AI Literacy: New European Framework Sets Out the Competences Teachers Need to Use AI Responsibly in Education
The TAILiC Framework, developed through the Erasmus+ Teacher Academy for AI Literacy project, offers a research-informed European reference structure to help teachers, schools and education policymakers move beyond basic AI tool use towards critical, ethical and pedagogically meaningful integration.
As artificial intelligence continues to reshape teaching and learning, a new European framework is setting out what teachers need to know, do and evaluate to use AI responsibly in education. The Teachers’ AI Literacy Competency Framework (TAILiC) provides a structured approach to developing, assessing and strengthening teachers’ AI literacy, connecting technical understanding with classroom practice, ethical judgement and school-level readiness.
Developed as part of the Teacher Academy for AI Literacy project, co-funded by the European Union’s Erasmus+ Teacher Academies programme, the framework responds to a growing challenge across education systems: teachers are increasingly experimenting with AI tools, but access to structured professional development, practical guidance and institutional support remains uneven.
Led by CARDET, the framework draws on desk and field research across seven countries: Greece, Cyprus, Germany, Italy, Romania, Sweden and Turkey. Its development also involved a comparative review of European and international approaches to AI literacy and digital competence, bringing together perspectives from research, teacher education, schools and education authorities.
From using AI tools to making informed educational decisions
The TAILiC Framework recognises that AI literacy requires more than the ability to operate generative AI applications or produce teaching materials. Teachers need to understand how AI systems work, recognise their limitations, evaluate the reliability of their outputs and decide when their use genuinely supports learning.
The research underpinning the framework identified several challenges. Although teachers were generally receptive to AI, their levels of readiness varied. AI use was often driven by individual experimentation rather than coordinated school strategies, while ethical concerns such as data privacy, algorithmic bias, misinformation and academic integrity were not always translated into practical classroom procedures.
The findings also highlighted a tendency to use AI primarily for lesson preparation, translation, resource creation and administrative efficiency. More ambitious applications, including inquiry-based learning, collaborative problem-solving and assessment redesign, were less common.
In response, the TAILiC Framework offers a common reference structure that helps educators connect AI knowledge with pedagogical decisions, ethical safeguards and continuous professional learning.
Four competency areas for responsible AI literacy
The framework organises teachers’ AI literacy into four interconnected competency categories:
- AI Knowledge and Awareness: Understanding AI concepts, data, model behaviour and limitations, while critically evaluating outputs for inaccuracies, bias and misleading information.
- AI in Pedagogical Practice and Innovation: Designing meaningful, curriculum-aligned learning activities that use AI to support critical thinking, creativity, feedback, accessibility and student engagement.
- Ethics, Governance and Human Agency: Protecting student data, promoting fairness and transparency, safeguarding academic integrity and ensuring that teachers and learners retain informed decision-making capacity.
- Professional Development and Organisational Readiness: Strengthening teachers’ professional learning, peer collaboration and contribution to school guidance, responsible tool selection and institutional AI policies.
Together, these categories position AI literacy as a professional and organisational competence, rather than an isolated technical skill. They apply across primary, secondary and vocational education, as well as across STEM and non-STEM subjects.
A progression model that recognises professional growth
A central feature of the framework is its three-level progression model, which supports differentiated learning and professional development:
- Acquire: Build foundational AI knowledge, recognise common risks, verify outputs and use appropriate tools cautiously.
- Apply and Evaluate: Design AI-supported learning activities, adapt outputs to curriculum and learner needs, apply safeguards and assess the educational value of AI.
- Lead and Transform: Support colleagues, contribute to school AI policies, evaluate wider impacts and promote a responsible, human-centred approach to AI integration.
These levels are developmental indicators rather than mandatory certification stages. Teachers may progress at different rates across the competency categories, depending on their experience, responsibilities and access to support.
The framework also emphasises the importance of demonstrating competence through practical evidence. Examples include AI-supported lesson plans, critical reviews of AI-generated content, tool evaluation reports, ethical-risk assessments, reflective portfolios and contributions to school policies. This approach shifts the focus from how frequently teachers use AI to how effectively, critically and responsibly they apply it.
Supporting teachers and education systems across Europe
The TAILiC Framework is designed for practical use by teacher education institutions, professional development providers, school leaders, policymakers, researchers and teachers. It can inform training curricula, self-assessment tools, mentoring activities, professional standards, school AI policies and quality assurance processes.
Its European dimension provides a shared structure while allowing adaptation to national curricula, institutional priorities, infrastructure and school contexts. Rather than prescribing a fixed list of approved tools or a single training programme, the framework focuses on competences that remain relevant as AI technologies evolve.
It also highlights a principle central to responsible implementation: teachers cannot carry the responsibility for AI integration alone. Effective adoption depends on sustained professional learning, clear institutional guidance, safe infrastructure, equitable access and collaboration between educators and school leadership.
Building a foundation for responsible AI in education
The TAILiC Framework provides a foundation for developing future learning pathways, assessment approaches and institutional implementation strategies. Its purpose is to help education systems translate broad ambitions for AI in education into identifiable professional competences and evidence of practice.
As AI becomes increasingly embedded in educational environments, the framework offers a practical reference for ensuring that technology serves educational goals, supports inclusion and protects learner agency.
By bringing together knowledge, pedagogy, ethics and organisational readiness, TAILiC contributes to a more coherent European approach to teacher AI literacy, one that places professional judgement and meaningful learning at the centre of responsible AI adoption.
The TAILiC Framework was developed under the Teacher Academy for AI Literacy project, Project No. 101249404, co-funded by the European Union’s Erasmus+ Teacher Academies programme.
Find the TAILiC Framework available here.
Learn more about the project: https://ai-literacy-teacheracademy.eu/
