AI LEARNING PATHWAY · LEVEL 3 OF 4
AI Builder
Ages 14 to 16 — The shift from "How do I use AI?" to "How do I build something with AI?"
10
One-hour classes
10 weeks
One class per week
5
One-to-one option
Intermediate
Coding level
Course goal
This level teaches students to build real AI applications and basic machine learning systems in Python. They work with actual datasets, train and evaluate models, meet neural networks and large language models conceptually, and connect a model to a working application.
Who this level is for
Required
- Basic computer skills
- Logical problem-solving ability
Helpful, but not required
- • Python fundamentals: variables, lists, loops, conditions and functions
- • Students without Python should complete a short Python foundation module first.
Group course · 10 weekly classes
One hour a week for ten weeks — ten hours in total. Nine teaching classes, then a final presentation delivered to parents.
| # | Class | What is covered | In class |
|---|---|---|---|
| 1 | Understanding the AI Landscape | AI, machine learning, deep learning and generative AI, and how modern systems fit together. | Real-world case studies |
| 2 | Python for AI | Functions, lists and dictionaries, loops, conditional logic and libraries. | Revision and extension |
| 3 | Working With Data | Datasets, CSV files, cleaning data, features and labels. | Introduction to Pandas |
| 4 | Machine Learning Fundamentals | Supervised and unsupervised learning, classification and regression. | Choosing the right approach |
| 5 | Build an ML Model | Prepare data, train a model, test predictions, understand accuracy. | Python and beginner libraries |
| 6 | Neural Networks and Deep Learning | Neurons, layers, inputs and outputs, and how training works. | Conceptual, not mathematical |
| 7 | Generative AI and LLMs | Large language models, tokens, context, prompts, and why AI hallucinates. | Strengths and limitations |
| 8 | AI Application Development | Connecting a model to an application: APIs, inputs, outputs, application flow. | Brief issued at the end of class |
| 9 | Build an AI Project | Chatbot, sentiment analyzer, recommender or research assistant. | Guided project work |
| 10 | Final Presentation | Students present their build and explain the architecture, then take questions. | Parents attend |
The project brief is issued at the end of Class 8. Students research and build between sessions, with guided support in Class 9, and present in Class 10.
Class 10 · Final presentation
The closing class is held with parents and the instructor. Each student presents the research they have done and the thing they have built, then answers questions.
The brief
Each student receives a real-world problem and produces a working AI solution to it.
What students prepare
- • Research into the problem and who it affects
- • A working solution: problem, input, model, application, output
What they present
- • The technical architecture and the model chosen
- • A clear statement of the limitations
Format: a five to ten minute presentation followed by questions from parents and the instructor.
How work is assessed
25%
Technical implementation
20%
AI understanding
20%
Problem solving
20%
Innovation
15%
Presentation
One-to-one course · 5 classes
Five one-hour classes, personalized to the student's age, interests and existing knowledge. Faster progression, and a capstone project presented to parents at the end.
Class 1
Python and AI Foundations
Assessment, then core Python for AI work.
Class 2
Data and Machine Learning
Real datasets, features, labels and model choice.
Class 3
Train and Evaluate a Model
Building a model and judging whether it is any good.
Class 4
Generative AI and APIs
LLMs and connecting them to an application.
Class 5
Final Project and Presentation
Complete the build, then present it to parents.
Project direction. Projects can go significantly further than the group course depending on the student.
Tools and environment
Inside a class
- 5 minRecap — Quick challenge on last week's ideas
- 15 minNew concept — The idea of the week, taught simply
- 20 minHands-on activity — Students try it themselves
- 15 minBuild or experiment — Applying it to their own work
- 5 minReflection — Wrap-up and a task for the week
What your child leaves with
“I understand machine learning and can build a basic AI-powered project.”
Takeaway. A trained and evaluated ML model, a working AI application, and the vocabulary to explain both.
AI can help you think, but it should not think instead of you. Students are taught to question outputs and understand limits, not to depend on the tools.
Ready to start AI Builder?
Book a free consultation and we'll confirm this is the right level for your child.