Over the past two years, conversations about education have been increasingly shaped by the arrival of powerful AI systems. Parents worry that, with AI at their beck and call, students will stop learning to write, but also that ‘writing won’t matter’ and that their children will be ‘left behind’. Teachers worry that, with AI ‘assistance’, assignments will lose their meaning, but also that they need to 'unlock' AI so their students can 'leverage' the technology effectively. Policymakers worry about academic integrity and the politics of assessment, but also, vaguely, that something more profound has gone horribly wrong. And young people themselves are caught between excitement and anxiety, unsure whether these tools will help them or replace them – and what the world will look like either way.

All these concerns are understandable. But they fail to reach the heart of the matter. The most important insight is not technological or political or educational – or, even, philosophical - at all. It is existential. It is this:

AI is not undermining the system; it is revealing the system’s underlying metaphysics.

To understand what AI is showing us, we must begin not with analysis of the algorithm or the machine, but appraisal of ourselves — with concern for the nature of human intelligence, the lived reality of learning, and the deep forms of knowledge that shape our lives long before they ever become symbols on a page, entries in a spreadsheet, information in a database, or numbers in a ledger.

I. Big-K Knowledge: The Living Ground of Learning

Every human being carries within them a vast reservoir of what we can call big-K Knowledge — the tacit, felt, embodied, affective, pre-conceptual understanding that accumulates through lived experience. In their own ways and to their own limits, all other living creatures share it.

In humans, this is the knowledge that forms when a child learns what trust feels like – or abandonment; or what joy – or humiliation – does to the body; or how to read the emotional weather of a room or how to spark the motivation that galvanises a team. It is the knowledge that grows when an adolescent discovers a talent, survives a disappointment, juggles a relationship, conquers a fear, or realises that a belief or value they inherited no longer fits the person they are becoming.

Big-K Knowledge is not boxed and archived; it is lived. It is not received; it is undergone. It is not symbolic; it is metabolic.

It is shaped by:

• encounter

• emotion

• memory

• social belonging

• developmental change

• the body’s responses to the world

• the organism’s ongoing attempt to make sense of its own experience

This is why human beings are autopoietic beings — living organisms engaged in the continuous work of self-making.

Human intelligence is not a detached computational process, founded in symbolic competence. It is a way of navigating a world that matters to us, because we are vulnerable, embodied, relational, and alive.

II. Small-k knowledge: The Symbolic Residue of Experience

Alongside this vast, tacit reservoir sits a much smaller, ultimately limited category, which we will call small-k knowledge — the explicit, symbolic, representational knowledge that can be captured, inscribed, standardised, transmitted ... and tested. It is unique to humans.

This includes:

• facts

• definitions

• formulas

• conceptual frameworks

• the structured representational content of textbooks and exams

Small-k knowledge is essential. It is the stuff of which recorded history is made. It allows societies to preserve insights, share learning, pool expertise, coordinate action, and build institutions. But it is, by definition, secondary. It is the residue of lived experience, not a substitute for its source.

The problem is not that schools teach small-k knowledge. Of course they do; and of course they should. Small-k, well understood and well-integrated, is indispensable. The problem is that the system has come to treat small-k knowledge as the more or less exclusive goal of schooling, and big-K Knowledge as an optional extra — something that happens well enough “naturally,” outside the formal curriculum, in the margins of pastoral care, or, better still, given its messiness, outside of school altogether.

This inversion has consequences.

III. The Metaphysical Error of Modern Schooling

Modern secondary education is built on a disproportionally representational model of learning. It assumes that:

• knowledge is symbolic

• learning is the acquisition of symbols

• progress is measured by the reproduction of symbols

• the successful reproduction of symbols guarantees that they have been internalised 

This model aligns neatly with the logic of computation. It treats students as if they were symbolic processors - amassing and storing data - rather than living organisms, with an appetite for life. It privileges clarity over complexity, accuracy over ambiguity, stability over flux, and standardisation over idiosyncrasy.

