Exploring the role of metacognitive abilities and trust in interaction with Generative AI
Muskan Singh, Horia A. Maior, Max L. Wilson, Jérémie Clos · 2025
Generative AI enables users to explore limitless possibilities, but its open-ended nature introduces ambiguity that differs from traditional GUIs. As general users integrate GenAI into their personal and professional workflows, challenges around prompting and usability have emerged. This study examines these challenges through the lens of metacognition, specifically the metacognitive abilities of monitoring and control - self-awareness and task decomposition, during intent-based interactions with a GenAI tool, exploring how these abilities influence control over three types of tasks. Our findings reveal that self-awareness is more critical in simpler tasks, while task decomposition becomes crucial as task complexity and output novelty increase. Additionally, we investigate underlying trust in AI, finding contradictions between user’s metacognitive awareness and their faith across tasks, revealing the role of output evaluation with domain knowledge. Based on these insights, we offer recommendations for enhancing metacognitive support in GenAI tools and suggest directions for future research.