Analysis of the integration of Artificial Intelligence in VET systems

Cesar Herrero (), Javier Portillo Berasaluce, Ander Arce Alonso, Elisabeth Te Hennepe, Daniel Wisniewski, Sandra Gil Sanchez, Alexander Petanovitsch and Zan Dapcevic
Additional contact information
Cesar Herrero: European Commission - JRC, https://joint-research-centre.ec.europa.eu/index_en

No JRC146918, JRC Research Reports from Joint Research Centre

Abstract: Artificial Intelligence (AI) is entering Vocational Education and Training (VET) systems at a time of important structural reform, tightening EU level regulation, and heightened digital investment. This JRC technical report examines AI integration across five Member States – Austria, Belgium, Slovenia, Spain and the Netherlands – as a multilevel ecosystem process rather than the mere diffusion of isolated tools. The analysis draws on (i) an extensive desk review of policy and academic literature, (ii) semi structured interviews conducted at macro (policy), meso (intermediary networks and VET institutions) and micro (classroom) levels, and (iii) a synthesis of selected initiatives presented in good practice fiches, subsequently validated through a stakeholder roundtable or similar approaches. Findings reveal that AI uptake in VET is condition driven: adoption occurs when a specific constellation of enabling factors is present. Key determinants include robust governance capacity, systematic teacher competence development, trusted digital infrastructures, and stable support from intermediary networks and centre-industry agreements. Critical prerequisites for scaling AI-enabled VET are (a) formal teacher accreditation in AI related competences, (b) protected release time for educators to experiment with innovative practices, and (c) the deployment of “sovereign” technical stacks – exemplified by the Dutch EduGenAI platform – that guarantee data protection, algorithmic transparency and alignment with public values. The report links EU and national frameworks (e.g., the EU AI Act and ongoing VET reforms) with concrete, actionable conditions for responsible, scalable adoption. A notable shift toward process-oriented assessment – oral defences, reflective portfolios and continuous feedback loops – is observed, aiming to safeguard academic integrity where educational AI is classified as “high risk”. While AI promises personalised tutoring and reduced administrative load, persistent barriers remain, such as vendor lock in, volatile token-based pricing, and heterogeneous AI literacy among staff. By analysing the five case studies through a high maturity lens, the report elucidates transfer mechanisms and argues that sustainable AI integration requires a strategic move from technology-centric inputs to human-centred ecosystem outcomes, ultimately empowering the VET workforce and reinforcing the European digital education agenda.

Date: 2026-08
References: Add references at CitEc
Citations:

Downloads: (external link)
https://publications.jrc.ec.europa.eu/repository/handle/JRC146918 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ipt:iptwpa:jrc146918

Access Statistics for this paper

More papers in JRC Research Reports from Joint Research Centre Contact information at EDIRC.
Bibliographic data for series maintained by Publication Officer ().