Ecce Histor_IA. The role of narratives, usage contexts, and tasks in influencing attitudes towards artificial intelligence

As artificial intelligence (AI) technologies are integrated into decision-making processes, understanding how users develop trust in these systems is essential. While prior research has examined isolated factors such as transparency or system performance, less is known about how trust is shaped by the combined influence of narrative framing, task type, application domain, and individual user characteristics. This study investigates these dimensions using a preregistered online experiment (N = 280) employing a 3 (narrative: human-centered, technical, control)×2 (task type: generative, recommendation)×3 (domain: healthcare, insurance, relationships) mixed factorial design. Participants were presented with a narrative introduction to an AI system and evaluated its trustworthiness, perceived utility, and likelihood of adoption across multiple scenarios. Results showed that narrative framing had minimal impact on trust, suggesting that brief descriptions may be insufficient to shape user attitudes. In contrast, both domain and task type significantly influenced trust: AI systems were trusted more in insurance and healthcare than in personal relationships, and recommendation systems consistently outperformed generative ones. Additionally, a generally positive attitude towards AI predicted greater trust, while the effect of AI literacy was more nuanced. These findings highlight the importance of contextual and individual factors in fostering trust in AI.