The growing influence of artificial intelligence (AI) on everyday life and professional contexts has increased the awareness of the need for AI-related competences (European Parliament and Council, 2024). This need is reinforced by European recommendations calling for enhanced awareness, training, and educational initiatives in schools (Council of Europe & Commissioner for Human Rights, 2019). For such initiatives to be successful, teachers themselves must possess adequate AI-related competences, which in turn require integration into curricula and the development of suitable measurement methods (Hattie, 2023). Initial initiatives aiming to strengthen AI competences among teachers indicate that the availability of a clear competence model, including all relevant dimensions for teaching and learning with and about AI, is highly beneficial (Seyferth-Zapf et al., 2025).

Many existing models and assessments build on the TPACK framework (Mishra & Koehler, 2006), which focuses on the interplay of technological knowledge and purposeful use, content knowledge, and pedagogical knowledge required for effective teaching. However, the pedagogical dimension, particularly regarding teaching about AI, often remains underrepresented. Current AI-related competence models are either targeted broadly at “non-experts” (Laupichler et al., 2023) and therefore lack pedagogical specificity, or they are tailored to teachers but remain strongly oriented toward technical aspects of using AI, only implicitly addressing the demands of teaching about AI (Celik, 2023).

Additionally, existing models and assessments employ heterogeneous terminology and partially inconsistent definitions of what constitutes competence. To address this, the competence definition by Weinert (2001) is adopted as a theoretically robust foundation. Weinert conceptualises competence as a combination of cognitive abilities and skills, together with motivational, volitional, and social dispositions that enable individuals to successfully manage relevant tasks. This conceptualisation provides a well‑established and internationally recognisable basis for examining AI-related teacher competences. Following this understanding, the present study refers to competence, rather than literacy or competency, in line with Weinert‘s (2001) definition.

Based on this conceptual foundation and the identified gap,  the lack of a comprehensive model covering all relevant competence dimensions,  the aim of this study is to identify, systematise and integrate these dimensions into a coherent AI‑related competence model for teachers and then operationalise it. Accordingly, the study addresses the following research questions:

-        Which existing competence models for AI-related competences exist, and what are their strengths and weaknesses regarding pedagogical, technological, and ethical requirements?

-        Which specific competences do teachers need in the context of AI, and how can these AI-related competences be defined?

-        How should a comprehensive competence model be designed to capture all relevant requirements related to AI for teachers?

-        Which methodological approaches and operationalisation strategies are suitable for establishing such a competence model in a theoretically grounded and empirically validated manner?

The resulting model is intended to provide guidance for teacher education, curriculum development and school improvement. By combining European relevance, theoretical clarity and empirical rigour, the project contributes to a coordinated European dialogue on professional AI competences in education.

This study follows a Design Science Research (DSR) approach (Peffers et al., 2007), which provides a systematic framework for developing, validating, and iteratively refining an artefact, in this case a competence model for AI-related teacher competences. DSR consists of iterative steps of problem identification and motivation, objectives of a solution, design and development, demonstration, evaluation, and communication. In this project, the process is structured into three interconnected steps. Step 1 has already been completed, step 2 is in progress, and step 3 is in preparation. The first step aimed to systematically map existing research on AI related competences. The review followed Levac et al. (2010) and the PRISMA-ScR guidelines (Page et al., 2021). It included a multi-database search strategy, development of inclusion and exclusion criteria, a systematic two-stage screening procedure, and structured data extraction and qualitative analysis based on Kuckartz and Radiker (2023). This step has been completed and has provided an initial set of relevant competence dimensions. These findings form the theoretical basis for further model development. Building on the preliminary competence dimensions derived from the literature, the second step collects empirical insights from experts in the field. Participants are recruited through purposive sampling (Ahmad & Wilkins, 2025) to ensure a diverse range of professional perspectives from different stakeholder groups, such as subject didactics, schools, educational research, disciplinary science, education policy, or industry. The interviews are being transcribed, analysed using qualitative content analysis (Kuckartz & Radiker, 2023). This second step serves to iteratively validate, refine, and expand the competence dimensions identified in step 1. The consolidated results from steps 1 and 2 will form a robust basis for the subsequent operationalisation. The third step, which is in planning, will focus on transforming the validated competence dimensions into measurable indicators and items. This will involve deriving behavioural descriptors and formulating scale items. The operationalisation process will emphasise content validity, clarity, and applicability for teacher education contexts. Taken together, these three phases form a coherent, DSR-aligned methodological process that integrates theoretical synthesis, empirical validation, and practical applicability. The resulting model is designed to serve as a comprehensive and operationalisable framework for AI-related teacher competences.

The expected outcomes of this study reflect both the preliminary results already obtained and the anticipated contributions of the ongoing and upcoming steps. Initial findings from step 1, the Scoping Literature Review, confirm that no comprehensive competence model specifically addressing all AI‑related competences for teachers currently exists. The majority of existing models place a strong emphasis on technological knowledge and usage-related competences, while AI didacitcs, particularly teaching and learning about AI is only implicitly represented. Furthermore, motivational, volitional, and social dispositions, as conceptualised in Weinert’s definition of competence, are rarely integrated in a systematic manner. Based on these findings, an initial conceptual model was developed, suggesting that AI‑related teacher competence should consist of four overarching dimensions to enable effective instructional practice: - Instructionally relevant AI knowledge, encompassing technical knowledge, understanding of AI systems, and application-oriented skills; - Content-related competences, including both subject knowledge and AI‑specific subject-matter knowledge; - Motivational, volitional and social dispositions and abilities; - Professional teaching competences, integrating AI didactics, and subject didactics Step 2, the expert interviews, is expected to validate, refine, and expand these dimensions, with particular emphasis on strengthening the underdeveloped area of AI didactics. Insights from this step are anticipated to clarify which specific teacher actions, decision-making processes, and reflective practices are essential for teaching with and about AI. The main outcome of Step 3 will be the operationalisation of the consolidated competence model. In contrast to self‑assessment instruments frequently used in digital competence research, this study aims to develop a performance‑based assessment, allowing participants to demonstrate knowledge and skills through authentic tasks. The operationalisation will focus on content validity, clarity, and applicability in teacher education, ultimately producing a reliable and valid construct that empirically corroborates the competence model.

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