But adolescents are not symbolic processors. They are metabolically powered, emotionally saturated, socially entangled beings whose intelligence, to become alive, requires grounding in big-K Knowledge. Their development is commensurate with identity formation, peer relationships, emotional enhancement, ego expansion, and the slow integration of experience into tacit understanding. And the world for which school is theoretically readying them is not a well-oiled machine or a complicated algorithm; it is complex, ambiguous, in permanent flux, and, well, odd. 

School’s metaphysical foundation is therefore misaligned with the nature of the learners it serves – and with the lives they will lead.

IV. What AI (CCA) Reveals About the System

Enter AI — or, more accurately, Combinatorial and Calculative Acumen (CCA) – a term which better captures the symbolic processing power of (so-called) AI, which excels at rearranging information but which lacks lived experience or actual understanding.

AI/CCA systems excel at manipulating symbols. They can access, summarise, rephrase, reorganise, and recombine small-k knowledge with astonishing fluency and tireless attention. But they do not understand what they produce, nor do they feel its consequences, nor do they care for its implications. This is definitional, for they have no big-K Knowledge: no lived experience, no emotional history, no sense of meaning, no stake in the world. They operate entirely as affectless artificers, within the symbolic realm.

So when AI/CCA can generate an essay, answer a comprehension question, or produce a model exam response more efficiently than a student, this does not indicate the machine is intelligent. It indicates that, if the AI/CCA can attempt the task at all, the task itself has been defined in symbolic terms. Lived life – with its actions, reactions, and consequences – is on hold. 

This, then, is the revelation:

AI/CCA excels at the tasks schools have spent decades treating as the core of learning, precisely because these tasks are mechanistic not metabolic.

The algorithms are not breaking the system. The algorithms are showing us what the system has become: a training set for combination and calculation; an exercise in affectless representation. AI/CCA is a mirror held up to the metaphysics of schooling. And what it reflects is a system that has drifted too far from the living reality of human intelligence.

V. The Adolescent Reality the System Overlooks

Meanwhile, the young people in our classrooms are engaged in the most intense period of big-K Knowledge formation in their lives, with consequences that will leave a permanent mark. They are learning:

• how to belong

• how to relate

• how to cope

• how to interpret themselves

• how to navigate complexity

• how to make meaning

These are not symbolic tasks. They are existential.

Yet the curriculum is centred on symbolic reproduction at precisely the stage when young people are, as still young people, least well-equipped — and, for good reason, least inclined — to treat learning as a purely representational activity.

VI. Toward a Life-Aligned Education

If we take seriously what AI/CCA reveals, then the future of education is not about optimising retention or chunking content or banning tools or managing classrooms or policing behaviour. It is about re-aligning schooling with the nature of human learning.

A life-aligned education would:

• begin with experience rather than abstraction

• understand the quality of relations as foundational

• treat dialogue and dialectics as central rather than peripheral

• value open-ended interpretation, reflection, and judgement

• recognise that understanding arises from big-K Knowledge

• accept that big-K cannot be ‘objectively’ assessed

• use AI as a catalyst for human sense-making, not a substitute for it

In such a system, the question would not be “How do we stop students using AI?” but “How do we design learning experiences that AI cannot complete because they hinge on lived understanding?”

VII. Conclusion: A Moment of Metaphysical Clarity

AI’s destabilisation of an otherwise supposedly intact system is no bolt from the blue. It is, rather, the intensification of a decades’ long unveiling, revealing the misguided  metaphysical assumptions that have shaped modern schooling. The challenge for educators, policymakers, and communities is not to rescue the old system from the denuding logic of the algorithm, while leaving its other deficiencies intact, but to abandon school’s less-than-human aspirations and build a new version of educational experience that honours the living intelligence of the young.

If we can do that — if we can elevate big-K Knowledge to its rightful place as the ground of learning — then AI will not diminish education. It will help us (re)discover what education is for.

The machine has not shown us our failure. It has shown us our forgetting. It invites us to remember.