Our research investigates how users' social contexts influence their perceptions of AI emotional support chatbots.

• VenueACM CHI 2026 in Barcelona
• Paperdoi.org/10.1145/3772363.3798800
• LabNCCU SITES (Social Intelligence Technologies Experimental Studio)
• RoleCo-author — quantitative analysis & writing
• FieldHuman-Computer Interaction, AI Emotional Support, Mental Health
• StatusTo be disclosed

Abstract

AI emotional support chatbots (e.g., LLMs) promise accessible mental health support, yet how users' social contexts influence their perceptions remains underexplored. We conducted a 10-day diary study (N=24) with an LLM-based chatbot, followed by in-depth interviews (n=8). Using grounded theory, we identified a "support gap" framing: participants with limited social support (e.g., fear of burdening others, unsatisfying relationships) evaluated the AI more positively, viewing it as a judgment-free resource. In contrast, those with strong support networks were more critical, using high-quality human empathy as their reference standard. Our findings suggest that AI evaluation is relative to users' pre-existing social experiences rather than system quality alone. We invite the CHI community to consider how social context should inform the design and ethical evaluation of AI emotional support systems.

Research Context

This work was conducted at the Social Intelligence Technologies Experimental Studio (SITES), a lab/design studio led by Yoyo Tsung-Yu Hou at National Chengchi University (NCCU), Taipei, Taiwan. The lab focuses on how interactive media and technology can be leveraged to create meaningful user experiences, combining design thinking, HCI methodology, and emerging technology exploration.

Why It Matters — Business & Product Impact

This study shows that users don't judge an AI support product on its features alone—they judge it relative to the human support they already have. People with weaker social networks valued the AI most, while those with strong support were the harshest critics. For a wellness or mental-health product, that reframes the market: the highest-value segment isn't the mass market but users experiencing a support gap. It points to sharper positioning, segmented onboarding, and honest expectation-setting as levers for adoption and retention—turning a qualitative insight into a go-to-market and product-strategy decision.