AI-induced never-skilling in medical education

Area of Science:

• Medical Education
• Artificial Intelligence
• Clinical Reasoning

Background:

• The rapid integration of artificial intelligence (AI) into medical training outpaces current educational frameworks.
• A significant risk is the potential for trainees to develop 'never-skilling' due to over-reliance on AI during formative clinical education.
• This contrasts with deskilling in experienced clinicians and mis-skilling from accepting AI errors as fact.

Purpose of the Study:

• To identify and address the risk of 'never-skilling' in medical trainees due to AI integration.
• To propose a framework for integrating AI into medical education while safeguarding the development of essential clinical reasoning skills.
• To highlight the need for a pedagogy research agenda and empirical investigation.

Main Methods:

• This perspective paper analyzes the potential impact of AI on medical trainee skill development.
• It draws upon established learning theory and empirical evidence from nonclinical settings.
• A three-phase competency-protective framework is proposed.

Main Results:

• Over-reliance on AI in early clinical education may impede the development of foundational reasoning skills necessary for independent practice.
• AI's educational impact is contingent on its implementation strategy and timing.
• A structured approach is needed to ensure AI complements, rather than compromises, trainee learning.

Conclusions:

• A proactive, phased approach to AI integration in medical training is crucial to prevent 'never-skilling'.
• The proposed framework emphasizes building AI-independent competency before supervised AI use.
• Further research is essential to inform policy and optimize AI's role in medical education.