Insights
Long-form strategic writing for educational leaders. Each collection distills patterns we have seen work across schools, universities, and training organizations — with the frameworks, numbers, and judgment calls that turn principles into pipelines. Articles are organized into thematic collections so each one stands as a complete operating model.
Our collections
The Institutional Playbook
Strategy, AI, and the marketing of educational institutions.
A three-part operating model — social media strategy, AI execution, and the institutional brief that makes both compound — plus three standalone follow-ups: an anonymized K-12 case study, a higher-education deep dive, and the marketing–admissions alignment problem most institutions leak through.
Explore the collection →The Patronage Playbook
Fundraising, donor stewardship, and the discipline of asking well.
A three-part operating model — the case for fundraising, the strategy that compounds, and the case-for-support document — plus five follow-ups: a catalog of mechanisms, a catalog of targets, the discipline of donor matching and due diligence, AI for fundraising, and a quarter-by-quarter first-year implementation guide.
Explore the collection →Methodology articles
Stand-alone articles on the Smoother Learning Methodology — its formats, its design constraints, and its place in the wider Smoother Experiences platform.
Education in the AI era
Personal essays by Carlos Miranda Levy on how teaching, learning, and institutions should change as AI reshapes what is worth knowing — and how to change them well. A six-part series drawn from my 2025 keynote, plus a standalone essay. (Working drafts; sourced revisions forthcoming.)
The Recurring Panic: Every New Tool Was Going to Destroy Learning
The alarm is 2,400 years old and always wrong — from Socrates on writing to calculators, PCs, and the Internet.
Every capacity-amplifying technology arrived to the same cry: it will destroy learning, students will stop thinking. It never happened. The tools transformed education; those who resisted fell behind. AI is the same pattern.
Read Part 1 →The Crisis Was Never AI: Two Well-Intentioned Mistakes That Broke Learning
Disengagement predates AI. We fragmented knowledge into measurable competencies and replaced practice with one-off projects.
The crisis in education has deeper roots than any technology: optimizing for measurement over deep learning, and beautiful creative experiences done once, without the repetition that builds mastery. The result is the illusion of learning — and our design, not AI, manufactures it.
Read Part 2 →No Shortcuts to Mastery: The Beatles, Picasso, Larry Bird & 100 Exercises
Excellence requires extensive, deliberate practice. Quantity is the precondition for quality — not its enemy.
When my son wants to stop after twenty minutes because the AI told him he is ready, I ask for the full hundred. Mastery comes from repetition, not from single memorable experiences — as the Beatles, Picasso, and Larry Bird each show.
Read Part 3 →What AI Finally Makes Possible: Deliberate Practice at Scale
Extensive, personalized, instantly-fed-back practice — for every student, not just those with private tutors.
Extensive practice was always necessary and always hard to scale. For the first time we can deliver deliberate practice — varied, adaptive, personalized, with immediate feedback — to a whole class at once. That is the real revolution, and it belongs to everyone.
Read Part 4 →Intelligence Is a Muscle: The Electric Bicycle and Two Futures
Will AI make students think less or more? It depends entirely on how we design its use — and that is our job, not theirs.
An electric bicycle can carry a lazy rider effortlessly or let an athlete train harder and go farther. AI is the same tool with radically different results. The responsibility for which future we build belongs to educators and institutions, not to a ten-year-old choosing the easy path.
Read Part 5 →Amplification, Not Substitution: Six Practical Ways AI Transforms Teaching
From lesson planning to assessment — six concrete uses that amplify the teacher instead of replacing them.
Practically, how do we do this well? Six areas — planning, personalization by profile, the real-time classroom assistant, resources, adaptive activities, and continuous assessment — where AI never replaces professional judgment or the human relationship, but amplifies preparation, personalization, and practice. The choice, as always, is resist or evolve.
Read Part 6 →The Maps We Copied — and What They Teach Us About AI in Education
We all copied the rivers of Africa out of a book, and no teacher called it plagiarism — because the map was never the point.
Carlos Miranda Levy's parallelism, taken through the evidence: what a geography assignment from decades ago tells us about designing AI assignments that actually make learners learn — copying versus generating, the 1995 deadline schooling ignored, the guardrails experiment, and how to grade the journey instead of the map.
Read the essay →The Knowledge: What’s Still Worth Learning When the Machine Already Knows the Way
London’s cab drivers memorize their whole city. GPS made that look obsolete — until you learn what the effort was quietly building.
Since 1865, no black-cab driver has worked London without passing “the Knowledge.” The neuroscience of that feat — and the cognitive science of knowledge, skills, and transfer — turns into a sourced argument for how to rebalance a curriculum in the age of AI: keep the effort that builds a mind, hand the machine what only ever produced an output, and never ban the tool when the fix is to design how it’s used.
Read the essay →Should We Still Teach Coding? A Principal’s Question in the Age of Vibe Coding
Yes — but teach the foundation, not the syntax. What to keep, what to change, and the trap of letting two vibes replace learning.
A friend who runs an innovative multilingual school asked whether her school should still teach coding now that AI can write it. The relevance of coding today is like the relevance of mathematics: not the syntax, but the structured thinking underneath. What endures — specifying, imagining, structuring — is exactly what you cannot delegate to a machine.
Read the essay →The Infographic I Didn’t Publish: AI Drift and the Discipline of Checking Everything
A good tool produced something polished and wrong. How we caught it — and why every AI output needs a compliance checklist before it carries your name.
Preparing the coding essay, I asked ChatGPT, Claude, and Gemini to verify my claims and find real sources — and they did. Then Gemini offered an infographic. It looked professional. It was not mine, and it was not all true. A companion piece on drift, guardrails, and the self-correcting checklists we run on everything we publish.
Read the essay →Comprehensive AI learning designed for educators, by educators. From awareness to mastery.