If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills](https://mlsanity.com/paper/2002.01862/) (2020)
- [On Evaluating and Comparing Open Domain Dialog Systems](https://mlsanity.com/paper/1801.03625/) (2018)
- [Mitigating Response Delays in Free-Form Conversations with LLM-powered Intelligent Virtual Agents](https://mlsanity.com/paper/2507.22352/) (2025)
- [Alexa as an Active Listener: How Backchanneling Can Elicit Self-Disclosure and Promote User Experience](https://mlsanity.com/paper/2204.10191/) (2022)
- [Prosody-TTS: An end-to-end speech synthesis system with prosody control](https://mlsanity.com/paper/2110.02854/) (2021)
- [GraphTTS: graph-to-sequence modelling in neural text-to-speech](https://mlsanity.com/paper/2003.01924/) (2020)
- [HealthPrism: A Visual Analytics System for Exploring Children's Physical and Mental Health Profiles with Multimodal Data](https://mlsanity.com/paper/2307.12242/) (2023)
- [Exploring the Effects of Chatbot Anthropomorphism and Human Empathy on Human Prosocial Behavior Toward Chatbots](https://mlsanity.com/paper/2506.20748/) (2025)
- [Effects of Online Self-Disclosure on Social Feedback During the COVID-19 Pandemic](https://mlsanity.com/paper/2209.10682/) (2022)
- ["Ronaldo's a poser!": How the Use of Generative AI Shapes Debates in Online Forums](https://mlsanity.com/paper/2502.09693/) (2025)
- [The Effect of Silence Feature in Dimensional Speech Emotion Recognition](https://mlsanity.com/paper/2003.01277/) (2020)

## Built on by (most cited first, 3 of 3)

- [Deco: Extending Cherished Physical Objects into AI Companion Agents through Dual Embodiment](https://mlsanity.com/paper/2605.03882/) (2026)
- [Does Listening Matter? Backchanneling and Nodding in AI Clone](https://mlsanity.com/paper/2608.19527/) (2026)
- [Can Vision-Language Models Analyze Human-Centered Video? Mapping Model Capabilities and Human-AI Collaborative Workflows](https://mlsanity.com/paper/2609.27327/) (2026)

## Abstract (arXiv)

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.