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Social anxiety (SA) has become increasingly prevalent. Traditional coping strategies often face accessibility challenges. Generative AI (GenAI), known for their knowledgeable and conversational capabilities, are emerging as alternative tools for mental well-being. With the increased integration of GenAI, it is important to examine individuals’ attitudes and trust in GenAI chatbots’ support for SA. Through a mixed-method approach that involved surveys (n = 159) and interviews (n = 17), we found that individuals with severe symptoms tended to trust and embrace GenAI chatbots more readily, valuing their non-judgmental support and perceived emotional comprehension. However, those with milder symptoms prioritized technical reliability. We identified factors influencing trust, such as GenAI chatbots’ ability to generate empathetic responses and its context-sensitive limitations, which were particularly important among individuals with SA. We also discuss the design implications and use of GenAI chatbots in fostering cognitive and emotional trust, with practical and design considerations. 

We observe spin transfer within a nondegenerate heteronuclear spinor atomic gas comprising a small 7Li population admixed with a 87Rb bath, with both elements in their F = 1 hyperfine spin manifolds. Prepared in a nonequilibrium initial state, the 7Li spin distribution evolves through incoherent spin-changing collisions toward a steady-state distribution. We identify and measure the cross sections of all three types of spin-dependent heteronuclear collisions, namely the spin-exchange, spin-mixing, and quadrupole-exchange interactions, and find agreement with predictions of heteronuclear 7Li - 87Rb interactions at low energy. Moreover, we observe that the steady state of the 7Li spinor gas can be controlled by varying the composition of the 87Rb spin bath with which it interacts.