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Policy·G Space·Evidence-backed gain·Published 2026-07-13

Understanding critical thinking in generative artificial intelligence use: Development, validation, and correlates of the critical thinking in AI use scale

Generative AI tools are increasingly embedded in everyday work and learning, yet their fluency, opacity, and propensity to hallucinate mean that users must critically evaluate AI outputs rather than accept them at face value. The present research conceptualises critical thinking in AI use as a dispositional tendency to verify the source and content of AI-generated information, to understand how models work and where they fail, and to reflect on the broader implications of relying on AI. Across six studies ( N =…

TRV-2026-0136Peer-reviewedPermanent record — cite & verify
Understanding critical thinking in generative artificial intelligence use: Development, validation, and correlates of the critical thinking in AI use scale

"Chatbot robot" by Beantin webbkommunikation is licensed under CC BY-SA 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/2.0/.

The quick read

By May 2026, researchers had developed and validated a 13-item critical thinking in AI use scale across six studies totaling 1341 participants. The work defined the construct as verifying AI-generated information, understanding how models work and fail, and reflecting on implications of reliance, and confirmed a structure of Verification, Motivation, and Reflection.

The scale matters because it provides a standardized way to measure oversight of fluent but opaque generative AI outputs that can hallucinate, linking higher scores to more frequent verification and greater accuracy in judging veracity. What remains uncertain is how the measure performs outside the validation samples and whether training to raise these dispositions translates into sustained real-world safeguards.

Main points
  • Developed 13-item critical thinking in AI use scale across six studies with total N = 1341
  • Three-factor structure identified as Verification, Motivation, and Reflection with higher-order model confirmed
  • Scale showed internal consistency, sex invariance, and test-retest reliability
  • Higher scores predicted deeper reflection about responsible AI and more diverse verification behaviors
Gain

Higher critical thinking in AI use was associated with better detection of inaccurate information during a GPT-powered chatbot interaction, including use of more verification strategies.

The rundown

Study 1 generated and content-validated items, Study 2 supported the three-factor structure, and Studies 3 and 4 confirmed the higher-order model with strong factor loadings and convergent and discriminant validity evidence.

Study 5 examined stability over time and Study 6 provided criterion validity using an ecologically grounded paradigm involving a naturalistic GPT-powered AI chatbot fact-checking task.

Sources

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The debate