# Exploring the Effect of Robotic Embodiment and Empathetic Tone of LLMs on Empathy Elicitation

arXiv: https://arxiv.org/abs/2503.20518 · HTML: https://mlsanity.com/paper/2503.20518/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [How should my chatbot interact? A survey on human-chatbot interaction design](https://mlsanity.com/paper/1904.02743/) (2019)
- [Harnessing Large Language Models' Empathetic Response Generation Capabilities for Online Mental Health Counselling Support](https://mlsanity.com/paper/2310.08017/) (2023)
- [Towards Designing a ChatGPT Conversational Companion for Elderly People](https://mlsanity.com/paper/2304.09866/) (2023)
- [Robot Vulnerability and the Elicitation of User Empathy](https://mlsanity.com/paper/2401.02684/) (2024)

## Abstract (arXiv)

This study investigates the elicitation of empathy toward a third party through interaction with social agents. Participants engaged with either a physical robot or a voice-enabled chatbot, both driven by a large language model (LLM) programmed to exhibit either an empathetic tone or remain neutral. The interaction is focused on a fictional character, Katie Banks, who is in a challenging situation and in need of financial donations. The willingness to help Katie, measured by the number of hours participants were willing to volunteer, along with their perceptions of the agent, were assessed for 60 participants. Results indicate that neither robotic embodiment nor empathetic tone significantly influenced participants' willingness to volunteer. While the LLM effectively simulated human empathy, fostering genuine empathetic responses in participants proved challenging.