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Hannes Tegelbeckers, André Dietrich, Frank Bünning, Sebastian Zug, 2025
Artificial intelligence is no longer a lab concept. It is rewriting job profiles as well as education practices across Europe and the globe. TVET must now prepare learners for workplaces where algorithms co-pilot everything, from welding robots to farm drones, while simultaneously equipping teachers to harness AI for deeper and fairer learning.
This article investigates European policies to reveal how AI is being woven into TVET systems. It maps EU and UNESCO governance frameworks, compares national strategies, and highlights private sector engagement in curricula design, open-source tools, and immersive simulations. While AI-enhanced learning and training demonstrate promise, deficits in teacher training, infrastructure, and ethical oversight raise concerns.
The TVET sector and its stakeholders must adapt their educational goals and approaches in response to the transformations driven by AI, while the future workforce require more transversal skills, such as critical thinking, creativity, and digital literacy, especially AI literacy, which makes lifelong learning and reskilling essential (Geetha et al., 2025; Bankins et al., 2024).
Artificial intelligence (AI) is rapidly transforming the future of work by automating routine and increasingly complex tasks, reshaping job profiles, and altering workplace dynamics across industries. While AI-driven automation can boost productivity and create new opportunities—especially in fields like data analysis and digital services—it also poses significant challenges, including job displacement, wage stagnation for middle- and low-skill workers, and rising economic inequality (Acemoglu and Restrepo, 2019; Tyson and Zysman, 2022; Niketa et al., 2025; Geetha et al., 2025). The impact of AI on workers’ experiences is nuanced: it can enhance job satisfaction and safety by taking over dangerous or exhausting tasks but may also threaten workers’ sense of identity and well-being if not implemented thoughtfully (Selenko et al., 2022; Bankins et al., 2024).
While EU and UNESCO emphasize Open Educational Resources (OER), the adoption of Open Source (OS) approaches in industry and education will depend on the establishment and continuous use of data standards to facilitate these developments. Organizations and educational institutions must adapt by fostering human-centred skills and supporting employees through digital transitions (Bankins et al., 2024; Sarala et al., 2025) and ethical concerns—such as data privacy, algorithmic bias, and the equitable distribution of AI’s benefits—demand proactive policy frameworks and responsible AI deployment (Niketa et al., 2025; Tyson and Zysman, 2022). Ultimately, the societal outcomes of AI’s integration into work will depend on the design of both technology and policy, emphasizing the need for collaboration between governments, businesses, and workers to ensure inclusive and sustainable progress (Tyson and Zysman, 2022; Acemoglu and Restrepo, 2019; Niketa et al., 2025; Sarala et al., 2025). What counts for a general development can be seen in its variances in the different European TVET sectors. The TVET sector overall is still facing challenges of the digital transformation age, as ICT integration in TVET is low, particularly in monitoring, evaluation, career guidance assessment and teacher training (Hassan et al., 2021). Exchange platforms and shared training approaches between industry and public sector remain singled out or the most regional exemplary initiatives to improve on one of the more fundamental aspects, enabling AI to reach students in the classroom. AI in TVET can revolutionize skill-based education, but requires ethical standards, effective teacher training, and digital technology for universal access (Deckker and Sumanasekkara, 2025). And it is not, in contrast to digitalisation strategies in the past, a lack of policy or lack of willingness to invest resources. Out of 27 EU countries, only one has not yet prepared its strategic plan on artificial intelligence development (Woszczyna and Mania, 2023), so policy makers from an early stage on saw the urgency to create a pathway for their respective countries to move ahead.
At the international level, UNESCO complements Europe’s policy efforts by advancing a humanistic, rights-based approach to AI in education, emphasizing that AI should augment human capacity and dignity rather than replace it. The 2019 Beijing Consensus urged governments to adapt education systems for the AI era, followed by UNESCO’s policy guidance promoting AI for equity, inclusion, and efficiency—such as reaching marginalized learners or automating teachers’ routine tasks—while addressing risks like bias, transparency, and data privacy. These principles, reinforced in the 2021 Recommendation on the Ethics of AI, align closely with European values of fairness, accountability, and data protection. UNESCO has further operationalized its vision through AI Competency Frameworks for Students and Teachers (2023/24), advocating that AI knowledge and ethics be embedded across curricula rather than confined to computer science, with relevance for vocational contexts (e.g., healthcare students learning AI’s role in diagnostics, or automotive apprentices exploring AI-driven vehicle systems). In parallel, UNESCO-UNEVOC embeds digitalization and AI within its medium-term strategy 2024-2026, which prioritizes institutional capacity-building and leadership for digital transformation, while the broader UNESCO strategy 2022-2029 explicitly addresses AI-related disruptions, calling for public-private partnerships, inter-agency collaboration, and initiatives such as the Global Skills Academy to prepare TVET systems for an AI-driven economy and a sustainable future of work.
