A robot with emotional intelligence might detect frustration in a user's tone and respond with supportive or motivational language, fostering trust and emotional bonds. This study explores users' expectations of emotional intelligence in social robots. Using the Wukong robot and the Hume.AI platform, we conducted a study with surveys and contextual interactions. Data were collected from 30 participants in the US and China. The preliminary findings indicate that emotional in-telligence in robots plays a critical role in meeting both functional and emotional needs, shaping expectations, and fostering trust. However, the expectations of users towards enhanced emotional intelligence are diversified.

Conditional driving automation (CDA, Level 3) vehicles will become widespread in the next few years. The driver only needs to take over the vehicle in an emergency and pay attention to the feedback from the vehicle. However, the mechanism by which feedback strategies and drivers' emotional factors influence drivers' levels of trust in automation is not clear. This study compares the effect of three road condition feedback strategies on drivers' trust in automation: no feedback, concentrated feedback (multiple feedback messages are presented together before the takeover request is issued) and real-time feedback (the road condition messages are fed in real time based on changes in the external environment). The mediating effect of anxiety in the relationship is also examined by a within-subjects factorial simulated driving experiment (N = 41). The anxiety score was significantly lower in the real-time feedback group than in the other two groups, but the level of trust in automation was significantly higher. Furthermore, an analysis of the mediating effect showed that anxiety plays a fully mediating role in the relationship between real-time feedback and the level of trust in automation. This finding will help professionals design more trustworthy feedback strategies from a psychological perspective.