A new study from academics from three Indonesian universities, and universities in Ecuador, Türkiye and Spain (a truly global effort) has just been published looking at research on Artificial Intelligence (AI) in VET.
Their ‘bibliometric’ analysis, covers literature published between 2014 and 2023 (ie before the widespread availability of large language models like Chat GPT).
The analysis shows that AI in VET was increasingly oriented toward personalised learning, adaptive systems, curriculum design enhancement, and hands-on automation (eg robotics, simulation) in technical fields such as manufacturing and logistics. (If some of the AI terms like personalised learning are new to you – VDC News had an explainer article recently on the different forms of AI.)
Interestingly their analysis showed that journal articles dominate in terms of citations and influence compared to conference proceedings, suggesting perhaps that across VET there has been less public discussion about AI.
The authors argue that AI has strong potential to improve accessibility, inclusion, and equity in VET, by enabling differentiated instruction and support for learners with varied backgrounds.
They also note that AI-based tools may streamline curriculum development, deliver real-time feedback in laboratories or workshops, and support teacher decision-making. And they situate the application of AI within the global sustainable development agenda, noting contributions to:
- Sustainable Development Goal 4 (quality education)
- SDG 8 (decent work and economic growth), and
- SDG 9 (industry, innovation and infrastructure).
The research also highlights several critical challenges in embedding AI in VET. These include data privacy, algorithmic bias, and ethical concerns about fairness and transparency. These are obviously concerns that also resonate in schools and higher education as leaders and systems respond to the growing prevalence of AI.
Infrastructure readiness is another barrier, particularly in institutions lacking reliable connectivity, computational resources, and/or teacher capacity in AI. The authors also point out a gap in teacher training and professional development for AI fluency, which is needed for effective deployment and oversight.
For those working in VET providers, the findings underscore the need for strategic investment in infrastructure, capacity building, and ethical governance frameworks before scaling AI tools.
The study also suggests more empirical, context-driven research is needed, especially case studies, longitudinal evaluations, and design experiments, to assess real-world impacts. Over time, effectively harnessed AI could help VET providers deliver more adaptive, efficient, and inclusive training models aligned with evolving industry needs, but we obviously still need to take a risk-based approach to how AI is being deployed.
