Users seek both emotional and practical support from AI systems
Affective and Functional Needs in AI Use: User Patterns and Their Relation to Real-Life Support and Well-Being
Human-Computer Interaction
Summary
People use conversational AI in different ways, sometimes for help with tasks and sometimes for emotional support. The authors found many people use AI for both reasons at the same time. They created a new way to measure how AI meets these different needs, showing that AI use affects real-life social support and well-being. This means AI can be designed to better match how different people want to use it.
What this means in practice
- •For ai product designers: Create AI systems tailored to users who seek both emotional comfort and functional assistance by using the AI-Support Scale for user profiling.$Commercial implications: This scale enables AI products to target diverse user support needs, improving engagement and satisfaction in commercial AI applications.
- •For mental health app developers: Incorporate affective and functional support measures from this study to enhance digital tools that provide emotional and informational assistance.
Authors
Jessy Wang-Sun, Estelle Yiyuan Sun, Hualin Zhang, Zihan Xu
Abstract
Conversational AI systems are increasingly used to fulfill both functional and affective needs, yet how users perceive the AI-adoption effects varies. Existing research tends to treat these two needs as binary categories and to classify users by a single dominant need. However, this approach fails to capture users who engage with AI for both affective and functional purposes simultaneously, leaving it unclear how these needs combine in real-world AI use. To capture the multifaceted nature of users' AI engagement, we developed the AI-Support measurement with 1,090 participants, grounded in social support theory, which evaluates four dimensions of perceived AI support: emotional/esteem, social companionship, informational, and instrumental support. This study provided (1) an AI-Support Scale developed to measure the perceived support types from human-AI interaction, (2) evidence that users might adopt AI with a high level of both functional and affective use, and (3) exploratory indicators of users' real-life social support and well-being, enabling descriptive results aligning with theoretical expectations. Overall, this study highlights the importance of understanding individual differences in AI affordance use, enabling AI systems to be designed in alignment with different user adoption profiles rather than assuming uniform benefits across all users.