Education must go beyond the mere production of words

“The end then of Learning,” wrote John Milton in 1644, “is to repair the ruines of our first Parents.” The image is hard to improve: education as repair, as recovery, as the restoration of capacities diminished by sin and neglect. 

Four centuries later, in the age of generative artificial intelligence (AI), that image has become urgent again — because we are now surrounded by a technology that offers to perform, on demand, much of what we had long assumed education required us to do ourselves.

I came across Milton’s passage by chance while browsing a collection of the English writer’s works and opening it to his 1644 tract Of Education. Milton was not writing about algorithms. Yet he saw with unusual clarity the educational error that AI now magnifies: the confusion of language with learning. 

Language, he wrote, is “but the instrument conveying to us things useful to be known.” He warned against mistaking command of words for possession of the solid things those words are meant to disclose. He joined language to substance, sequence to maturation, and study to direct contact with reality — principles that four centuries have not made less urgent.

No technology in recent memory has so enlarged the instrument. Large language models such as ChatGPT can summarize books, draft essays, organize research notes, translate passages, generate code, and imitate the prose that schools and universities have long taken as evidence of education. 

Used with discipline, they can be genuinely useful. A professor may use them to prepare discussion questions. A researcher may use them to survey literature more quickly. An administrator may use them to accelerate routine writing. It would be foolish to deny their utility.

But utility is not the same as education, and AI magnifies an older weakness. It tempts us to mistake verbal fluency for understanding itself. A student can submit polished prose without having really grappled with the question. A researcher can produce a competent summary without having seen the problem clearly. A professional can sound informed without having formed a judgment. The danger is not only dishonesty — it is substitution. 

For Catholic education, that substitution matters because learning is not the production of acceptable performances but the formation of a person capable of truth, judgment and responsibility.

Milton saw a version of this in his own day. He criticized the practice of demanding “Themes, Verses and Orations” from young students before their minds had been formed by “long reading and observing.” He objected to asking for finished performances before the underlying powers had matured. 

Generative AI industrializes exactly that pedagogical mistake. It supplies finished language before the student has undergone the reading, questioning, hesitation and revision that make language meaningful. What Milton regarded as a mistake of sequence, AI turns into a system.

This matters because education is not built from answers alone. Every answer worth teaching was once a response to a question someone genuinely asked. 

Students do not assimilate knowledge merely by receiving conclusions — they must be brought into the question. That is why the principal agent of education is the student. No one can learn in another’s place. A tool may assist instruction; it cannot do the learning for the student.

The teacher’s role accordingly becomes more important in the age of AI, not less. A real teacher is not merely a distributor of content. A real teacher is an experienced guide in inquiry: someone who knows what the student has not yet seen, what distinctions must be made, what confusion needs exposing, and what question should come next. The best classroom is not a transfer of information from one container to another. It is a living act of thought. That is why seminar, disputation, laboratory, tutorial and serious conversation retain their force even when information itself becomes cheap.

We tend to celebrate knowledge: facts accumulated, results confirmed, information stored. But as the biologist Stuart Firestein has argued, discovery begins not only with what we know but with a disciplined sense of what we do not yet understand. That frontier is where large language models reach their limit. They can reorganize the archive with astonishing fluency, but they cannot inhabit uncertainty, pose a genuinely new question, or take responsibility for truth.

This clarifies why certain acts cannot be delegated to machines without ceasing to occur at all. Attending carefully to a text, weighing conflicting evidence, judging whether a conclusion is warranted, taking responsibility for what one claims — these are not ancillary tasks. They are the work by which a mind is formed. 

No machine can perform them in our place — not because machines lack processing power, but because these acts have no effect unless a person performs them. Their purpose is not to produce an output. It is to form the one who does them.

Education worthy of the name has always understood this. Its end is not the delivery of content, however accurate. It is the formation of persons capable of judgment, attention and intellectual honesty. That formation requires a genuine encounter with difficulty — the friction of a hard text, the resistance of a problem that does not yield quickly, the discomfort of revising what one believed. It requires embodiment as much as intellect: reading slowly, speaking in one’s own voice, accepting the cost of standing behind one’s words. A person does not become capable of truth by managing information alone. Wisdom is formed in contact with reality, not in its simulation.

The deepest challenge of AI in education is therefore not academic integrity, though that problem is real. It is whether we will allow our schools and universities to define learning as the production of acceptable outputs. If that is our standard, outsourcing will always look like efficiency. But if education is the formation of judgment, substitution becomes self-defeating.

What should institutions do? The answer is neither panic nor blanket prohibition. It is pedagogical redesign. More writing done in class. More oral defense of arguments. More seminars organized around live questions rather than passive downloads of information. More laboratory and studio work in which students must explain not only what a result shows but what it does not. 

When students use AI, one reasonable requirement is transparency: disclose what was asked, what the system produced, what was kept, what was rejected, and why. The point is not surveillance. It is intellectual ownership — the habit of standing behind one’s own thinking. Institutions should also reinvest in the teacher-scholar whose presence, judgment and intellectual seriousness cannot be automated.

The same commitment belongs at home. A dinner table free of devices, conversation across generations, reading aloud together, and the habit of asking children not only what they think but why — these are small schools of freedom. They teach that education is not the production of impressive sentences. It is the formation of honest minds.

The moment we are living through is, in this light, less a crisis than a clarification. AI has not created new educational problems; it has made old ones impossible to ignore. The habit of rewarding performance over understanding, fluency over depth, and polish over genuine engagement was already present in our institutions before the first language model was trained. AI simply industrializes and accelerates those habits until their emptiness becomes undeniable.

That may be its most unexpected gift. If this disruption forces us to recover what education was always for — the formation of minds capable of real questions, careful judgment, and responsibility for truth — then the age of AI may prove, paradoxically, to be an age of educational renewal. 

Milton’s deeper claim presses further. The end of learning is not merely competence or civic virtue, but to “know God aright, to love Him, to imitate Him, to be like Him.” Education, in that view, participates in the restoration of what sin has obscured.

No machine will ever repair those ruins. That restoration is finally God’s work before it is ours; yet, aided by grace, we must still undertake the human labor of attention, judgment and love.

Santiago Schnell is provost and professor of mathematics at Dartmouth, with adjunct appointments in biochemistry and cell biology, and biomedical data science at the Geisel School of Medicine. A mathematical biologist by training, he also writes on the Catholic intellectual tradition, the philosophy of science, and the mission of Catholic higher education.