The AI Competence Framework Landscape: A Comparative Analysis of Frameworks for Education and Workforce Development

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The rapid growth of Artificial Intelligence (AI) competence frameworks has made it increasingly difficult for educators and workforce-development professionals to identify those suited to their purposes. This study maps and compares institutionally endorsed frameworks for education, professional development, and workforce training. From 76 candidates, 24 frameworks and reference models were shortlisted, and 16 met the criteria for comparative analysis. These were examined in terms of their target populations, intended uses, competence structures, progression models, implementation guidance, and coverage of ten common AI competence areas. The findings show that all 16 frameworks substantively address AI Use and Interaction, Output Evaluation and Verification, and Domain or Workflow Application, while 15 address AI Foundations and Ethics and Responsible AI. Greater variation was found in Problem Formulation, Data Competence, and AI Creation and Development, as well as in the provision of proficiency levels, learning outcomes, pedagogical guidance, and assessment. The comparison indicates that AI competence brings together foundational understanding, practical application, critical judgement, and responsible human engagement, with further requirements shaped by the population and context. The resulting map provides a reference point for selecting and adapting frameworks for curricula, vocational education and training, professional upskilling, and workforce transformation.