Exploring the Effect of Robotic Embodiment and Empathetic Tone of LLMs on Empathy Elicitation
This addresses the challenge of eliciting genuine empathy through AI agents for applications in social robotics and human-computer interaction, though it is incremental as it builds on existing empathy research.
The study investigated whether robotic embodiment or an empathetic tone in LLM-driven agents could increase empathy and willingness to volunteer for a fictional character in need, but found no significant effects on volunteer hours.
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.