AI LEARNING PATHWAY · LEVEL 4 OF 4
AI Innovator
Ages 16 to 18 — Building modern AI systems, with a portfolio project to show for it.
10
One-hour classes
10 weeks
One class per week
5
One-to-one option
Advanced
Coding level
Course goal
This level prepares students for university study, hackathons and independent work. Students build with the modern AI stack — machine learning, computer vision, LLM applications, retrieval and agents — and finish with a project fit for a portfolio or college application.
Who this level is for
Required
- Comfortable using computers
- Strong logical and mathematical thinking
Helpful, but not required
- • Python fundamentals: functions, lists and dictionaries, loops and conditions
- • Basic knowledge of statistics
- • Students without Python experience should complete a prerequisite Python 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 | The Modern AI Landscape | Machine learning, deep learning, generative AI, LLMs, computer vision and agents. | Where is AI going? |
| 2 | Python for AI Development | Practical libraries and workflows: NumPy, Pandas and data handling. | Environment setup |
| 3 | Maths and Statistics for AI | Probability, distributions and correlation, applied rather than theoretical. | Why the maths matters |
| 4 | Machine Learning Fundamentals | Classification, regression, overfitting and underfitting, evaluation metrics. | Train versus test data |
| 5 | Build and Evaluate ML Models | Train/test split, accuracy, precision, recall, and improving a weak model. | Full model workflow |
| 6 | Deep Learning and Neural Networks | Layers, activation functions, computer vision and image classification. | Hands-on build |
| 7 | Generative AI and LLM Apps | Tokens, context windows, prompting, hallucinations, RAG and AI safety. | Applied LLM work |
| 8 | AI Agents and Modern Systems | Workflows, tools, memory, retrieval and APIs in real AI products. | Brief issued at the end of class |
| 9 | Build a Portfolio AI Project | RAG chatbot, prediction system, computer vision app or agent with tools. | Guided project work |
| 10 | Final Presentation | Students present the system, architecture and demo, 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 brief and builds an AI solution to a meaningful real-world problem.
What students prepare
- • Research into the problem and its target users
- • A working prototype with a live demo
What they present
- • The AI technology used and the system architecture
- • Limitations and planned future improvements
Format: a five to ten minute presentation followed by questions from parents and the instructor.
How work is assessed
25%
Technical depth
20%
Innovation
20%
Effective use of AI
20%
Problem solving and impact
15%
Presentation and demo
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
AI Roadmap and Assessment
Python and AI knowledge assessed, goals mapped to a plan.
Class 2
Choose a Track
Machine learning, generative AI or computer vision.
Class 3
Advanced Practical Concepts
Model evaluation, APIs, LLMs, RAG or computer vision.
Class 4
Project Architecture
Problem, data, model, backend, interface, testing.
Class 5
Final Project and Presentation
Finish the prototype, then present and defend it.
Project direction. Built to be shown: school portfolio, college application, GitHub or hackathon entry.
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 can build, explain and present an AI project using modern AI technologies.”
Takeaway. A portfolio-ready AI project, a technical presentation, and a real foundation for university study.
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 Innovator?
Book a free consultation and we'll confirm this is the right level for your child.