Category: Foundations

The Technocratic Paradigm

Pope Leo XIV’s recent encyclical on artificial intelligence is being discussed primarily as a theological document. That is understandable. Yet it would be a mistake to read it only within ecclesial or doctrinal categories. What makes the text important is not simply that the Church has entered the AI debate, but the level at which it enters it.

The encyclical treats artificial intelligence not merely as a technological development requiring regulation or ethical oversight, but as part of a broader civilisational transformation affecting how human beings understand responsibility, agency, judgement, and formation itself. Its central concern is not whether intelligent systems are useful. Clearly, they are. The concern is whether societies organised increasingly around optimisation, automation, and immediacy gradually weaken the conditions under which human beings develop the capacity to govern such systems wisely.

What the encyclical calls the technocratic paradigm at the level of institutions and social organisation is the institutional expression of what delegated thinking describes at the level of individual cognition. In both cases, interpretative responsibility progressively disappears behind systems optimised for efficiency, prediction, and procedural execution.

The convergence itself is intellectually significant. Different anthropologies and entirely different philosophical premises arrive at the same educational intuition: human judgement forms through encounters that resist immediate assimilation. This convergence may itself constitute evidence that both traditions are tracking something structurally real about human cognitive formation rather than merely expressing a cultural preference. When traditions beginning from incompatible premises converge not merely on similar conclusions but on the same description of what those conclusions require educationally, the independence of the routes strengthens the case that the conclusion tracks something real.

Whether one begins from the encyclical’s theological anthropology, grounded in the irreducibility of the person created in the image of God, or from a more philosophical account centred on framework interpretation and human agency, both traditions identify the same structural vulnerability in AI-mediated environments: the displacement of the conditions under which judgement forms.

Responsibility Cannot Be Delegated

One of the recurring illusions surrounding AI is the belief that automated systems somehow remove human accountability because they appear objective. The encyclical rejects this directly and correctly.

Algorithms do not dissolve moral agency. They relocate it.

Someone still defines the parameters. Someone still determines what is measured, optimised, ignored, or prioritised. Someone still answers for the consequences. The apparent neutrality of automated systems often conceals rather than eliminates the human decisions embedded within them.

What is specific to algorithmic systems is not simply that responsibility is delegated. All institutions delegate responsibility. The distinctive feature of algorithmic systems is that the interpretative choices embedded in their design become progressively invisible during operation. The system appears neutral while silently reproducing prior assumptions, priorities, and exclusions.

This differs even from ordinary bureaucratic diffusion of responsibility. In institutional systems, responsibility may become distributed across many actors, but the chain of decisions remains in principle traceable through procedures, records, and identifiable acts of judgement. In algorithmic systems, by contrast, interpretative assumptions become embedded in technical architectures whose operation may be opaque even to those who deploy them. The concealment is structural rather than merely practical.

As these systems scale, the distance between the interpretative choices embedded in design and the consequences generated in practice increases dramatically. Responsibility becomes technologically mediated at scale. Power concentrates within infrastructures that increasingly appear detached from visible human judgement, even as they continue to embody it at every level.

The educational significance of this is difficult to overstate. A society that increasingly relies on systems whose embedded assumptions are hidden from ordinary perception requires citizens capable not merely of using systems competently, but of interrogating the frameworks within which those systems operate.

Plato, Friction, and the Conditions of Learning

The encyclical’s educational section is its most philosophically interesting part. It warns repeatedly against what it calls a culture of immediacy: the progressive disappearance of the interval between question and answer.

Remarkably, the text invokes Plato in this context. Genuine understanding, it argues, emerges through the intellectual friction produced by rubbing concepts and experiences together like flint until insight appears. Learning requires resistance, dialogue, uncertainty, and effort. Understanding is not downloaded intact into consciousness. It forms through sustained engagement with situations that do not immediately yield resolution.

