Statement of My Educational Philosophy

I needed to write this out for a project I'm working on, so I thought I'd share it here.

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Education has never floated above its era. The Sumerian tablet house trained scribes because a temple economy needed its records kept; the factory-era classroom — bells, batches, subjects sorted by the clock — was built to staff an assembly line.

Each age has built the education it believed it needed, and each has been tempted to mistake its own model for the final one. We are now living through the largest change in the medium of knowledge in five hundred years. Copernicus moved us from the center of the cosmos and Darwin from a place apart in nature; capable machines press a third reckoning — whether we are still the only minds that matter in a room — and an education built for the age when intelligence was ours alone is not the one this moment requires.

The question I keep returning to is the one most schools have skipped: not which tools belong in a classroom, but what it means for a person to flourish when machines can do so much of the work we built schools to teach. Settle that, and the questions about tools and tests largely answer themselves; skip it, and we keep buying the ship before we have chosen the port.

My answer begins with deeper learning. In their study of American high schools, Jal Mehta and Sarah Fine (In Search of Deeper Learning) found that the moments when students were genuinely thinking — mastering something hard, doing the real intellectual work themselves rather than receiving it secondhand — happened most often not in the standard classroom but at the edges of school: in the debate round, the studio, the robotics bay, the research project, the school paper. I have spent my career trying to move that participatory work from the edges to the center, and the arrival of capable AI has made the project more urgent, not less. The discipline I hold to is easy to state and hard to honor: AI belongs in that work as a partner, not as a shortcut around it. A student can ask a machine to draft a passable speech; the machine cannot debate. The point of the assignment was never the speech.

Debate is the sharpest example of this I know, and four decades of coaching it — in public schools, independent schools, and universities, in this country and abroad — taught me what the deeper kind of learning feels like from the inside. After reading, writing, and numeracy, there is a fourth capacity schooling has always undervalued and that has suddenly become the most important thing we can teach: the ability to take a contested question, weigh evidence on more than one side, hold competing values in view at once, and arrive at a defensible judgment under pressure and in public. This is the work that does not transfer to a machine. The Greeks had a word for it — phronesis, the practical wisdom of discerning what to do in a particular, novel situation, which Aristotle insisted could never be reduced to a rule. Phronesis is the human remainder, and it does not travel alone. The act of standing and defending a position builds what a machine has no need of and no claim to: the courage to hold an unpopular view, the care for the people a decision touches, the honesty to change one’s mind when the evidence turns, and the resilience to keep learning in a world that will not sit still.

The same conviction runs through the rest of what I build, and debate is only its sharpest case. I have designed programs in which students launch ventures, conduct original research, tell stories, model diplomacy, and design solutions to problems that have no answer key — and I bring AI into each as a collaborator in genuine work. In one recent program, a team I trained used AI, working alongside a medical student, to better detect antibiotic resistance in wastewater; their project took first place in New York State. That is what deeper learning with AI looks like in practice: not a worksheet finished faster, but a real problem, real stakes, and real judgment about what the machine got right and where it had to be checked. I am committed to redesigning instruction around that kind of work — interdisciplinary, hands-on, grounded in design thinking — rather than bolting new tools onto old practice. And I teach AI literacy from its foundations, the data underneath the systems and the societal stakes on top of them, because a student who cannot interrogate an algorithm is not yet ready to use one. I am an enthusiast, not an evangelist: I care as much about the moments these tools do not belong in a classroom as about the moments they do.

All of this changes how I understand my own role. I am not in the room to deliver content; content is now abundant and nearly free. I am there to create the conditions under which students have to think — to defend a position to a skeptical peer, to notice when a confident-sounding claim, their own or a machine’s, does not survive scrutiny. The temptation I most want to resist is the quiet one: using these tools to make the twentieth-century classroom run more smoothly and calling that progress. Efficiency is not transformation. The worry that students are learning to offload their thinking is real — and ancient: Plato has Socrates fear that writing itself would hollow out memory and leave learners with the appearance of wisdom in place of the thing. He was partly right, and writing was still civilizational. The lesson I draw is not to ban the tools but to design learning in which the thinking cannot be skipped, because the student has to make something, argue something, decide something, and answer for it.

I hold one conviction without qualification: this education cannot become a privilege. The participatory work that builds these capacities — the debate team, the design lab, the research seminar — is exactly what underfunded schools cut first, often left to parents and bake sales while the budget flows to content delivery that tracks poorly to how the world actually works. Schools have always reflected the power structure around them, and an unequal transition into the age of intelligent machines will not stay politely outside the schoolhouse. Well-resourced students will get the tutors, the tools, and the teachers who help them learn to direct these systems; others will get the version that uses the same technology to cut costs and monitor compliance. When you give a young person the floor and the expectation that their argument matters, they rise to it, in every community, without exception. I treat that as the north star of my work — what I have come to call Universal Basic Debate: the participatory, judgment-building work of a citizen as a basic entitlement, not an enrichment activity for a lucky few.

I am not making this argument from the speculative edge of the technology. I do not need merged minds or the contested timelines for artificial general intelligence to make the case; the demonstrated floor is enough — machines that already hold a fluent conversation, write, code, design, and argue were impossible a few years ago and are not promises. The engine they run on is called deep learning. The deeper learning I want for students rhymes with it on purpose: the rise of the machine kind is exactly what makes the human kind the point. As the artificial sort grows cheaper and more capable, what becomes scarce is everything it cannot do — judgment, meaning, care, and the decision about what is worth learning at all — and that is what school must now exist to cultivate. There is no final form of education; there is only its next one.