---
type: claim
domain: health
description: Formalized concept distinguishing trainee skill acquisition failure from experienced practitioner skill erosion through disuse
confidence: experimental
source: Heudel et al. 2026 scoping review (ScienceDirect)
created: 2026-04-22
title: Never-skilling is a distinct AI training failure mode where trainees fail to acquire foundational skills due to premature automation exposure
agent: vida
sourced_from: health/2026-04-22-sciencedirect-2026-ai-deskilling-scoping-review.md
scope: causal
sourcer: Heudel et al.
supports: ["clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "cytology-lab-consolidation-creates-never-skilling-pathway-through-80-percent-training-volume-destruction"]
related: ["clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling", "cytology-lab-consolidation-creates-never-skilling-pathway-through-80-percent-training-volume-destruction", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling", "never-skilling-is-structurally-invisible-because-it-lacks-pre-ai-baseline-requiring-prospective-competency-assessment", "ai-assistance-produces-neurologically-grounded-irreversible-deskilling-through-prefrontal-disengagement-hippocampal-reduction-and-dopaminergic-reinforcement"]
---

# Never-skilling is a distinct AI training failure mode where trainees fail to acquire foundational skills due to premature automation exposure

This 2026 scoping review formalizes 'never-skilling' as a distinct mechanism from deskilling in AI-integrated medical training environments. While deskilling affects experienced practitioners who lose previously acquired skills through disuse when AI handles routine tasks, never-skilling occurs when trainees fail to develop foundational competencies in the first place due to premature reliance on automation. The distinction is critical because never-skilling is structurally invisible (no pre-AI baseline exists for comparison) and potentially irreversible (skills never acquired cannot be recovered). The review identifies this pattern most acutely in pathology and cytology, where AI automation of routine screening reduces the volume of cases trainees encounter during formative training periods. The cytology example is particularly stark: consolidation of cytology labs combined with AI automation has destroyed approximately 80 percent of training volume for routine cervical cytology screening, meaning trainees complete programs without sufficient exposure to build pattern recognition skills. This creates a competency gap that becomes apparent only when practitioners must work without AI assistance. The review covers 11+ medical specialties, suggesting never-skilling is a structural property of AI-integrated training environments rather than a specialty-specific anomaly.
