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.

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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

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article

Be AIware! An AI Competency Model for K-12 Education

Jan Μ. Pawlowski, Lana Sattelmaier
2 citations
Social Sciences & Humanities Open
Online Learning and Analytics
3.39
article

Be AIware! An AI Competency Model for K-12 Education

Jan Μ. Pawlowski, Lana Sattelmaier
article en
2 citations

Abstract

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.

Social Sciences & Humanities OpenVol. 12
Ruhr West University of Applied Sciences (DE), University of Jyväskylä (FI)
European Commission, Erasmus+
Quality Education
Openalex Percentile: Top 6%
Online Learning and Analytics
3.39
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Be AIware! An AI Competency Model for K-12 Education — Jan Μ. Pawlowski, Lana Sattelmaier · Social Sciences & Humanities Open (2025) | TGRS Research Map | TGRS