Why human mistakes hurt more? Emotional responses in human-AI errors

Understanding user responses to AI versus human errors is crucial, as they shape trust, acceptance, and interaction outcomes. This study investigates the emotional dynamics of human-AI interactions by examining how agent identity (human vs. AI) and error severity (low vs. high) influence negative emotional reactions. Using a 2 × 2 factorial design (N = 250), the findings reveal that human agents consistently elicit stronger negative emotions than AI agents, regardless of error severity. Moreover, perceived experience moderates this relationship under specific conditions: individuals who view AI less experienced than humans exhibit stronger negative emotions toward human errors, while this effect diminishes when AI is perceived as having higher experience. However, perceived agency does not significantly influence emotional responses. These findings highlight the critical role of agent identity and perceived experience in shaping emotional reactions to errors, adding insights into the dynamics of human-AI interactions. This research shows that developing effective AI systems needs to manage user emotional responses and trust, in which perceived experience and competency play pivotal roles in adoption. The findings can guide the design of AI systems that adjust user expectations and emotional responses in accordance with the AI's perceived level of experience.