Oct 2026· The European Educational Researcher· 0 citations· 38 references
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.
Analysis of how teachers perceive their AI competencies, the purposes for which they apply AI in teaching practice, and how confident they feel when integrating intelligent models into their teaching practices shows that teachers primarily associate educational technology with AI, use it mainly for lesson preparation, and report psychological effects such as enhanced confidence, motivation, and innovation.
Maja Veljković Michos, Valentina B. Bošković Marković, Milica Čolović· SINTEZA· 0 citations
This study presents an abductive analysis of interview data from 13 Finnish teachers involved in national AI education development projects and conceptualises a potential synergy between the domains where students' self‐determined, informed engagement with AI is placed at the centre of educational efforts in AI education.
Janne Fagerlund, Pekka Mertala, Jukka Lehtoranta et al.· Journal of Computer Assisted...· 0 citations
As artificial intelligence (AI) becomes increasingly embedded in educational systems, teachers face new expectations for engaging AI as part of their instructional work and professional responsibilities. This paper argues that clarifying AI competencies for teaching requires more than listing skills: it requires a coherent account of (1) what these competencies involve, (2) how they are enacted in practice, and (3) what evidence can support teacher learning and professional judgment over time. We conceptualize these integrated capacities as AI competencies for teaching and distinguish them from broader notions of AI literacy by emphasizing application, professional judgment, and context sensitivity. Drawing on trends in how AI is reshaping teaching, we introduce a framing of AI’s roles in education (as tool, content, and context) to specify the instructional demands to which our current understanding of teaching competencies must respond. We then summarize features of existing competency frameworks to surface shared foundations and key tensions related to ethics, technical depth, professional agency, and domain specificity. We describe six interrelated domains of AI competencies for teaching, spanning AI content knowledge, AI technologies, pedagogical integration, ethical awareness, professional judgment and agency, and openness to continuous learning. To illustrate what these competencies look like in use, we present a scenario that makes teachers’ instructional decision-making visible when AI is present. Finally, we discuss progress-oriented, evidence-centered approaches, including implications for assessment, that can support teachers’ development of these competencies. This work ultimately positions AI competencies for teaching as central to sustaining ethically grounded, responsible, and professionally empowering teaching in AI-mediated educational environments.
Suggested citation: Ober, T. M., Tenison, C., Phelps, G., Renfrow-Symon, E., Mikeska, J., & Dean, V. J. (in press). AI competencies for teaching: From conceptual frameworks to enacted practices. ETS Research Report Series. https://doi.org/10.64634/z5j3ts49
Teresa M. Ober, Caitlin Tenison, Geoffrey Phelps et al.· ETS Research Report Series· 0 citations
The findings indicate that primary school teachers’ AI literacy is not limited to technical proficiency but constitutes an integrated professional competence characterised by an “awareness-driven, knowledge-supported, competence-oriented, and ethics-guided” mechanism.
Yang-Hua Qiu· International Educational Re...· 0 citations
This scoping review maps how artificial intelligence (AI) is being connected to teacher competence in recent research. The review was based on 33 peer-reviewed articles published in 2022–2026 and identified through a bounded Web of Science search. Its purpose was not to evaluate intervention effectiveness, but to describe the extent, range, and nature of the available evidence on AI, machine learning (ML), and learning analytics (LA) in teacher assessment, modeling, and professional development within this indexed corpus. The mapped literature suggests two broad lines of work. One uses AI, ML, LA, and computational psychometrics to assess teaching practice, model teacher development, or measure AI-TPACK-related competence. The other treats AI as part of what teachers themselves need to know and do. Instruments represented in the corpus, such as TAICS, T-GAIC, AI-SRLS, AI-TPACK, and RAIS, broaden the concept of competence to include AI literacy, self-efficacy, ethical reasoning, readiness, and teacher–AI co-teaching. The review found frequent use of supervised machine learning, regularized regression, EFA, CFA, SEM, and learning analytics, but limited reported use of explainable AI, subgroup fairness analysis, multimodal validation, and longitudinal designs. Quality appraisal indicated stronger support for measurement-structure claims than for causal claims about professional-development effectiveness or high-stakes AI deployment.
N. Baizhanov, Batyr Sharimbayev, Zhairan Churbanova et al.· Frontiers in Artificial Inte...· 0 citations
The rapid integration of Artificial Intelligence (AI) into educational settings has generated significant scholarly debate about its implications for teacher professionalism. This qualitative study explores how Moroccan pre-service EFL teachers at two teacher education institutions, ENS Rabat and ESEF Kénitra, perceive the impact of AI tools such as ChatGPT, Gemini, Grammarly, and others on their pedagogical decision-making and professional autonomy. Drawing on Priestley et al.’s (2015) ecological model of teacher agency and Braun and Clarke’s (2006) thematic analysis framework, data were collected from 42 purposively sampled participants, drawn from the wider target population of pre-service EFL teachers engaged in practicum-based training at Moroccan teacher education institutions (CRMEF, ENS, and ESEF centers), through an open-ended qualitative survey. Four major themes emerged from the analysis: (a) AI as a pedagogical co-pilot, (b) convenience versus critical engagement, (c) negotiated teacher agency, and (d) emerging professional identities in the AI era. The findings indicate that while AI enhances instructional efficiency and builds confidence among novice teachers, uncritical reliance on AI-generated content risks limiting opportunities for independent pedagogical reasoning and reflective practice. Crucially, participants did not perceive AI as eliminating teacher agency; rather, they described agency as continuously negotiated through technology-mediated interactions. The study advances a conceptual distinction between “teaching with AI”, whereby teachers employ AI as a supportive resource while retaining professional judgment, and “teaching through AI,” whereby instructional decisions are progressively delegated to algorithmic systems. Implications for AI literacy development and critical pedagogy within Moroccan teacher education programs are discussed. The article concludes by discussing implications for AI literacy development and critical pedagogy within Moroccan teacher education, and offers recommendations for teacher education curricula, institutional policy, and future research on AI-mediated professional development.
Ali Bekou, Mohamed Benmhamed· International journal of res...· 0 citations
