Curriculum · Learning design · Q-STEM
A student can use an AI tool without knowing its limits, copy code without seeing the logic, or build a robot without understanding the system inside it. I design lessons that pull those hidden ideas into the light.
My curriculum work covers AI literacy, computational thinking, Python, robotics, maths connections and teacher training. The proposed Q-STEM Framework is one way I’m testing how open questions can shape the whole learning journey.
AI is already part of children’s lives, but what happens behind the screen is a mystery. This course opens the black box. Learners find where AI shows up, how it learns from data, why it gets things wrong, and how simple models are sorted, trained and tested, with plenty of hands-on and unplugged activities. They finish by explaining an AI idea in their own words.
Audience: Students · Grades: 3–8
Using an AI tool is not the same as understanding it. Learners practise writing clear prompts, comparing answers, fact-checking against reliable sources, spotting bias and made-up information, and deciding when AI is helpful and when it really isn’t. The goal: thoughtful users, not copy-paste champions.
Audience: Students · Grades: 3–8
AI isn’t magic. It runs on patterns, data and maths that students may already know from school. This course links AI with patterns, averages, probability, graphs and optimisation. Learners work out how a model makes a prediction, what “confidence” really means and how mistakes happen. Thinking first, formulas later.
Audience: Students
Good learning should build year on year. These programmes link AI literacy, computational thinking, Python and robotics across grades, with teacher guidance, assessment ideas and project briefs that fit each school’s curriculum.
What should students know before trusting an AI answer? This programme explains how AI works at the right level for their age, where it helps, where it fails, and how bias, privacy and responsibility come into play. Prompting practice comes with plenty of discussion and healthy questioning.
Code is more than syntax to memorise. These step-by-step units take learners from block coding to Python, using code to express ideas, solve problems, fix bugs and explain the logic behind their solution.
Robotics gets exciting when the build solves a real problem. Project modules use tools like Arduino, micro:bit, drones and 3D design to explore sensors, control, design choices and computational thinking. Learners build, test, improve and explain. Not just assemble and hope.
A good AI Olympiad should test more than what students can remember about AI. It should get them reasoning, spotting patterns, reading data, questioning AI answers and tackling problems they’ve never seen before.
I’ve contributed to AI challenge and competition design in my professional roles. I don’t share employer-owned question banks, scoring rubrics, documents or branding here. If I publish sample assessments, they’ll be made from scratch and clearly labelled.
Focus areas: AI concepts · Logical reasoning · Computational thinking · Problem-solving · Applied AI literacy
What happens when students stop just using AI and start putting their ideas to the test?
In my professional roles, I’ve helped plan school AI challenges focused on prompting, reasoning, creativity and responsible AI use. Event names, materials and results belong to those organisations, so they aren’t shown here as my own portfolio work.
For my own portfolio, I’m designing original challenge formats that test how well students frame questions, check AI outputs and explain their decisions. I’ll publish them once the samples are made and reviewed.
Focus areas: Prompt engineering · AI literacy · Critical thinking · Creative problem-solving · Responsible AI use
The method · Proposed framework under investigation
Q-STEM is a question-led way of teaching that I’m developing alongside my PhD on Quantum-Inspired Pedagogy (QIPA). Each lesson starts with a question that doesn’t have one obvious answer. Then a five-stage cycle helps learners explore options, spot connections, test ideas and explain their choices.
You’ll find some of my independent curriculum projects in my portfolio. They’re brand-new demonstrations of how I design, not copies of school or employer material. Each one links learning goals to activities, assessment criteria and a clear plan for testing whether it works.
Teachers are often asked to teach new technology before they’ve had a chance to play with it themselves. My training sessions mix hands-on practice, question-led lesson planning and ways to assess students’ reasoning. Teachers leave with real confidence and resources they can adapt for their own class.
A practical Q-STEM Teacher Guide is on its way, and my portfolio includes a workshop I designed on checking AI-generated teaching materials.
I start with what learners need to understand, then work backwards to the questions, ideas, tools and teacher support that will get them there. Technology is part of the learning, not the whole show.
Some of my curriculum work belongs to employers and clients, so I describe the approach and experience here. Happy to discuss suitable samples on request.
Let’s talk about the questions, ideas and learners behind it.