This converges closely with a philosophical tradition extending from Aristotle through Dewey and phenomenological accounts of expertise such as Hubert Dreyfus’s. In each case, formation depends not simply on information acquisition but on encounters that resist immediate assimilation into existing frameworks.

Immediacy weakens this process because it removes the interval in which thought begins.

That interval is not merely time. It is the period during which the absence of an available answer generates the pressure that makes interpretation necessary. The question forms because the situation remains unresolved long enough for uncertainty to become intellectually productive. When answers arrive before this pressure fully develops, the question itself never fully forms either.

What fails to form is not the surface query but the deeper question of whether one’s existing conceptual frameworks are actually adequate to the situation at hand. That question only emerges when reality resists immediate resolution.

The encyclical’s educational insight therefore goes beyond a generic defence of slower learning. It identifies something structurally important about human formation itself: judgement develops through sustained encounter with situations that cannot immediately be absorbed into available categories.

This is why one of the encyclical’s most striking claims is that educating for the digital age also means educating people for when and why not to use AI.

Prediction Is Not Judgement

The relevant distinction is not between optimisation and judgement in the abstract. Increasingly capable AI systems can clearly assist in sophisticated forms of deliberation across competing considerations.

The relevant distinction is between operating within predefined frameworks and determining whether those frameworks remain adequate to the situation itself.

Even highly sophisticated AI systems remain structurally framework internal. They may propose reframing, identify tensions, or detect inconsistencies within the objectives they have been trained to optimise. But the adequacy of those objectives themselves remains outside the horizon the system can independently establish.

This does not mean human beings stand outside all frameworks in some absolute sense. Human judgement is also historically situated, fallible, and shaped by inherited assumptions. The difference is that human beings can commit themselves to revising even their framework evaluating assumptions through encounters with resistant reality in ways not fully determined by prior optimisation structures.

Artificial intelligence can predict. It can optimise. Increasingly, it can even assist sophisticated forms of deliberation. But judgement appears precisely where rules, distributions, and procedural criteria no longer fully settle the matter.

The issue is therefore not whether AI can assist deliberation. Clearly it can. The issue is whether the conditions through which human beings develop the capacity to question frameworks themselves remain preserved.

Much of the current debate about artificial intelligence treats different human capacities as if they belonged to a single scale: more data, faster processing, better outputs. From that perspective, systems that outperform us in some tasks appear to be gradually approaching, or even surpassing, human judgement. But the activities usually grouped together under that heading are not the same kind of thing. To predict, to decide, to deliberate, and to judge are related capacities, but they are not interchangeable.

Prediction: Estimating What Is Likely

Prediction concerns likelihood. It identifies patterns in past data in order to estimate what is likely to happen next. Weather forecasts, fraud detection systems, recommendation engines, and language models all operate substantially in this way. They estimate probable continuations, probable behaviours, or probable outcomes.

Predictions may inform action, sometimes decisively, but they do not settle it. A model may predict that a borrower from a particular postcode is more likely to default. That may be statistically useful. It does not by itself determine whether granting the loan would be fair, prudent, or socially desirable.

Decision-Taking: Selection Within Given Criteria

Decision-taking means selecting among alternatives according to criteria that have already been set. A navigation system chooses the fastest route. A hiring filter ranks candidates based on specified criteria. A school admissions algorithm allocates places according to published priorities.

This can be highly effective because the system is not determining the worth of the criteria themselves. It is operating within them. Choosing the fastest route is not deciding where one ought to go. Ranking candidates by chosen metrics does not decide whether those metrics deserve priority.

Decision in this sense is powerful, but it remains bounded by a prior human act: someone has already decided what counts.

Deliberation: Weighing What Cannot Be Fully Measured

Deliberation begins when no single rule or metric resolves the matter. It involves weighing competing goods, considering consequences, interpreting circumstances, and recognising that some values cannot be reduced to one scale. 

