Artificial Intelligence is often described as a tool that improves the speed and efficiency of existing processes. This framing, however, underestimates the scale of change underway. AI is not merely accelerating work; it is reorganising what counts as work by transforming task structures, roles, and forms of value creation.
For Vocational Education and Training (VET) institutions, this raises a fundamental question: if machines can increasingly perform the tasks for which humans are trained, what should education focus on instead?
Rethinking Education for an AI-powered Economy
Modern education systems were designed for an industrial economy. Their structure – standardized curricula, clear hierarchies, measurable outputs – reflects a world where knowledge was relatively scarce and productivity, while increasingly amplified by industrial technologies powered by the steam engine, was still fundamentally structured around human labour. VET, in particular, has been closely tied to labour market needs of the capitalist system. It prepares individuals for specific roles, equipping them with the skills required to perform defined tasks. AI disrupts this model.
When systems can communicate, analyze data, automate processes, design prototypes, generate ideas, and simulate decision-making, many task-based competencies lose their relevance. The half-life of skills is shrinking at mind-boggling speed. What is relevant today may be obsolete tomorrow.
Maintaining relevance will require VET institutions to rethink what they teach at a foundational level, adapting to a new system that is only beginning to take shape. This shift will unfold over time, as AI progressively transforms a world economy built on human-paced intelligence. In this transition, educational institutions are not passive observers. They have both the opportunity and the responsibility to actively shape this transformation, equipping learners with the capabilities to navigate AI-driven disruption and the digital economy in ways that steer society toward more positive scenarios of meaningful and sustainable futures.
Abundance or Dependence? The Responsibility of Education in the Age of AI Power
For centuries, Western societies have been organized around labour as their central structuring principle. Work provides not only income, but also identity, social status, and a sense of purpose. This model is deeply rooted in industrial capitalism and culturally reinforced by the Protestant work ethic, a system in which productivity and moral worth are closely intertwined.
Artificial Intelligence challenge this foundation. As machines increasingly perform cognitive and creative tasks, productivity becomes less dependent on human labour. This opens up a trajectory long anticipated in economic thought. From John Maynard Keynes’ vision of reduced working hours to more recent theories of post-scarcity and abundance economies, where digital technologies enable the near-zero marginal cost reproduction of knowledge and services. A digital textbook, for example, can be distributed to one student or to 100,000 without significant additional production cost once created (abstracting for the moment from underlying energy and infrastructure costs).
Yet this transition is not predetermined. Alongside the potential for abundance, there is a growing risk of what some describe as digital feudalism: a concentration of data, infrastructure, and power in the hands of a few platform actors, where access to digital systems, not labour, defines participation and dependency. In such a scenario, AI amplifies inequality rather than alleviating it.
This is where education becomes a political actor. VET institutions are not merely adapting to economic change, their role can and should be to help shape its direction. By deciding what skills, values, and capacities are taught, they influence whether societies move toward greater autonomy, participation, and meaning, or toward deeper dependency within opaque technological systems.
Skills for a Meaning-Based Society
In one future scenario, AI enables a trajectory towards a meaning-based society, in which human activity becomes less defined by economic necessity and more oriented toward purpose, contribution, and connection. However, such a shift is not determined by technological capability alone. It depends fundamentally on political will and on how education systems prepare individuals to participate in and shape these emerging conditions.
As AI systems take over routine, predictable, and increasingly cognitive tasks, the nature of valuable human contribution shifts. What remains central are capabilities that cannot be easily automated because they are context-dependent, relational, and normative rather than procedural. These include human interaction and empathy, ethical judgment and responsibility, creativity and original thinking, contextual understanding, and the ability to navigate uncertainty.
These are not peripheral “soft skills,” but foundational competencies in AI-mediated environments. For vocational education and training, this implies a clear reorientation: moving beyond narrowly defined occupational preparation toward the development of learners who can understand, question, and actively shape the systems they operate within. The future of work, and of society more broadly, will not be determined by technology alone, but by the epistemic and practical capacities of those who engage with it.
