在职业教育与培训发展中有效应用人工智能的五大原则

Five principles for the effective use ofAIin vocational education and trainingdevelopment 23June2026 Key messages The use ofartificial intelligence (AI)is emerging to supportthe developmentandupdatingofvocationaleducation and training(VET)curriculum and qualifications(hereafter‘VETdevelopment’). It is increasingly used for tasks such as labourmarket analysis, drafting andsynthesisinginputs across stakeholders, with potential to improve efficiency, responsiveness and alignment withevolving labour market demand.Across countries, its use remains ad hoc, experimental and focusedon preparatory stages, such as analysing large volumes of labourmarket data, mapping skills,synthesising stakeholder feedback and producing initial drafts. While these applications can improvespeed, consistency and evidence use, they also raise important questions about governance,quality,accountability andownership inVETsystems. VET development has unique institutional characteristicsto be considered when applyingAI.Unlike other education contexts, VET curricula and qualifications are developed through negotiated,regulated and multi-stakeholder processes, often embedded in legal frameworks and quality-assurancearrangements and involving public authorities, social partners, sector bodies and providers. Decisionstaken in these processes carry strong signalling effects for learners and employers and have directimplications forlabour-market outcomesandpublic trustin VET.Evidence shows that AI use in VETdevelopment remains unevenandshaped bydifferentconcerns, barriers and potential risks. There is a strong need for VET-specificguidance on the effective and secure use of AI in VETdevelopmentprocesses,as existing international AI principles–while providing an importantfoundation–do not fully account for the institutional realities of VET systems.Without VET-specificguidance, AI risks beingappliedin fragmented ways that may weaken collective decisionmaking, blurresponsibility or reinforcedividesbetween stakeholders with different levels of digital readiness.Clearguidance is therefore needed to ensure that AI strengthens,rather than undermines,the collaborativegovernance, legitimacy and quality that are central to VET systems. This policy brief addresses this gap by setting out fiveprinciplesto supportVET stakeholderson theeffectiveand secure use of AI in VET development. Theseprinciplesclarify how AI shouldsupport collective expert judgement and decisionmaking rather than replace it: 1.Human-centred use emphasises that AI should function as a support tool for VETdevelopment, assisting with analytical or technical tasks while final judgement, validation andresponsibility remain firmly with human experts and stakeholders.2.Diversityand inclusivenessfocuson ensuring that AI use does not advantage only well-resourcedstakeholders,but instead supports broad and inclusive participation in VETdevelopment, including SMEs, smaller providers and stakeholders with lower digital or AIcapacity.3.Accountability clarifies that responsibility for decisions, processes and outcomes in VETdevelopment must remain clearly assigned to VET stakeholders, even when AI is used,preventing ambiguity about who owns and approves curricula and qualifications.4.Transparencyrequires thatVETstakeholders understand when and how AI is used in VETdevelopment, for what purpose, on which data and with what limitations, helping to maintaintrustsupports continuous improvement,and avoid over-reliance on AI-generated outputs.5.Data quality, security and protectionhighlights the need for high quality, relevant and well-governed data, alongside strong safeguards for sensitive educational and labour marketinformation, ensuring that AI use complies with legal requirements and preserves trust in VETsystems. What’s the issue? AI is increasingly influencing how VET curricula and qualificationsare developed, reviewed andaligned with labour-market needs. As skill demands evolve rapidly due to technological change, the greentransition and demographic shifts, VET systems face growing pressure to become moreagile, responsiveand evidence informed. In this context, AI offers a range of innovation opportunities for VET development,including analysing large volumes of labour-market data, accelerating drafting and revision processes,supportingmodularisation and micro-credentials,and supporting co-ordination across many VETstakeholders involved inVETdevelopment. At the same time, the integration of AI into these processesraises important governance, quality and trust-related challenges. Despite the growing body of international guidance on AI in education, these are not tailored to AIuse in VET curriculum and qualification development.Existing AI principles and education andtraining-focused frameworks provide an important foundation(seeBox1), but do not fully reflect theinstitutional, legal and procedural characteristics of VET systemsin details. VET curricula and qualificationsare the outcome o