Should a hospital prioritise urgency, age, prognosis, equality, or waiting time? Should a school prioritise examination performance, inclusion, character formation, or social mobility? Should a company protect jobs, maximise profit, or preserve long-term trust?

These are not merely technical questions. They involve rival goods that cannot be fully compared. Deliberation does not process considerations toward an optimum. It weighs what cannot be fully measured and reaches a position that remains contestable.

Judgement: Commitment Under Responsibility

Judgement is deliberation that has issued in commitment. It includes elements of prediction, decision, and deliberation, but it cannot be reduced to any of them, because what distinguishes it is not the quality of the reasoning alone, but the nature of the commitment it produces.

Judgement occurs when someone determines what ought to be done here and now, under conditions where certainty is unavailable, and consequences matter. A machine may recommend, rank, or simulate an argument. But it cannot bear responsibility in the human sense.

A system can be audited, corrected, regulated, or even blamed instrumentally. That is not the same as answering for a determination. Responsibility requires a subject capable of recognising fault, offering justification, and bearing the consequences of being wrong.

Systems may be part of the chain of action, but they do not stand within it as accountable agents.

A Simple Example

Imagine a university awarding a scholarship. 

A predictive model estimates which applicants are most likely to complete the degree with high marks. That is a prediction. An algorithm then ranks candidates based on grades, income levels, and predefined weightings. That is decision-taking. A human committee notices that one student’s lower grades followed a year of serious family difficulty. Members discuss whether resilience, disadvantage, or future potential should matter more than raw attainment. That is deliberation. The committee then chooses and publicly stands behind the decision. That is judgement.

The acts are connected, but they are not the same. Each requires something different of the human being involved.

Why This Matters for Education

If education is understood mainly as the production of correct outputs, much of its domain becomes vulnerable to automation. Systems can already predict, classify, rank, retrieve, summarise, and generate competent responses at speed. If these are treated as the highest aims of learning, machines will increasingly outperform students in the very tasks schools reward.

But education has a deeper purpose.

Students need to learn what predictions can and cannot tell them. They need to understand how decisions are shaped by criteria chosen in advance. They need practice in weighing goods that cannot be reduced to a formula. Above all, they need opportunities to commit themselves to conclusions they can justify and defend.

That kind of formation does not arise through exposure to information alone. It develops through use, through practice that requires one to take a position and answer for it.

Conclusion

Judgement begins where no metric fully determines what ought to matter, where someone must decide, and where that decision must be defended and owned.

The more we distribute execution to intelligent systems, the more essential it becomes to form individuals capable of governing what those systems do not and cannot settle.

That capacity, to deliberate under genuine uncertainty and to answer for the determination reached, is what education must still form. No system can form it on our behalf.

A Misplaced Anxiety

Much of the current debate about artificial intelligence in education begins in the wrong place.

We worry that students may use AI systems to complete tasks once treated as evidence of learning: essays, problem sets, summaries, and explanations. We ask how to prevent misuse, preserve integrity, and defend established forms of assessment.

These concerns are real. But they do not reach the deeper issue.

Artificial intelligence did not create the gap between performance and understanding. It exposed it.

What appears as technological disruption may reveal something older and more structural: a weakness in how education has often equated visible output with genuine formation.

The Proxy Assumption

For long periods of educational history, producing the answer usually required substantial intellectual effort. There was no easy alternative source. Under those conditions, visible performance often functioned as a reasonable proxy for underlying understanding. But a proxy is not an identity.

To produce a correct result is not necessarily to possess the capacities that the result appears to display. Correct outputs may arise from memorisation, imitation, procedural training, narrow rehearsal, or temporary pattern recognition. They may indicate learning, but they do not guarantee it.

What artificial intelligence has changed is not the truth of this distinction, but our ability to ignore it.

Tasks once sustained by the practical difficulty of imitation can now be completed fluently, instantly, and at scale. The proxy assumption, long tolerated, has become unstable.

