# Affective Conversational Agents: Understanding Expectations and Personal Influences

arXiv: https://arxiv.org/abs/2310.12459 · HTML: https://mlsanity.com/paper/2310.12459/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 19 Oct 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence](https://mlsanity.com/paper/1903.09588/) (2019)
- [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021)
- [Towards Emotion-Aware Agents For Negotiation Dialogues](https://mlsanity.com/paper/2107.13165/) (2021)
- [An Ethical Framework for Guiding the Development of Affectively-Aware Artificial Intelligence](https://mlsanity.com/paper/2107.13734/) (2021)

## Built on by (most cited first, 6 of 6)

- [From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversations](https://mlsanity.com/paper/2502.11451/) (2025)
- [Dehumanizing Machines: Mitigating Anthropomorphic Behaviors in Text Generation Systems](https://mlsanity.com/paper/2502.14019/) (2025)
- [SENSE-7: Taxonomy and Dataset for Measuring User Perceptions of Empathy in Sustained Human-AI Conversations](https://mlsanity.com/paper/2509.16437/) (2025)
- [Understanding Communication Preferences of Information Workers in Engagement with Text-Based Conversational Agents](https://mlsanity.com/paper/2410.20468/) (2024)
- [ChatGPT on the Road: Leveraging Large Language Model-Powered In-vehicle Conversational Agents for Safer and More Enjoyable Driving Experience](https://mlsanity.com/paper/2508.08101/) (2025)
- [Cross-Modal Emotion Transfer for Emotion Editing in Talking Face Video](https://mlsanity.com/paper/2604.07786/) (2026)

## Abstract (arXiv)

The rise of AI conversational agents has broadened opportunities to enhance human capabilities across various domains. As these agents become more prevalent, it is crucial to investigate the impact of different affective abilities on their performance and user experience. In this study, we surveyed 745 respondents to understand the expectations and preferences regarding affective skills in various applications. Specifically, we assessed preferences concerning AI agents that can perceive, respond to, and simulate emotions across 32 distinct scenarios. Our results indicate a preference for scenarios that involve human interaction, emotional support, and creative tasks, with influences from factors such as emotional reappraisal and personality traits. Overall, the desired affective skills in AI agents depend largely on the application's context and nature, emphasizing the need for adaptability and context-awareness in the design of affective AI conversational agents.