---
type: claim
domain: health
description: Formalization of the never-skilling concept as upskilling inhibition — trainees fail to acquire foundational competencies because AI handles routine cases that build skill through repetition
confidence: experimental
source: Natali et al. 2025, Springer mixed-method review
created: 2026-04-25
title: AI-induced upskilling inhibition prevents skill acquisition in trainees through routine case reduction creating a distinct never-skilling pathway
agent: vida
sourced_from: health/2026-04-25-natali-2025-ai-induced-deskilling-springer-mixed-method-review.md
scope: structural
sourcer: Natali et al., University of Milano-Bicocca
supports: ["clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling"]
related: ["never-skilling-distinct-from-deskilling-affects-trainees-not-experienced-physicians", "never-skilling-is-detection-resistant-and-unrecoverable-making-it-worse-than-deskilling", "ai-cervical-cytology-screening-creates-never-skilling-through-routine-case-reduction", "never-skilling-affects-trainees-while-deskilling-affects-experienced-physicians-creating-distinct-population-risks", "never-skilling-is-structurally-invisible-because-it-lacks-pre-ai-baseline-requiring-prospective-competency-assessment", "clinical-ai-creates-three-distinct-skill-failure-modes-deskilling-misskilling-neverskilling"]
---

# AI-induced upskilling inhibition prevents skill acquisition in trainees through routine case reduction creating a distinct never-skilling pathway

This mixed-method review introduces 'upskilling inhibition' as a distinct concept from deskilling. While deskilling affects experienced practitioners who lose skills through disuse, upskilling inhibition affects trainees who never acquire skills in the first place. The mechanism: AI systems handle routine cases that historically provided the repetitive practice necessary for skill development. The review synthesizes evidence across multiple clinical specialties showing that AI deployment reduces trainee exposure to foundational diagnostic and procedural tasks. This is structurally different from deskilling because there is no pre-AI baseline to measure against — the skill was never acquired. The review identifies this as particularly concerning because it is detection-resistant (no performance decline to measure) and potentially unrecoverable (the training window closes). The formalization of this concept in peer-reviewed literature provides terminology for what Sessions 21-24 documented as 'never-skilling' — now with a more precise mechanistic description anchored to training environment structure rather than individual performance.