Artificial Intelligence as a Diagnostic

Artificial intelligence functions less as an enemy than as a diagnostic. It reveals what our tasks were demanding of learners.

When a generative system can complete an assignment with little apparent loss of value, a serious question arises: what, precisely, was the task cultivating in the learner?

This does not mean the task was worthless. Many traditional exercises built fluency, discipline, familiarity, and background knowledge. But their educational value may not have been the value often attributed to them.

Activities treated as evidence of thought may have depended more heavily on execution than on judgement.

AI now performs execution with increasing competence. In doing so, it forces institutions to distinguish what can be produced from what must be formed.

Result and Formation

The present moment makes one distinction difficult to avoid: the distinction between obtaining a result and being changed by the effort of reaching it.

A student may submit a correct mathematical solution generated by AI. Yet when asked why a particular step was necessary, how the reasoning would change in a new case, or where the method would fail, uncertainty appears. The result exists. The underlying capacity may not.

This does not mean formation occurs only through solitary struggle from first principles.

Students often learn through examples, models, imitation, and guided solutions. A proof carefully studied can teach more than one poorly invented. An elegant solution, when examined attentively, may cultivate greater understanding than blind trial and error.

But in such cases, formation occurs only when the learner reopens the process: reconstructing reasons, testing alternatives, identifying limits, and making the logic their own.

Outputs may support formation. They do not automatically constitute it.

An Older Warning

In the Phaedrus, Plato recounts a warning about writing. Writing, he is told, may give the appearance of wisdom without its reality.

Historically, Plato was too pessimistic. Writing enabled forms of thought impossible without it. Philosophy, science, law, and memory itself were transformed by textual culture.

Yet the warning still contains an enduring insight. A technology may extend intelligence while also loosening the connection between possession and acquisition, between access to knowledge and earned understanding.

Artificial intelligence raises a related question today. It can generate forms associated with learning (explanation, argument, solution, summary) without ensuring that the learner has undergone the intellectual work those forms once implied.

What Education Must Now Clarify

If intelligent systems can generate answers instantly, education faces a structural choice.

It can concentrate primarily on the use of the policing tool. Or it can clarify what education was meant to cultivate all along.

Understanding becomes visible when a learner can reconstruct reasoning in a new situation, recognise where a method fails, and defend conclusions that are genuinely their own.

These capacities belong to judgement. They may be assisted by tools, but they cannot be substituted by them.

Conclusion

Artificial intelligence did not break education. It revealed where education had confused outputs with understanding, performance with formation, and visible success with the formation it was meant to produce.

The central task now is not to preserve familiar assignments merely because they are inherited.

It is to design forms of learning in which result and formation cannot be cleanly separated — where producing the answer already requires becoming capable of understanding it.

Education and Human Judgment in the Age of Artificial Intelligence

Artificial intelligence changes not only what we can do, but also how we learn to think. The central task of education is therefore the formation of judgement.

For centuries, education prepared individuals to execute intellectual tasks: solving equations, analysing evidence, drafting arguments, diagnosing problems and producing solutions. Mastery meant learning how to apply knowledge correctly and efficiently, and professional expertise was largely defined by the capacity to perform complex cognitive work reliably.

Today, that assumption is beginning to change. Systems capable of generating explanations, analysing data, producing text and assisting decision-making are rapidly becoming part of our intellectual environment. Tasks that once required years of training can now increasingly be initiated — and sometimes completed — by machines.

Much of the discussion surrounding this transformation focuses on productivity, efficiency or technological capability. Yet the deeper question raised by artificial intelligence is not primarily technological. It is educational.

If intelligent systems begin to perform many of the cognitive tasks once associated with expertise, what then becomes the purpose of education?

For much of the modern era, education has trained individuals to execute. Students learned procedures, mastered analytical techniques and acquired the intellectual tools necessary to produce correct results. Execution became the foundation of professional competence and the measure of intellectual achievement.

