Abstract:

Artificial Intelligence (AI) is reshaping European education systems and creating an urgent need to redefine the competences required of educators. This contribution presents the results of a desk-based study that maps existing frameworks, policies and initiatives on AI in education in order to derive competence domains and learning outcomes for educators at different qualification levels.

The study addresses three objectives:
(1) to analyse how current European and national policy documents conceptualise the role of AI in education and the expectations placed on educators;
(2) to identify and compare existing competency frameworks, standards and projects relevant to AI, digital competence, accessibility, Universal Design for Learning (UDL) and academic integrity; and
(3) to synthesise this evidence into a coherent, qualification-referenced structure that can underpin future training and microcredential offerings for educators.

Methodologically, the research adopts a structured desk review. It examines key European-level documents (including the Digital Education Action Plan, the European AI Act, the European Qualifications Framework and DigCompEdu), national AI and digital education strategies from several EU Member States, and a selection of international and European frameworks addressing ethics, data protection, accessibility and inclusive pedagogy. This is complemented by a mapping of Erasmus+ and other projects that explicitly tackle AI in education or related areas, such as learning analytics, academic integrity, and the digital upskilling of teachers. Finally, a PESTEL lens is used to situate these developments within broader political, economic, social, technological, environmental and legal drivers that condition AI adoption in education.

The analysis shows a rapid proliferation of policy guidance and competence models, but also fragmentation, uneven attention to ethics and accessibility, and limited operationalisation of AI-specific competences for educators. Across sources, convergences emerge around a set of core areas: AI literacy and critical understanding of AI systems; ethical, legal and governance issues (including transparency, bias, privacy and accountability); pedagogical design with AI, grounded in UDL principles; assessment and academic integrity in AI-rich environments; and collaboration, communication and institutional engagement. On this basis, the study proposes a structured set of competence domains and sample learning outcomes for two qualification levels, broadly corresponding to higher education teaching staff and adult, vocational, and youth educators. These domains are formulated in EQF-consistent terms of knowledge, skills and responsibility/autonomy, enabling their potential use in modular curricula, professional development pathways and stackable microcredentials.

By clarifying expectations and providing a cross-referenced structure grounded in European policy and ethical principles, the study contributes to ongoing efforts to build a trustworthy, inclusive and future-ready European Education Area in the age of AI.

Acknowledgement:
This work was carried out within the project AI-UpSkillED (2025-1-ES01-KA220-HED-000355590), a transnational Erasmus+ cooperation partnership in higher education, co-funded by the Erasmus+ Programme of the European Union by the Spanish National Agency (SEPIE).

Keywords:

Artificial Intelligence in education, educator competences, European policy, EQF, microcredentials.