Refers to concerns that widespread use of artificial intelligence (AI), automation, and decision-support systems may prevent learners, workers, or professionals from fully developing foundational knowledge, judgment, or competencies because the technology performs too much of the underlying cognitive work.  Never-skilling describes situations in which individuals may never acquire certain capabilities because AI systems reduce the need to practice, struggle through, or independently perform essential tasks.

The concept is increasingly discussed in healthcare, education, software development, and other knowledge-intensive professions. In medicine, for example, educators and clinicians are raising concerns that heavy reliance on AI-assisted diagnosis, documentation, or clinical decision support tools could weaken development of clinical reasoning, pattern recognition, and diagnostic judgment among medical students and early-career practitioners.

Supporters of AI-assisted work argue that AI can improve efficiency, reduce routine burdens, and augment human performance. Critics caution that overreliance on AI may create long-term risks if workers become dependent on systems they do not fully understand or cannot effectively evaluate when errors occur.

The term is not yet standardized across research literature but reflects growing debates about how AI may reshape professional learning, expertise development, and workforce preparation.

See related term, deskilling, which refers to concerns in which workers gradually lose skills they once possessed: Deskilling | Learn & Work Ecosystem Library