Artificial intelligence is now rapidly expanding the automation of execution. Machines can assist with tasks that require defined expertise: analysing evidence, writing reports, generating explanations and proposing solutions. This development does not make human knowledge irrelevant. On the contrary, it clarifies the importance of a different human capacity.

Judgement is not simply the production of an answer. It is the capacity to determine which answers matter, why they matter and whether they should be trusted. It involves recognising context, weighing competing considerations, anticipating consequences and assuming responsibility for decisions. In a world where machines increasingly produce outputs, the value of judgement becomes clearer.

Results and Formation

Artificial intelligence introduces another educational challenge: the distinction between obtaining results and being intellectually formed through the process of learning.

Correct answers have never been the ultimate goal of education. Learning involves intellectual struggle. Students encounter difficulty, confront contradictions, revise arguments and gradually reconstruct their understanding. Through this process, knowledge becomes internal rather than external. The learner is transformed by the act of thinking.

Artificial intelligence makes it possible to obtain results without passing through that formative process. A student can request a complete mathematical solution, a structured essay or a plausible explanation within seconds.

The output may be correct. Yet if the reasoning behind it has not been reconstructed by the learner, something essential may be missing. The student possesses the answer but has not undergone the thinking-through that makes understanding genuine.

This distinction between result and formation becomes one of the central educational questions of the age of intelligent machines.

Language and Thought

Another dimension of this transformation concerns language itself.

Language is not merely a tool for expressing thought; it shapes the categories through which we understand the world. Philosophers such as Ludwig Wittgenstein emphasised that the limits of our language shape the limits of our thinking.

Artificial intelligence increasingly generates the language that circulates through our intellectual environments: summaries, explanations, reports, analyses and narratives. As algorithms produce a growing proportion of the texts we read and write, they inevitably begin to influence how ideas are framed and understood.

Education must therefore cultivate not only knowledge but awareness of the linguistic frameworks through which knowledge appears.

Seeing Structure

Human understanding does not arise from the accumulation of isolated fragments of information. Gestalt psychology showed that the mind perceives meaningful structures rather than disconnected elements. Understanding occurs when relationships between ideas become visible, and concepts begin to form coherent patterns.

Artificial intelligence excels at producing informational fragments: explanations, examples, summaries and answers. But education must ensure that learners still develop the ability to perceive the conceptual structures that connect those fragments.

Without that capacity, knowledge risks becoming a collection of correct statements without a coherent understanding.

An Ancient Question

Concerns about intellectual technologies are not new.

In the dialogue Phaedrus, Plato recounts a myth in which the Egyptian god Thoth presents writing as a gift that will improve wisdom and memory. The response is sceptical. Writing, the king warns, may create the appearance of wisdom without genuine understanding:

“You provide your students with the appearance of wisdom, not with its reality.”

Plato feared that writing might allow people to possess knowledge without truly understanding it. Artificial intelligence raises a similar question today. When answers become instantly available, education must ensure that the intellectual processes through which understanding develops are not lost.

The Educational Task Ahead

Artificial intelligence will undoubtedly continue to transform how knowledge is produced, distributed and applied. The question is not whether education should use these tools. It must.

The question is: what kind of minds should education form in a world where machines can increasingly execute cognitive tasks?

The answer lies in cultivating intellectual capacities that cannot be automated: discerning what matters, recognising structure and context, weighing competing considerations and assuming responsibility for decisions. These capacities define judgement.

In the age of intelligent machines, the central task of education is not to compete with machines in execution, but to form individuals capable of governing the systems they use. Technology expands what we can calculate and create, but it also transforms the intellectual environment in which thinking develops.

Execution can be delegated.

Judgement cannot.

And preserving our capacity to judge — to weigh reasons carefully, determine what deserves priority and assume responsibility for our conclusions — may therefore become one of the defining responsibilities of education in the decades ahead.

Why execution can be delegated, but judgment cannot.

