The AI in European Vocational Education

The Future of Jobs report highlights a pressing concern: employers predict that an astonishing 44% of workers’ skills will become obsolete within five years.

AI deployment is rapidly changing Europe’s Economic and Cultural landscape. Vocational Education will be impacted by those changes in many ways, not just in terms of what and how students are learning but also in the need for a large swath of teachers to become AI savvy. It is even more difficult for educational leaders who need to be AI savvy and understand how AI will impact the business around Education. Keeping track and taking advantage of change requires significant effort.

The AI4VET4AI project develops AI literacy curriculums in Europe as part of its remit. Within the project, I am a senior researcher and data storyteller who has, for the past year, been privileged to be part of a team comprised of members from the TIB Eindhoven and the University of Amsterdam. Our recently completed goal was to develop a list of new skills associated with AI literacy. The project also tasked us with recommending curriculum development for European Vocational Education.

The impact of AI in Europe is difficult to track, and the associated skills that students, teachers, and educational policymakers need to learn. However, that was our task. In this post, I will briefly outline how we managed.

Given the complexity and velocity of change in the AI landscape and the immense number of opinions, research, opportunities, regional and sectorial variance in AI approaches and impact, developing a realistic snapshot of our buzzing, turbulent, opinionated reality is rather complex.

From the outset of our investigation, it was clear that any source of truth, such as data from Statistics bureaus, would only partially capture the story we needed to tell. Therefore, the research team triangulated with several methodologies.

I will not bore you with the structural details. Still, we took several robust approaches, including surveys, asking experts, systematic literature reviews, data-driven Job Market Intelligence (JMI), and my part weaving numerous data sources together to provide a coherent story. Thankfully, there was significant support from experts within or associated with the project. For example, ten highly Educated and motivated volunteers filtered 7000 articles from a systematic literature review. You know who I am talking about! Again, thank you, fellow hard workers.

As for the plan, all the methods, comments, and expert feedback reinforced each other, helping the team develop a list of skills and curriculum recommendations based on multiple strands of evidence.

Once the team detailed the AI literacy skills, we chose a limited subset from the greater whole. The skill’s importance is liable to change. Therefore, we also needed an approach to keep track of those changes. Job Market Intelligence using information derived from a BIG set of Job Advertisements is one viable approach. Another supplementary method is keeping track of curated documents in evidence hubs. These evidence hubs are often known as AI observatories; the one funded by the European Commission is AI Watch. All credit is due; this is an excellent resource. There are also periodic reports such as the Stanford AI index report, data hubs, and research papers. Keeping track of AI literacy needs is a full-time job for a whole team.

Based on the multiple strands of evidence, it is essential to recognize that as generative AI continues to advance, we should consider it as a collaborative partner that can sometimes provide convincing yet potentially misleading information. This phenomenon, known as the AI butterfly effect, illustrates how even a small amount of poor-quality training data in AI can result in a significant amount of misleading output. Issues such as the effort to gather data securely, the use of data and privacy, biases in the data, methods of AI training, and the transparency of decision-making processes all present challenges that we must address. In particular, we need to consider how humans interact with AI and the specific training that human users require to utilize AI critically and creatively.

The reality is that low-skilled work is being rapidly disrupted, with tasks such as manual bookkeeping and tourism services increasingly being automated. AI has quickly become integral to our professional lives; we must adapt and evolve alongside these technological advancements. Workers must proactively develop their critical thinking and creative strengths to remain competitive and relevant. Competitiveness requires providing additional value beyond the repetitive, 24/7 drudgery that current AI systems can accomplish. Individuals can augment their abilities and deliver enhanced value by focusing on higher-order transversal skills that support critical thinking and creativity.

Keeping track of the AI literacy landscape requires time, effort, methodology and vigilance. We learned a lot during the investigation and can now efficiently keep track through mixed methods.

After the hard work, we delivered the AI skillset list to the partner teams at AI4VET4AI. We are excited to see how they will use them to create new curriculums. We fully expect that these curriculums will motivate and educate a broad workforce.

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