Shortcut to Knowledge or Shortcut to Thinking? Investigating AI-Induced Metacognitive Laziness in Future Doctors

Abstract

Background: The rapid expansion of artificial intelligence (AI) and machine learning has transformed industries, including education and healthcare. In medical education, AI is increasingly used for personalized learning and clinical decision-making. However, growing reliance on AI may contribute to metacognitive laziness, where students engage less in critical thinking and self-regulation. Objective: This study examines the relationship extent of AI reliance in medical students and its relationship with metacognitive laziness. Methods: The study involved medical and dental students, with data collected via a four-point Likert scale-based questionnaire. Content validity was ensured by expert ratings on relevance and clarity, and reliability was determined using Cronbach’s alpha. Descriptive statistics with median response category were used to describe students’ AI reliance, and Spearman’s rank correlation was used to analyze the relationship between AI reliance and metacognitive laziness, with a significance level set at p = 0.05. Results: The initial 47-item questionnaire was refined to 36 items, with an S-CVI/Ave of 0.88 and a CCA of 90%. Cronbach’s alpha was 0.936, indicating excellent reliability. The survey revealed that 74.4% of students relied on AI for learning, with 61.3% reporting decreased motivation for independent analysis and 62.4% expressing concerns about its impact on future patient care. Spearman’s rank correlation showed a moderate positive relationship (ρ = 0.621, p = 0.000). Conclusion: The increasing reliance on AI among medical students is associated with metacognitive laziness, emphasizing the need for careful AI integration to promote independent learning and critical thinking.

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