CORTEXA
← Browse
arxivcs.HC2026-07-15

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

Ann-Kareen Gedeus, Jack Good, Nadine Wagener, Angelique Taylor

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.

View free PDFSource page

Related papers

arxivcs.MMcs.CLcs.HCeess.SP2026-07-19

EII-SCL: Harnessing Emotional Inertia for Multimodal Emotion Recognition in Conversation

Zilong Huang, Kong Aik Lee, Chong-Xin Gan, Zezhong Jin, Ruichen Zuo, Man-Wai Mak

Multimodal emotion recognition in conversation (MERC) achieves accurate predictions by integrating multimodal and contextual information in dialogues. While current MERC approaches focus on modeling complex contextual dependencies in conversation, they often overlook the impact o…

View free PDFSource page
arxivcs.HC2026-06-26

Functional outcomes and naturalistic engagement with a purpose-built conversational AI for mental health (Ash)

Kristen M. Van Swearingen, Thomas D. Hull, Karthik V. Sarma, Caitlin A. Stamatis

Background: Conversational AI chatbots designed for mental health may offer an accessible, scalable avenue for supporting psychological well-being, yet prior evaluations have largely focused on clinical symptom reduction rather than broader indicators of day-to-day functioning, a…

View free PDFSource page
arxivcs.HCcs.AIcs.CY2026-06-28

LLMography: Transforming Human-AI Conversations into Traceability, Oversight, and Auditability Indicators

Mohammed Bousmah

The growing use of Large Language Models (LLMs) in education, software engineering, academic writing, and technical documentation raises a key question: how can we evaluate not only AI-assisted outputs, but also the interaction process that produced them? Current debates often fo…

View free PDFSource page
arxivcs.HC2026-07-16

Conversational Tactile Data Interfaces: Co-Designing Accessible Data Experiences with Blind Users Using Refreshable Tactile Displays and Conversational AI

Samuel Reinders, Munazza Zaib, Bongshin Lee, Ingrid Zukerman, Matthew Butler, Thien Autran, et al.

Combining refreshable tactile displays (RTDs) with conversational AI offers a promising approach to accessible data visualization for people who are blind or have low vision (BLV). However, it remains an open question how these modalities should be integrated to support accessibl…

View free PDFSource page
arxivcs.HCcs.CY2026-06-29

Anthropomorphism in AI Companion Communities: Age, Gender, and Emotional Correlates

Afia Mubashir, Boden Moraski, Stephanie Choi, Rose E. Guingrich

Artificial intelligence (AI) systems are increasingly integrated into daily life, with millions now using AI chatbots built on Large Language Models (LLMs) for companionship. Both humanlike AI qualities and user predispositions to anthropomorphize relate to social consequences, s…

View free PDFSource page