- 2024Implementation
- 2025Implementation
Background
The systematic exploration of the use of artificial intelligence in education, together with targeted support for steering and achieving this objective, has become increasingly necessary. Rapid technological change and the growing presence of AI in workplaces require education and training systems to respond proactively. This need is particularly pronounced in vocational education and training (VET), where learners must acquire practical, occupation-specific skills aligned with evolving technologies and work processes. Without clear guidance, evidence and pedagogical frameworks, the use of AI risks remaining fragmented or ineffective, potentially widening skills gaps. This policy development addresses these challenges by supporting the informed, pedagogically sound and equitable integration of AI into education and training.
Objectives
The objectives are as following:
- Research and development (R&D) in the field of Gen-AI in Education for Slovenia;
- Analysis of the needs of education institutions for the efficient use of Gen-AI in the education and training process in Slovenia;
- Developing guidelines for the efficient use of Gen-AI in Education;
- Preparation of sample teaching scenarios for the use of generative artificial intelligence in selected educational institutions Preparation of guidelines for the rational and effective use of generative artificial intelligence for and in education.
Description
In the 'Generative AI in Education project 2024–2026’, the purpose is to study, plan, and apply guidelines for the meaningful use of GenAI in learning and teaching to support achieving the learning objectives.
The project is based on two well-established pedagogical models. The TPACK model helps teachers choose and use technology in a way that supports teaching goals and fits well with subject content and pedagogy. The SAMR model is used to assess how technology, in this case artificial intelligence, is used in teaching and learning, focusing on whether it adds real value to achieving learning objectives and developing students’ competences.
The core of the project is the intertwining of three areas:
- research in the field of GenAI,
- testing of pedagogical approaches in connection with Gen-AI, and
- testing relevant content and applications (with AI) that can support both teachers and students in the teaching and learning process.
The project is implemented by a partnership that brings together key institutions from the fields of education, research and technology. The partners include the Institute Anton Martin Slomšek; the Faculties of Education of the University of Ljubljana, the University of Maribor and the University of Primorska; the Faculty of Arts of the University of Ljubljana; the National Education Institute Slovenia; the Jožef Stefan Institute; the Faculty of Computer and Information Science of the University of...
In the 'Generative AI in Education project 2024–2026’, the purpose is to study, plan, and apply guidelines for the meaningful use of GenAI in learning and teaching to support achieving the learning objectives.
The project is based on two well-established pedagogical models. The TPACK model helps teachers choose and use technology in a way that supports teaching goals and fits well with subject content and pedagogy. The SAMR model is used to assess how technology, in this case artificial intelligence, is used in teaching and learning, focusing on whether it adds real value to achieving learning objectives and developing students’ competences.
The core of the project is the intertwining of three areas:
- research in the field of GenAI,
- testing of pedagogical approaches in connection with Gen-AI, and
- testing relevant content and applications (with AI) that can support both teachers and students in the teaching and learning process.
The project is implemented by a partnership that brings together key institutions from the fields of education, research and technology. The partners include the Institute Anton Martin Slomšek; the Faculties of Education of the University of Ljubljana, the University of Maribor and the University of Primorska; the Faculty of Arts of the University of Ljubljana; the National Education Institute Slovenia; the Jožef Stefan Institute; the Faculty of Computer and Information Science of the University of Ljubljana; the Faculty of Electrical Engineering and Computer Science of the University of Maribor; and Primary School Oskar Kovačič Škofije.
It is financed by the Republic of Slovenia and the European Union – NextGenerationEU as part of the Recovery and Resilience Plan. The budget is EUR 1 000 000.
The project activities have been organised into seven Work Packages (WPs):
WP1: R&D in the field of generative AI in education
WP2: Situation analysis / state-of-play analysis
WP3: Development and preparation of foundations and guidelines
WP4: Examples of good practice
WP5: Evaluation and ensuring sustainability of results
WP6: Dissemination
WP7: Management and coordination
The first work package (WP1: R&D in the field of generative AI in education) focused on a state-of-the-art analysis of the literature on generative AI in education. This included a comprehensive review of key aspects, namely the policy and legislative framework, research on didactic potential and support for vulnerable groups, as well as technological, ethical and security considerations.
The second work package (WP2: Situation analysis / state-of-play analysis) was dedicated to developing the necessary methodological framework and then conducting a study to collect data and understand the current situation and needs of key stakeholders within the education system. This included the following steps: development of a methodology for assessing the current use and potential of Gen-AI in the Slovenian education sector, conducting a large-scale analysis to identify the key needs and challenges of the Gen-AI in Gen-AI implementation (collection of data include teachers, learners and managers in educational institutions), development of recommendations for improving and optimizing the use of Gen-AI in educational processes. The data collection phase was conducted in December 2024.
