AI-induced never-skilling in medical education

Abstract

The integration of artificial intelligence (AI) into medical training is accelerating faster than the educational frameworks designed to govern it. This Perspective identifies a risk that has received insufficient attention: that trainees who rely on AI during the early formative years of clinical education may fail to develop the foundational reasoning skills that safe, independent practice requires. We refer to this as ‘never-skilling’, distinguishing it from deskilling in experienced clinicians and from mis-skilling, in which uncritical acceptance of AI errors leads trainees to internalize flawed clinical knowledge as fact. Although direct evidence from medical training is absent, the concern is grounded in established learning theory and supported by early empirical signaling from nonclinical settings. AI is not inherently harmful to learning; its educational impact depends on how and when it is introduced. We propose a three-phase competency-protective framework: establishing AI-independent baseline competency, building critical calibration through structured pedagogy, and integrating AI under supervision in medical training. This is a pedagogy research agenda that requires further empirical investigation to ultimately inform future policy recommendations.