AI Studio
Fluency in the new era.
Fluency with artificial intelligence is the defining skill of this generation. We teach students not only to use it, but to build with it: bespoke websites, research tools, and automations, made with the very tools we use to run Classical.
The Premise
Beyond the chatbot.
Anyone can type into a chat box. We teach students to direct the machine.
Most people will spend the next decade asking AI for answers. The students who define it will be the ones who can turn an idea into a working website, a tedious task into an automation, a research question into a tool of their own. That is the literacy we teach, and the one we practice every day.
The Curriculum
From idea to working build.
A hands-on path from first principles to a project a student can ship.
- 01
Foundations
Direct the machine, not just chat with it: the explore, plan, and verify loop, and why every answer gets checked.
You'll buildA first project spec, and a running "where the model was wrong" log you keep the whole way through.
- 02
Build
From an idea to a working site, shipped live one verified step at a time, with version control as your safety net.
You'll buildA personal one-page website, live at a real URL, in a public GitHub repository.
- 03
Automate
How a chatbot becomes an agent, and how to capture one tedious task so you never do it by hand again.
You'll buildA study or research automation you actually use, from syllabus to study sheet.
- 04
Ship
One bespoke project, scoped small enough to finish and genuinely your own.
You'll buildA finished capstone, shipped live, with a README explaining how it works.
Why Classical
Taught by builders, not bystanders.
We do not teach AI from a textbook. We run on it.
Classical's own operation, this very website included, is built and run with Claude Code. We work with the same open models we teach students to understand, like Hermes. The program is led by Michael Labib, our Head of AI Literacy, who ships with these tools every day. Students learn from a practicing builder, not a course bought off the shelf.
Outcomes
From curious to capable.
Every student leaves with something real, and the confidence to build the next thing.
- A finished project they designed, built, and can explain decision by decision: shipped live at a real URL.
- A public GitHub repository with a clean history: something real to show, not just talk about.
- A working automation they actually use for their own schoolwork.
- A plain-language grasp of how these systems behave, and the judgment to know when not to trust them.
- A spec-and-brief discipline: the same muscle as a strong research question.
Genuine AI-building literacy and a portfolio centerpiece, not a computer-science credential. We teach what we practice.