As part of WP2, in October 2024 the publication ‘Research plan for the project Generative Artificial Intelligence in Education. Work Package 2’, led by the Faculty of Education of the University of Maribor, was released. The document outlines the research plan for the Generative Artificial Intelligence in Education project, based on a mixed-methods approach combining quantitative surveys and qualitative focus groups. It examines the current use of generative AI in education, perceived challenges and opportunities, pedagogical approaches, skills gaps, ethical and technical issues, and policy needs across primary, secondary and higher education. Data will be collected from students, teachers and school leaders, analysed using statistical and qualitative analysis tools, and synthesised into evidence-based guidelines and a final research report to support the effective and responsible use of generative AI in education.
As part of finalising the WP1 (R&D in the field of generative AI in education), the monograph ‘Education in the Age of Generative Artificial Intelligence: International Guidelines and Research’ was released in 2025. The publication provides a comprehensive overview of research and policy developments related to the use of Gen-AI in education. Drawing on international literature and empirical evidence from Slovenia, it examines pedagogical opportunities and risks, ethical and regulatory considerations, digital inclusion, and institutional readiness across primary, secondary and tertiary education. The publication concludes with practical recommendations and guidelines to support the responsible, effective and pedagogically sound integration of Gen-AI into education systems.
As part of finalising the WP2 (Situation analysis / state-of-play analysis), the collected data from all stakeholders were analysed and a scientific monograph was prepared. The publication entitled ‘Generative Artificial Intelligence in Education: An Analysis of the State of Play in Primary, Secondary, and Tertiary Education’ presents a comprehensive research-based analysis of the use of Gen-AI in education in Slovenia. It combines an extensive literature review with empirical research, including surveys and focus groups involving teachers, school leaders and higher education staff across primary, secondary and tertiary education. The study examines pedagogical opportunities and challenges, digital divides, ethical and technical constraints, and the readiness of educational institutions to integrate Gen-AI into teaching and learning processes. Based on the findings, the publication identifies key competence needs for educators, explores the impact of Gen-AI on student-centred learning and inclusion, and assesses institutional and policy-level gaps. It concludes with concrete recommendations for education policy, focusing on responsible and pedagogically sound use of Gen-AI, professional development for educators, quality assurance, and alignment with European frameworks such as the EU Artificial Intelligence Act. The document serves as a reference for policymakers, researchers and practitioners seeking to guide the effective integration of generative AI into education systems.
The ‘Gen-AI in education project 2024-2026’ continued its work with WP3, which focused on developing foundational frameworks that address the organisational, didactic, technical, and content-related aspects of integrating Gen-AI in education. In close coordination with the Ministry of Education, official pedagogical guidelines for the sensible and meaningful use of GenAI were drafted. These guidelines address key issues such as ethical use, assessment practices, the promotion of critical thinking skills, and the effective integration of new guidelines and frameworks into existing educational structures, including national curricula.
In addition, WP3 included a dedicated study on the digital divide, aiming to identify how the use of generative AI may contribute to inequalities among different learner groups. Based on these findings, policy proposals were developed to mitigate identified gaps and support equitable access to educational technologies. Finally, a monograph associated with WP3 was published in late 2025, presenting the proposed frameworks, guidelines and strategies for transforming education through generative AI.
WP4 (Examples of good practice) ran in parallel with WP3 and focused on bridging the gap between policy and classroom practice through the development of practical resources, such as case studies and good practices for teachers. In its first stage, didactic recommendations were developed and published in the document ‘Didactic Recommendations and Guidelines for the Integration of Generative AI in Teaching’, providing a foundation for designing concrete examples of generative AI use in education. In the next stage, a repository of practical examples and use cases showcasing the effective use of generative AI in various educational contexts is foreseen. The goal is to implement the developed use cases in real-world educational settings in order to assess their effectiveness, usability and impact on learning outcomes.
While the ‘Generative AI in Education project 2024–2026’ project addresses the education system as a whole, it is also highly relevant for the VET sector. The project’s findings are expected to directly contribute to:
- updating VET curricula: The results will inform the updating of VET programmes by integrating insights on the use of Gen-AI in specific occupational contexts.
- innovative pedagogical practices: The project identifies ways in which teachers and trainers can use generative AI to create realistic work-environment simulations, develop diagnostic and assessment tools, and automate routine tasks, thereby freeing up time for more targeted mentoring. These approaches are directly applicable to VET programmes, VET trainers and VET learners.
- strengthening learners' digital skills: integrating Gen-AI into education supports the development of VET learners’ digital literacy and their preparedness for the modern labour market, where such tools are increasingly prevalent.
