AI Project Lab Level 2

Course Overview

Level 2 moves from single-screen builds to products that respond and remember. Students take one idea and grow it through successive versions, learning that complexity arrives as a product matures rather than by swapping to a harder task.

We don’t teach students to let AI think for them. We teach them how to think with AI.

Why Learn AI at All?

Until recently, the hardest part of learning to code was one sentence:

I don’t know how to write the code.

That barrier is gone. AI now writes functions, fixes errors, suggests architecture, builds interfaces, sets up databases and explains its own mistakes.

Which leaves two questions that actually decide whether a student gets anywhere:

• Does your child know what they are trying to build?
• Can they tell whether the answer they just got is any good?

Those two abilities get more valuable as the tools improve, not less.

Why Learn It Here?

General AI tools are built for adults getting work done. Their job is to finish the task as fast as possible — which, for a student still forming judgment, is exactly the problem.

Handed one with no structure around it, the natural way to use it becomes:

• “Do it for me.”
• “Give me the answer.”
• “Just fix it.”
• “Build the whole thing.”

And it will. What should have been:

Think → Try → Get it wrong → Evaluate → Revise → Understand

quietly collapses into:

Ask → Receive → Submit

The problem was never that students use AI. It is that the stronger AI gets, the more a student needs to know when not to hand over their thinking.

So the environment is built to keep them in it:

• Think with AI — Not just prompting. Students learn to question, evaluate and challenge what AI gives them.
• Build Real Projects — Ideas become prototypes, applications and increasingly complex products.
• Guided, Not Automated — Our learning environment is designed to keep students thinking instead of asking AI to do the work.
• Learn the Technology Behind It — Real programming and software knowledge builds as projects get more advanced.

We are not training students who nod along with whatever AI produces. Push back is something we actively grade — noticing an answer is wrong, saying so, and deciding what to do instead.

How Students Build

Every project runs the same loop, at whatever complexity the level calls for:

Idea → Visualize → Build → Test → Question → Improve → Build Again

Three Stages, Every Term

Each level works through all three, scaled to its own complexity:

Rapid Cycles

Short think-build-test loops that build confidence and shorten the feedback loop.

Progressive Versions

One project grown through versions - interaction, then data, then more complex relationships. Students see how complexity arrives as a product matures.

A Longer Project

A multi-week build where feature planning, scope control, refactoring, bugs and version management become real.

What This Level Covers

Technical scope

• Dynamic interfaces
• Basic state
• Working with structured data
• Simple API use
• Multi-step user flows

Thinking focus

• User flow
• Scope control
• Taking critique and revising

What We Actually Measure

Progress is judged on how a student works, not only on what appears on screen:

• Question quality
• Problem decomposition
• AI judgment
• Push back - disagreeing with AI when it is wrong
• Iteration depth
• Debugging behaviour
• Independence
• Technical growth

What AI Labs Is Not

So there is no confusion about what students are doing:

• Not a ChatGPT tutorial
• Not a prompt-template course
• Not copy-and-paste coding
• Not one-click game generation
• Not a weekly project that looks impressive but has no depth
• Not a pure syntax class

Materials

• Laptop required - a tablet will not work
• The learning environment is provided as part of the program
• Advanced project work may involve professional AI development tools, arranged with families as needed

Prerequisites

• Grade: Grades 5-10 (ages 10-16)
• Experience: AI Project Lab Level 1