Adopted in 2024, the EU AI Act categorizes AI systems by risk, placing those used in “education and vocational training” in the high-risk category because of their potential impact on learners’ rights and opportunities. Tools such as AI-driven admissions, automated assessments, or exam proctoring must therefore meet strict requirements for transparency, human oversight, accuracy, and data protection. In practice, this means that vocational institutions adopting AI tutoring or assessment systems must ensure traceability, fairness, and informed use. Complementing the Act, the EU has updated its digital skills frameworks: DigComp 2.2 (2022) now includes AI and data literacy as core competences for citizens, while DigCompEdu and AI curriculum guidelines equip educators to teach these skills. For TVET learners entering AI-intensive workplaces, these frameworks ensure both adaptability and responsible use of AI (European Commission, 2022).
The European AI implementation strategy is characterized by a strong emphasis on ethical, human-centred, and trustworthy development of artificial intelligence, underpinned by solid regulatory frameworks such as the above mentioned EU AI Act, which adopts a risk-based approach to ensure both innovation and the protection of fundamental rights across member states (Outeda, 2024; Annoni et al., 2018; Roberts et al., 2022). National strategies across Europe focus on improving data quality, strengthening collaboration with the private sector, and investing in AI for social good, though there are noted gaps in internal capacity building and funding (Van Noordt et al., 2023; Foffano et al., 2022). Key dimensions of the strategy include building AI ecosystems, enhancing education and skills, ensuring data availability and protection, and establishing clear governance and liability structures (Polyviou and Zamani, 2022; Annoni et al., 2018). The EU’s approach is distinct from global stakeholders by prioritizing ethical outcomes and societal benefit, particularly in sensitive sectors like health, where balancing privacy, security, and innovation presents unique challenges (Cohen et al., 2020; Roberts et al., 2022). Implementation mechanisms include creation of the European Artificial Intelligence Office, national competent authorities, and collaborative governance structures to harmonize efforts across the continent (Outeda, 2024). Stakeholder engagement and multidisciplinary collaboration are highlighted as essential for responsible and sustainable AI innovation (Polyviou and Zamani, 2022; Foffano et al., 2022). The strategy also recognizes the need for transparent, secure, and explainable AI systems to build public trust and address concerns about bias and accountability (Annoni et al., 2018; Kotter et al., 2025). While Europe’s strengths lie in research excellence, regulatory experience, and industrial leadership, the strategy acknowledges ongoing challenges such as data access, expertise shortages, and the need for continuous adaptation to technological advances (Van Noordt et al., 2023; Annoni et al., 2018; Outeda, 2024). Overall, the European approach seeks to shape AI development in line with shared values, aiming for both global competitiveness and societal well-being (Annoni et al., 2018; Foffano et al., 2022; Roberts et al., 2022). This contrasts with approaches in some other regions, where strategies tend to prioritize rapid industry-led innovation and market dominance, sometimes with less emphasis on societal safeguards or equity considerations.
National AI strategies in Germany, France, and Finland converge in recognizing AI’s transformative potential yet diverge in vocational teacher training, curriculum innovation, and governance. Germany pursues a human-centric, semi-liberal approach balancing innovation with ethical safeguards and calls for broad societal AI competencies, including vocational and applied sciences, but concrete teacher training measures remain underdeveloped (Hälterlein, 2024; Harhoff et al., 2018). France, emphasizing state intervention and public investment to secure international competitiveness, prioritizes research and higher education curriculum modernization, similarly neglecting vocational teacher training (Bareis and Katzenbach, 2021; Kim, 2023). Finland stands out for practical AI integration in public organizations and libraries, fostering interdisciplinary collaboration and innovative curricula while actively upskilling public sector employees, including educators (Mirović, 2025; Mikalef et al., 2021). Governance across all three is top-down, with Germany and France aligning strategies to European cooperation, while Finland adopts a more decentralized, innovation-driven model (Bareis and Katzenbach, 2021; Woszczyna and Mania, 2023; Mikalef et al., 2021). Overall, although curriculum reform and governance structures are well-articulated, systematic provisions for vocational teacher training remain weak, exposing a persistent gap between policy ambition and implementation (Hälterlein, 2024; Harhoff et al., 2018; Mikalef et al., 2021).
