Lucas Academy · Research & Notes
Research and notes
Essays, field notes, and straight answers — the longer writing behind how we teach and the projects it came out of. What we believe about education lives on the About page; this is where it gets worked out at length.
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Our educational philosophy
Written for parents, educators, and community partners — in English and Chinese.
The Foundations of Whole-Person Education
Five verses, five questions — authority, aim, teaching and learning, method, and practice. A condensed version of the essay, in English and Chinese.
Read the essay → Essay · Teaching practiceDifferentiated Instruction: Helping Every Kid Get Back on Track
Kids really are different. More support for those who need it most, kids teaching and learning from each other, relationships first, and a habit of asking for help. In English and Chinese.
Read the essay →Essays
On learning, making, and AI
Five essays about learning, making, and AI. Most name a framework and ground it in something built at Lucas Academy, and each ends with something you can try. Four are in English; What Is Intelligence in the Age of AI? is in English and Chinese.
Learning in the age of AI is not just about skills
When a machine can produce the answer, the learning shows up somewhere else: in noticing, doubting, choosing, and redirecting. A map of what education is really for now.
Read the essay → Essay · FoundationsWhat Is Intelligence in the Age of AI?
Machines pass almost every test we built to measure intelligence, yet no one agrees they are intelligent in the way we mean. Three words, a question psychology leaves open, and what learning is for. In English and Chinese.
Read the essay → Essay · FrameworkThinking artifacts: what student work should reveal
In the AI age, a finished product is cheap. The evidence of learning is the artifact plus the visible trace of judgement behind it. Almost every project here produces one.
Read the essay → Essay · FrameworkComputational studio art: code as a material
Not "AI, make me a picture." Algorithms, computer vision, and simulation become studio materials a child questions, shapes, and transforms — all in the browser.
Read the essay → Essay · FoundationsGames as thinking laboratories
One arcade, every subject: how code connects geometry and physics (Breaker), game theory and social experiments (Monkey Republic), data science, art, and music.
Read the essay →From the workbench
Studio notes
Shorter field notes from the prototypes — what we tried, what surprised us, and what is still an open question. Observations from our own rooms, not tested results. These pages are in English.
Computer vision as a paintbrush
We let kids decide what the machine sees. The seeing turned out to be the art supply — and "what does it see?" became the question in the room.
Read the note → Studio note · Art + codeThe function brush: code as a studio gesture
A brush whose behavior is a small program the child edits. What we learned: kids don't want magic — they want dials.
Read the note → Studio note · SimulationWhy Monkey Republic needed adjustable worlds
A fixed-world trust game teaches one lesson. We made the world a dial — and watched harsh worlds retrain the pragmatists.
Read the note → Deep dive · BreakerThe geometry of a bounce
A technical look at Breaker: the reflection law, the paddle's ±30° steering, unfolding mirrors to predict bounces, and the real numbers a bot runs on.
Read the deep dive →Straight answers
Resources for parents & educators
The questions we hear most, answered directly — no jargon, no sales pitch, each with something you can try at home. These pages are in English.
Should kids still learn to code if AI writes code?
Yes — but the goal shifts from producing code to reading, judging, and directing it. Why, and what to look for.
Read the answer → Resource · Q&AWhat is computational thinking for kids?
Four moves — break it down, spot the pattern, ignore the noise, write clear steps — with or without a screen.
Read the answer → Resource · Q&AHow to talk with kids about AI & creativity
Focus on choice, judgement, and meaning — and the questions to ask instead of "did the AI make it?"
Read the answer → Reference · GlossaryGlossary
Plain-language definitions of every idea we teach — thinking artifacts, reward hacking, reaction-diffusion, Nash equilibrium — plus our own frameworks.
Open the glossary →Still being worked out
This library grows slowly and deliberately — fewer, better pages. If something here is unclear, wrong, or worth arguing with, we would genuinely like to hear it.