AI In Dental Education: Upskilled, Differently Skilled, or Never Skilled?

BACKGROUND: Artificial intelligence (AI) is being integrated rapidly into dental education, yet frameworks to guide its use while safeguarding foundational competence remain underdeveloped. Recent work in medical education has highlighted the risk of AI-induced 'never-skilling', whereby learners fail to develop core reasoning because AI repeatedly performs the cognitive work required for competence development. AIM: To apply the concepts of 'upskilled', 'differently skilled' and 'never skilled' to European dental education and explore their implications for curriculum design, assessment and governance. MAIN ARGUMENTS: 'Never skilled' refers not to poor AI literacy, but to progression through training without acquiring the foundational biomedical, behavioural and clinical knowledge, and independent reasoning expected of a safe clinician. In contrast, 'upskilled' individuals extend existing competencies through critical AI use, while 'differently skilled' individuals possess strong foundational competence and collaborate effectively with AI through contextual judgement, educational expertise and ethical reasoning. AI is not inherently detrimental to learning; its impact depends on when and how it is introduced. A staged approach that prioritises AI-independent competence before guided AI integration and supervised collaboration is proposed. CONCLUSIONS: Dental schools should protect foundational competence through staged AI integration, explicit AI-independent milestones, aligned assessment strategies, and separate governance frameworks for educational and clinical AI. The distinction between upskilled, differently skilled, and never skilled offers a practical framework for ensuring AI augments rather than replaces competence development.