Across these national examples, a recurring theme is the critical role of industry partnerships in AI-enhanced TVET. Effectively integrating AI into vocational training often requires resources and expertise beyond what public education systems alone possess – and this is where private sector actors step in. In Europe, collaborations with companies are helping to keep curricula relevant, provide technology access, and even develop new AI-driven educational tools. For instance, global tech firms like IBM, Google, and Amazon are active in Europe’s education sector, offering their platforms and know-how. IBM has a history of engagement in workforce development programs. Through its IBM SkillsBuild initiative (a free learning platform) and the P-TECH program (which in some countries blends secondary school with last-mile technical training), IBM has partnered with schools and NGOs in countries such as France and Germany to provide content on data science, AI, and cybersecurity. These programs often include mentorship by IBM experts and expose students to industry use cases of AI. The European Commission’s Digital Skills and Jobs Coalition has also facilitated many pledges and projects by companies to train students and teachers in AI-related skills. For example, Microsoft’s AI Schools program in Europe provided training for teachers to incorporate AI curriculum modules, and Google has supported nonprofits to run AI tinkering labs for youth. These partnerships are often supported by policy mechanisms – governments may provide matching funds, recognition of qualifications and learning outcomes, or simply a framework for collaboration (such as France’s pooling mechanism or Germany’s inviting of international tech firms to co-develop AI tools for education).
On a structural level, Sector Skills Alliances and the new European Digital Innovation Hubs serve as platforms where industry and vocational educators meet to design training for emerging occupations, many of which involve AI. For example, a sector alliance on automotive might produce a curriculum for electric vehicle technicians that includes working with AI diagnostics, drawing on input from car manufacturers (industry) and vocational institutes (education). The EU’s concept of Centres of Vocational Excellence (CoVEs) further promotes such collaboration; some CoVEs focus on digital manufacturing or smart agriculture and involve companies providing equipment, internships, and expertise to students learning about AI applications in those fields.
In summary, national strategies across Europe reinforce that cooperation with the private sector is crucial to bring AI into TVET effectively. Industry partnerships help ensure that vocational training stays up to date with the latest AI developments in the workplace, provide opportunities for students to learn in real-world contexts, and can contribute funding or in-kind support (like software licenses or cloud computing access for schools). Policymakers facilitate these partnerships through strategic funding and by creating forums for dialogue between educators and employers. The result is a more agile training ecosystem: one that can respond to fast-evolving skill demands and exploit cutting-edge tools (like AI simulators or learning platforms) that the private sector is often first to develop.
AI industry engagement and the involvement of private actors are increasingly recognized as crucial drivers of innovation in TVET. While much emphasis has been placed on embedding Industry 4.0 technologies—such as intelligent tutoring systems, robotics trainers, and adaptive learning platforms—into curricula, current debates also point toward the relevance of Industry 5.0, which stresses human-centric and sustainable innovation. This shift aligns with the European Commission’s call since 2021 for approaches that combine technological advancement with social and environmental responsibility. In this context, industry partners play a critical role not only in contributing to curriculum design and offering hands-on training but also in promoting practices that integrate digital transformation with broader societal goals (Idris et al., 2025; Mohamad et al., 2024; Deckker and Sumanasekara, 2025; Amdan et al., 2024). Despite their transformative potential, the adoption of advanced technologies such as AI, IoT, and data science in TVET remains limited, particularly in areas like monitoring, evaluation, and job placement (Hassan et al., 2021; Mohamad et al., 2024). A central barrier is the technological literacy gap among students and educators, which prevents effective use of these tools (Baharin et al., 2025; Amdan et al., 2025). This is compounded by insufficient institutional support, including weak policies, limited resources, and inadequate training programs (Baharin et al., 2025; Bonde, 2024). Unequal access to infrastructure further exacerbates educational inequalities, leaving many institutions unable to integrate such technologies effectively (Rajamanickam et al., 2024; Bonde, 2024). Ethical and privacy concerns add to these challenges, as AI and IoT often require processing sensitive data that raises issues of security and responsible use (Baharin et al., 2025; Marengo, 2024). Addressing these barriers requires stronger collaboration between educational institutions and industry stakeholders to provide resources, training, and real-world applications that align learning with labour market needs (Rajamanickam et al., 2024; Bonde, 2024). Without such comprehensive action, the potential of digital transformation in TVET will remain unrealized. Private actors play a pivotal role here by offering financial support, developing AI-driven training tools, and facilitating real-world project experiences, which are essential for workforce readiness and employability (Idris et al., 2025; Mohamad et al., 2024; Deckker and Sumanasekara, 2025).
