Empirically Grounding and Refining a Model for Teachers’ AI-Related Competences: Insights from Expert Interviews
TL;DR
This study examines and refines an existing competence model by incorporating perspectives from diverse stakeholder groups and elaborated to provide an empirically grounded, didactically actionable framework for teacher education and future research.
Abstract
The growing adoption of artificial intelligence (AI) in education is reshaping teaching and learning in schools, while also increasing the professional demands on teachers. To support effective and responsible AI use in schools, a clear and empirically grounded understanding of teachers’ AI-related competences is required. This study examines and refines an existing competence model by incorporating perspectives from diverse stakeholder groups. Seven semi-structured expert interviews were conducted with representatives from computer science, educational science, subject-specific didactics, schools, industry, and education policy. The data were analysed using structured qualitative content analysis [LM1.1]to identify relevant competence dimensions and their interrelations. The findings largely confirm the overall structure of the model but place particular emphasis on the central role of AI didactics. AI-related competences are not limited to technical understanding or tool use but crucially involve the ability to design, implement, and reflect on learning processes with and about AI. This includes selecting meaningful use cases, adapting instructional formats, addressing ethical and societal implications, and developing new approaches to assessment and classroom practice in response to AI. In addition, personal and social dispositions for effective AI use and teaching about AI in everyday school practice function as enabling conditions for the enactment of these competences. Based on these results, the model was elaborated to provide an empirically grounded, didactically actionable framework for teacher education and future research.