Artificial intelligence challenges traditional notions of authorship and judgment. This foundational essay explores why execution can be delegated — but genuine intellectual responsibility cannot.

During the Renaissance, artistic creation rarely conformed to the modern myth of solitary genius. Master painters worked in workshops. They conceived the composition, drafted initial sketches, set proportions, and ensured coherence. Apprentices executed large sections of the work — preparing surfaces, applying pigment, and completing secondary figures.

What mattered was not who held the brush, but who held the vision.

Authorship did not depend on the number of brushstrokes personally applied.

It depended on intellectual architecture.

Execution could be delegated.

Conception could not.

What distinguished a Leonardo or a Raphael was not manual exclusivity but intellectual architecture: the capacity to conceive a form, organise complexity, and impose unity upon multiplicity.

The analogy with artificial intelligence is not superficial; it is structural.

Today, AI systems generate text, images, code and analysis at extraordinary speed. They extend productive capacity as much as apprentices once did. They accelerate execution, expand output and assist refinement.

But they do not originate the vision.

They do not determine which questions are worth asking, which distinctions matter, or which direction inquiry should take. They operate within parameters set by human intention. They process patterns. They do not inhabit meaning. There lies the limit.

The architecture remains ours — or it dissolves.

This distinction becomes especially urgent in education.

A recurring anxiety has emerged: when students use AI tools for academic work, does authorship disappear? Has originality been compromised?

The concern is understandable, but it is also incomplete.

Plagiarism, in its classical sense, concerns the misappropriation of identifiable words or ideas. Generative systems complicate this definition because they produce novel formulations rather than copying from traceable sources. From a purely textual perspective, the output may be technically original.

But education has never been merely about textual originality. It has been about intellectual formation.

Authorship, in its deeper sense, lies not in phrasing but in ownership of questions, responsibility for arguments, and the capacity to justify conclusions. It resides in the tacit dimension of understanding — the integration of evidence, context and judgement that cannot be fully reduced to explicit procedure.

If a learner defines the problem, evaluates evidence critically, and assumes responsibility for interpretation, AI may serve as an instrument — like the apprentice in a Renaissance workshop. If the machine performs the thinking and the human merely submits the output, authorship becomes hollow, even if the wording is new.

The decisive distinction is therefore not between “AI” and “no AI.”

It is between assistance and substitution.

Used reflectively, AI can clarify structure, expose weaknesses in reasoning and stimulate further inquiry. It can serve as cognitive scaffolding. Used uncritically, it risks encouraging passivity — replacing the very processes academic work is meant to cultivate: doubt, revision, discernment and judgement.

The anxiety surrounding AI often masks a deeper discomfort. We fear learners will cease to think for themselves. Yet history suggests that tools do not determine outcomes; frameworks do. Calculators did not abolish mathematics. Digital archives did not abolish research. Each technological shift compelled a clearer articulation of what intellectual responsibility entails.

Artificial intelligence now performs a similar function. It serves as a stress test for educational design. If a task can be fully outsourced to a generative system without intellectual loss, perhaps it never required genuine judgment in the first place.

This is the uncomfortable implication.

There was no golden age of critical thinking suddenly destroyed by algorithms. What exists is a moment that exposes fragilities. AI does not create the weakness. It reveals it.

The central ethical question is therefore no longer whether a text is original. It is whether the thinking behind it is genuinely human — reflective, accountable and internally owned. Judgement is not merely a cognitive operation; it is a form of responsibility.

In the Renaissance workshop, greatness lay not in the brush but in the mind that guided it. In the age of artificial intelligence, the same truth holds.

Technology expands what we can calculate. Education must strengthen our capacity to judge.

Execution can be delegated. Judgment cannot.

In the age of intelligent machines, preserving human judgment is not resistance to technology. It is the condition for using it well.

Developing this argument

• AI as a Stress Test for Critical Thinking →
• AI, Plagiarism, and Intellectual Authorship in the IB Diploma Programme →