Bodies responsible
- Ministry of Education
Target groups
Learners
- Learners in upper secondary, including apprentices
- Young people (15-29 years old)
- Learners with disabilities
- Adult learners
- Learners from other groups at risk of exclusion (minorities, people with fewer opportunities due to geographical location or social-economic disadvantaged position)
Education professionals
- Teachers
- Trainers
- School leaders
- Adult educators
Entities providing VET
- VET providers (all kinds)
Other stakeholders
- Social partners (employer organisations and trade unions)
Thematic categories
Governance of VET and lifelong learning
This thematic category looks at existing legal frameworks providing for strategic, operational – including quality assurance – and financing arrangements for VET and lifelong learning (LLL). It examines how VET and LLL-related policies are placed in broad national socioeconomic contexts and coordinate with other strategies and policies, such as economic, social and employment, growth and innovation, recovery and resilience.
This thematic category covers partnerships and collaboration networks of VET stakeholders – especially the social partners – to shape and implement VET in a country, including looking at how their roles and responsibilities for VET at national, regional and local levels are shared and distributed, ensuring an appropriate degree of autonomy for VET providers to adapt their offer.
The thematic category also includes efforts to create national, regional and sectoral skills intelligence systems (skills anticipation and graduate tracking) and using skills intelligence for making decisions about VET and LLL on quality, inclusiveness and flexibility.
This thematic sub-category refers to the integration of VET into economic, industrial, innovation, social and employment strategies, including those linked to recovery, green and digital transitions, and where VET is seen as a driver for innovation and growth. It includes national, regional, sectoral strategic documents or initiatives that make VET an integral part of broader policies, or applying a mix of policies to address an issue VET is part of, e.g. in addressing youth unemployment measures through VET, social and active labour market policies that are implemented in combination. National skill strategies aiming at quality and inclusive lifelong learning also fall into this sub-category.
High-quality and timely skills intelligence is a powerful policy tool, helping improve economic competitiveness and fostering social progress and equality through the provision of targeted skills training to all citizens (Cedefop, 2020). Skills intelligence is the outcome of an expert-driven process of identifying, analysing, synthesising and presenting quantitative and/or qualitative skills and labour market information. Skills intelligence draws on data from multiple sources, such as graduate tracking systems, skills anticipation mechanisms, including at sectoral and regional levels. Actions related to establishing and developing such systems fall under this thematic sub-category.
Modernising VET infrastructure
This thematic category looks at how VET schools and companies providing VET are supported to update and upgrade their physical infrastructure for teaching and learning, including digital and green technologies, so that learners in all VET programmes and specialities have access to state-of-the-art equipment and are able to acquire relevant and up-to-date vocational and technical skills and competences. Modernising infrastructure in remote and rural areas increases the inclusiveness of VET and LLL.
This thematic sub-category refers to measures for modernising physical infrastructure, equipment and technology needed to acquire vocational skills in VET schools and institutions that provide CVET or adult learning, including VET school workshops and labs.
This thematic sub-category focuses on establishing and upgrading to state-of-the-art digital infrastructure, equipment and technology, such as computers, hardware, connectivity and good broadband speed that should ensure quality and inclusive VET provision, especially in blended and virtual modes. It also includes specific measures to remove the digital divide, e.g. supporting geographically remote or rural areas to ensure social inclusion through access to such infrastructure for learning and teaching. It also includes support measures for learners from socially disadvantaged backgrounds to acquire the necessary equipment.
Modernising VET offer and delivery
This thematic category looks at what and how individuals learn, how learning content and learning outcomes in initial and continuing VET are defined, adapted and updated. First and foremost, it examines how VET standards, curricula, programmes and training courses are updated and modernised or new ones created. Updated and renewed VET content ensures that learners acquire a balanced mix of competences that address modern demands, and are more closely aligned with the realities of the labour market, including key competences, digital competences and skills for green transition and sustainability, both sector-specific and across sectors. Using learning outcomes as a basis is important to facilitate this modernisation, including modularisation of VET programmes. Updating and developing teaching and learning materials to support the above is also part of the category.
The thematic category continues to focus on strengthening high-quality and inclusive apprenticeships and work-based learning in real-life work environments and in line with the European framework for quality and effective apprenticeships. It looks at expanding apprenticeship to continuing vocational training and at developing VET programmes at EQF levels 5-8 for better permeability and lifelong learning and to support the need for higher vocational skills.
This thematic category also focuses on VET delivery through a mix of open, digital and participative learning environments, including workplaces conducive to learning, which are flexible, more adaptable to the ways individuals learn, and provide more access and outreach to various groups of learners, diversifying modes of learning and exploiting the potential of digital learning solutions and blended learning to complement face-to-face learning.
Centres of vocational excellence that connect VET to innovation and skill ecosystems and facilitate stronger cooperation with business and research also fall into this category.
