Understanding Critical Thinking in Generative Artificial Intelligence Use: Development, Validation, and Correlates of the Critical Thinking in AI Use Scale

Critical thinking in AI use involves three key behaviors: verifying AI sources and content, understanding how AI models work and their limitations, and reflecting on broader implications of AI reliance—rather than simply accepting AI outputs at face value.

Most people lack adequate critical thinking skills for AI: The research developed and validated a measurement scale across 1,341 participants, suggesting this is an emerging competency gap as generative AI becomes embedded in everyday work and learning environments.

Critical thinking in AI use predicts better fact-checking accuracy: Study 6 found that people with higher critical thinking scores demonstrated "greater veracity judgement accuracy" when evaluating AI-generated information in real chatbot interactions, directly linking this skill to better decision-making.

Personality traits correlate with AI critical thinking: The research identified that openness and extraversion are positively associated with critical thinking in AI use, suggesting some individuals may naturally be more inclined to question AI outputs, while others may need targeted training.

Critical thinking in AI use drives responsible AI practices: Higher scores on the scale predicted "deeper reflection about responsible AI" and more frequent use of verification strategies, indicating that developing this skill could help users engage more responsibly with AI tools.