Be AIware! An AI Competency Model for K-12 Education
The rapid emergence of generative artificial intelligence (GAI) is reshaping educational contexts, creating an urgent need for K–12 systems to adapt. Teachers, in particular, face increasing demands to respond to AI-driven transformations in learning environments yet lack structured guidance on which competencies matter. This paper presents the AIware Competency Model, a comprehensive framework of 98 AI-related competencies designed for K–12 education, including emerging aspects of GAI. The model was developed using an Action Design Research (ADR) approach, structured across four iterative stages: (1) a systematic literature review and analysis of 14 existing AI competency models to identify conceptual gaps; (2) semi-structured interviews with AI experts and educators (n = 5) to inform initial model development; (3) three rounds of focus groups involving teachers, policymakers, and AI professionals (total n = 46) to refine and evaluate the competencies; and (4) a competency ranking process (n = 10) to assess perceived importance across educational roles. The AIware Competency Model addresses current fragmentation in AI education research by offering a unified, empirically grounded framework aligned with practical classroom needs. It enables mapping to existing curriculum standards and supports both teacher preparation and student readiness in a world increasingly shaped by AI. The model contributes to the field by combining theoretical synthesis with practitioner input, offering a robust foundation for future research, curriculum design, and policy development.
Authors
- Jan Μ. Pawlowski (ORCID: https://orcid.org/0000-0002-7711-1169)
- Lana Sattelmaier (ORCID: https://orcid.org/0009-0002-8579-3208)
Institutions
- Ruhr West University of Applied Sciences (DE)
- University of Jyväskylä (FI)
Publication Details
- Journal
- Social Sciences & Humanities Open
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1016/j.ssaho.2025.101838
- Citations
- 2
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 3.39
Funders
- European Commission
- Erasmus+