We present Empathic Prompting, a novel framework for multimodal human-AI interaction that enriches Large Language Model (LLM) conversations with implicit non-verbal context. The system integrates a commercial facial expression recognition service to capture users' emotional cues and embeds them as contextual signals during prompting. Unlike traditional multimodal interfaces, empathic prompting requires no explicit user control; instead, it unobtrusively augments textual input with affective information for conversational and smoothness alignment. The architecture is modular and scalable, allowing integration of additional non-verbal modules. We describe the system design, implemented through a locally deployed DeepSeek instance, and report a preliminary service and usability evaluation (N=5). Results show consistent integration of non-verbal input into coherent LLM outputs, with participants highlighting conversational fluidity. Beyond this proof of concept, empathic prompting points to applications in chatbot-mediated communication, particularly in domains like healthcare or education, where users' emotional signals are critical yet often opaque in verbal exchanges.
@article{arxiv.2510.20743,
title = {Empathic Prompting: Non-Verbal Context Integration for Multimodal LLM Conversations},
author = {Lorenzo Stacchio and Andrea Ubaldi and Alessandro Galdelli and Maurizio Mauri and Emanuele Frontoni and Andrea Gaggioli},
journal= {arXiv preprint arXiv:2510.20743},
year = {2026}
}