Translating high-level AI policies into classroom practice requires concrete applications tailored to TVET contexts. Two categories of AI-powered tools have proven particularly relevant: The first are Intelligent tutoring systems and AI assistants which provide learners with individualized, on-demand support. Chatbots, adaptive tutoring platforms, and other tools enable real-time feedback and skill tracking. Such systems reduce delays in feedback cycles and support a diverse set of learners in vocational classrooms (360Learning, 2025; Park University, 2025). Personalized and adaptive learning systems, including e.g. Cornerstone and CYPHER Learning, tailor pacing and content to learners’ performance. Adaptive feedback fosters inclusive, learner-centred education—essential in VET, where student groups are highly diverse. Platforms like MagicSchool or Quiz Gecko provide adaptive assessment templates that extend learning beyond the classroom (Coursebox, 2024; Panda Education, 2024).
The second category contains AI-enhanced simulations and VR training which offer immersive, risk-free environments to practice complex tasks. Just naming a view examples, projects like VET2Sustain highlight tools such as 3DVista or WarpVR, while platforms like Interplay Learning demonstrate industry-specific applications, including for example wiring circuits or troubleshooting HVAC systems. These simulations adapt dynamically and prepare learners for unpredictable scenarios in construction, healthcare, and engineering (VET2Sustain, 2025). Those systems can come in their proprietary license format or in public copyright licensing structures such as the well-established and widely used creative commons (CC). This distinction matters because Open-source innovations also play a critical role, especially in the early stages. One example which marks a shift in content creation for example is LiaScript, which, provides a markdown-based, open framework for interactive courses aligned with OER principles. It enables educators with minimal coding skills to design materials that integrate multimedia, quizzes, or conversational AI assistants. LiaScript allows localization and collaboration across borders while supporting decentralized distribution methods for low-bandwidth contexts. Its AI-driven features—such as auto-generating quizzes or suggesting course improvements—illustrate how open-source tools democratize access to AI-enhanced vocational education (LiaScript, 2024). In essence, AI-powered tools and open frameworks like LiaScript exemplify scalable approaches to modernizing TVET. They empower educators to design inclusive and adaptive learning materials while maintaining transparency and autonomy, aligning with UNESCO’s call for equitable use of digital technologies in education.
The integration of artificial intelligence into European TVET systems brings both promise and complexity. AI enables personalized learning through intelligent tutoring systems, adaptive platforms, and data-driven feedback, allowing learners to progress at their own pace. Such individualized approaches improve engagement, mastery of skills, and success rates. At the same time, automation of routine tasks frees educators for higher-value teaching, while AI-powered simulations and virtual labs accelerate safe skill acquisition. AI also supports inclusion by breaking down language and accessibility barriers and extending opportunities to remote or non-traditional learners. Furthermore, aligning curricula with industry practices, particularly in Industry 4.0, ensures graduates acquire relevant skills and positions TVET institutions as hubs of innovation. These opportunities are tempered by significant challenges. Many vocational teachers lack the digital literacy needed to use AI effectively, making professional development essential. Unequal digital infrastructure—unreliable internet, outdated hardware, and weak IT support—risks widening divides between institutions. Ethical concerns also loom large: algorithmic bias, privacy risks from learning analytics, and diminished human interaction require strong oversight. While the EU AI Act addresses some issues, ensuring compliance remains difficult. Finally, scaling small pilots into sustainable, system-wide practice demands investment, coordination, and strategies to keep technology and curricula up to date, without which innovations risk becoming obsolete.
Policy responses must therefore be comprehensive. Scaled teacher training is critical to build both technical and pedagogical AI competencies. Investment in infrastructure must ensure equitable access, supported by open-source and affordable tools. Ethical frameworks, including codes of practice, institutional committees, and audits, are needed to guarantee fairness and transparency. Partnerships across government, industry, and education can pool resources, co-develop curricula, and drive innovation. Continuous research and evaluation will help identify effective practices, with successful models scaled and shared through European and international networks.
By pursuing these strategies, European TVET can navigate the opportunities and challenges of AI integration in ways that promote inclusivity, effectiveness, and ethical responsibility. The stakes are high: a more agile and innovative workforce, a narrowing of skills gaps, and the establishment of TVET systems that not only adapt to but also shape the technological transformations of the twenty-first century.
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