In concrete terms, this reorientation can be structured around three interdependent domains for VET:
- Navigating AI as a Socio-Technical System
Learners require more than operational competence with AI tools. They need critical literacy regarding AI as a socio-technical system embedded in institutional, economic, and epistemic infrastructures. This includes the ability to evaluate when AI use is appropriate and when it introduces distortion or overreliance, to critically interrogate outputs rather than accept them as authoritative, and to understand how AI systems shape decisions, workflows, and what is perceived as relevant knowledge. The emphasis shifts from tool proficiency to situated judgment.
- Developing Distinctively Human Capabilities
As automation extends into cognitive and creative domains, human value increasingly lies in capabilities that are interpretive, relational, and normative rather than procedural. This includes communication and collaborative sense-making, ethical reasoning under uncertainty, contextual interpretation of ambiguous situations, and creativity understood as reframing and recombination rather than production of outputs. These are not secondary “soft skills,” but core capacities of action in AI-mediated environments.
- Building Adaptive and Fundamental Learning Competences
In rapidly evolving technological landscapes, vocational identity can no longer be anchored in stable occupational profiles. Education must instead prioritise adaptive capacity: the ability to reorient across shifting systems of work, knowledge, and tools. This implies learning as continuous reconfiguration rather than linear acquisition, tolerance for ambiguity, and the ability to revise mental models in response to changing systems. “Learning how to learn” becomes a form of epistemic resilience rather than a generic competence.
VET Teaching Example: Digital Disconnection as Critical Pedagogical Tool
Educational settings now operate in an environment of permanent connectivity. Information is instantly available, attention is constantly pulled by digital inputs, and many cognitive tasks are increasingly supported or replaced by digital systems. AI will intensify this trend by making access to answers and outputs even more immediate. In this context, the key challenge is no longer access to technology, but the ability to use it deliberately, critically, and appropriately.
Digital Disconnection as a Learning Exercise
One practical way to develop this capacity is through structured periods of digital disconnection. Learners temporarily operate without selected digital tools. The aim is not rejection of technology, but the creation of a contrast condition in which habitual dependencies become visible. Learners typically notice how quickly they default to external support, how they respond to uncertainty, and how their problem-solving changes when tools are unavailable. The key learning emerges through individual reflection and group discussion, where learners analyse what changed in their thinking, where dependency appeared, and how their reasoning adapted.
From this applied exercise, several shifts in the learning process become visible in practice:
- from looking for simple answers → to acknowledging complexities and forming critical questions
- from focusing on producing outputs → to focusing on thinking processes and experiences
- from passively receiving knowledge → to actively constructing understanding
Digital disconnection is one example of a pedagogical tool for making technological dependence visible and open to critical reflection. As AI becomes more embedded in everyday work and learning, the ability to deliberately interrupt access and critically reflect on its effects becomes a necessary civic capacity for individuals operating in digital systems, an essential condition for sustaining meaningful democratic agency under growing technological dependence.
AI-powered Efficiency – Education-driven Human Agency
A dominant narrative frames AI primarily as a mechanism for efficiency: automating tasks, accelerating production, and knowledge output. While these effects are significant, they obscure a more fundamental transformation. AI does not simply speed up knowledge work; it reshapes the conditions under which knowledge is produced, validated, and used by reducing the friction of cognition itself. When answers become instantly generatable and outputs continuously optimised, education can no longer define its purpose as the reproduction of knowledge. Likewise, assessment frameworks centred primarily on outputs lose meaning and validity when outputs can be decoupled from understanding. This requires a shift in how learning is understood and designed: away from reproducing correct answers, and toward developing inquiry, reflective engagement with process, and the construction of meaning under conditions of uncertainty.
More broadly, AI reshapes the relationship between humans, knowledge, and action. As cognitive work becomes increasingly mediated, automated, and externally supported, education can no longer be organised around stable knowledge transmission or predefined competencies. Instead, its role shifts toward cultivating learners who can navigate uncertainty, critically engage with socio-technical systems, and exercise independent judgment in environments saturated by intelligent technologies. The central challenge is no longer how education adapts to AI, but what forms of human capability it must actively sustain when cognition itself is increasingly distributed across human-machine systems.
Susanne Julinek
Note on AI-assisted Writing:
The conceptualization and ideas contained within this article are the author’s. Yet, AI assistants helped with writing the first draft and formulate broad ideas into precise words. Ideas and concepts were evaluated, modified, fine-tuned, and double-checked.
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Image generated with Gemini3.1 (w/ Nano Banana 2)