VET standards and curricula define the content and outcomes of learning, most often at national or sectoral levels. VET programmes are based on standards and curricula and refer to specific vocations/occupations. They all need to be regularly reviewed, updated and aligned with the needs of the labour market and society. They need to include a balanced mix of vocational and technical skills corresponding to economic cycles, evolving jobs and working methods, and key competences, providing for resilience, lifelong learning, employability, social inclusion, active citizenship, sustainable awareness and personal development (Council of the European Union, 2020). The thematic sub-category also refers to establishing new VET programmes, reducing their number or discontinuing some. It also includes design of CVET programmes and training courses to adapt to labour market, sectoral or individual up- and re-skilling needs.
The learning-outcomes-based approaches focus on what a learner is expected to know, to be able to do and understand at the end of a learning process (Cedefop, 2016). Learning outcomes can be defined at the system level as in national qualification frameworks (NQFs), most of which are currently based on learning outcomes. Learning outcomes can be defined in qualification standards, curricula, learning programmes and assessment, although the last one is still uncommon. This thematic sub-category refers to the use of learning outcomes in these contexts and to development and use of modules or units of learning outcomes in VET curricula and programmes.
This thematic sub-category is about the way learners learn, how the learning is delivered to them, and by what means. Programmes become more accessible through a combination of adaptable and flexible formats (e.g. face-to-face, digital and/or blended learning), through digital learning platforms that allow better outreach, especially for vulnerable groups and for learners in geographically remote or rural areas.
This thematic sub-category focuses on developing and updating all kinds of learning resources and materials, both for learners and for teachers and trainers (e.g. teachers handbooks or manuals), to embrace current and evolving content and modes of learning. These activities target all kinds of formats: hard copy and digital publications, learning websites and platforms, tools for learner self-assessment of progress, ICT-based simulators, virtual and augmented reality, etc.
This thematic sub-category refers to acquisition of key competences and basic skills for all, from an early age and throughout their life, including those acquired as part of qualifications and curricula. Key competences include knowledge, skills and attitudes needed by all for personal fulfilment and development, employability and lifelong learning, social inclusion, active citizenship and sustainable awareness. Key competences include literacy; multilingual; science, technology, engineering and mathematical (STEM); digital; personal, social and learning to learn; active citizenship, entrepreneurship, cultural awareness and expression (Council of the European Union, 2018).
This thematic sub-category refers to updating VET curricula and programmes to incorporate skills related and needed for the digital transition, including sector- and occupation-specific ones identified in cooperation with stakeholders.
Teachers, trainers and school leaders competences
Competent and motivated VET teachers in schools and trainers in companies are crucial to VET becoming innovative and relevant, agile, resilient, flexible, inclusive and lifelong.
This thematic category comprises policies and practices of initial training and continuing professional development approaches in a systemic and systematic manner. It also looks at measures aiming to update (entry) requirements and make teaching and training careers attractive and bring more young and talented individuals and business professionals into teaching and training. Supporting VET educators by equipping them with adequate competences, skills and tools for the green transition and digital teaching and learning are addressed in separate thematic sub-categories.
The measures in this category target teachers and school leaders, company trainers and mentors, adult educators and guidance practitioners.
This thematic sub-category refers to all kinds of initial and continuing professional development (CPD) for VET educators who work in vocational schools and in companies providing VET. VET educators include teachers and school leaders, trainers and company managers involved in VET, as well as adult educators and guidance practitioners – those who work in school- and work-based settings. The thematic sub-category includes national strategies, training programmes or individual courses to address the learning needs of VET educators and to develop their vocational (technical) skills, and pedagogical (teaching) skills and competences. Such programmes concern state-of-the-art vocational pedagogy, innovative teaching methods, and competences needed to address evolving teaching environments, e.g. teaching in multicultural settings, working with learners at risk of early leaving, etc.
This thematic sub-category refers to measures aimed at engaging more professionals into teaching and training careers, including career schemes or incentives. It includes measures enabling teaching and training of staff, managing VET provider and trainer teams in companies to act as multipliers and mediators, and supporting their peers and/or local communities.
This thematic sub-category is in line with the EU policy focus on the digital transition, and refers to professional development and other measures to prepare and support teachers and trainers in teaching their learners digital skills and competences. It also covers measures and support for them to increase their own digital skills and competences, including for teaching in virtual environments, working with digital tools and applying digital pedagogies. Emergency measures taken during the COVID-19 pandemic also fall into this sub-category.
European priorities in VET
VET Recommendation
- VET as an attractive choice based on modern and digitalised provision of training and skills
Osnabrück Declaration
- Resilience and excellence through quality, inclusive and flexible VET
Subsystem
Further reading
Country
Type of development
Cedefop, & ReferNet. (2026). Generative artificial intelligence in education: Slovenia. In Cedefop, & ReferNet. (2026). Timeline of VET policies in Europe (2025 update) [Online tool].
https://www.cedefop.europa.eu/nl/tools/timeline-vet-policies-europe/search/49911