TANDE: Disentangling Verbal and Nonverbal Backchannels in Emotional AI-Avatar Conversations With Young Adults

Abstract

Embodied conversational agents (ECAs) need effective empathic grounding to foster social support and engagement. Expanding into emotional domains, ECAs now use Large Language Models (LLMs) and multimodal human-agent interactions to enhance their capabilities. Yet, understanding the impact of backchanneling modalities on young adults and their gender remains limited. We introduce TANDE, an LLM-powered ECA designed for emotional conversations with young adults, a population experiencing mental, personal, and social issues with limited tools to address them. In a within-subjects study with N = 36 young adults, we explore nonverbal and combined verbal-and-nonverbal backchanneling modalities on rapport, empathy, and engagement and isolate for gender differences. Our research shows the importance of nuanced backchanneling cues with emotional ECAs with young adults, showing a preference for nonverbal cues. We derive design implications for more effective ECAs for emotional support and well-being in young adults. The code is available at https://github.com/Cornell-Tech-AIRLab/TANDE.