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.
A class can reach the right answer without anyone knowing how they got there. Students follow the steps perfectly and still can’t tell you why they work, or what to do when they don’t.
In AI education, that gap is a big deal. AI tools give smooth, confident answers that can be incomplete or simply wrong. So learners need practice asking: what’s the evidence? What’s being assumed? What would change the answer? Q-STEM puts that question right at the centre of the lesson.
The framework borrows three ideas from QIPA as teaching metaphors: superposition, entanglement and uncertainty. In plain words: keep more than one possibility open, notice how things connect, and reason well when you can’t be sure of the outcome. Don’t worry, nobody is being taught quantum physics.
The research is looking at possible links with flexible thinking, systems thinking, problem-solving, and students’ confidence in STEM and AI. These links are ideas being tested, not proven results.
Learners identify what they know, what they do not know and what they need to find out. The teacher frames the problem without immediately giving the method.
Learners suggest more than one explanation or approach before deciding which is strongest. The teacher asks, “What else might be possible?”
Learners examine how ideas, subjects, data or system components affect one another—and what may change when one part changes.
Learners use code, a robot, data or an AI tool to test ideas. Unexpected results and errors become material for analysis rather than something to hide.
Learners justify a decision, consider alternatives and formulate a sharper question that can lead into the next investigation.
The sharper question at Stage 5 can open the next cycle.
Example lesson · Grades 6–8
This is a sample lesson design, not a report of measured results.
| Explore | Teams propose different ways to judge usefulness, such as accuracy, the cost of an error, speed and who might be affected. |
|---|---|
| Connect | Learners map how data, model design and the context of use influence one another. |
| Test | Teams evaluate the AI tool on a set of examples, record where it fails and discuss which errors matter most. |
| Explain and reframe | Teams recommend whether and how the tool should be used, give reasons and pose a follow-up question such as, “Who should check an AI’s work?” |
It’s a suggested structure for inquiry-led lessons. It is not a magic formula, a replacement for a good teacher, a checklist for learning a tool, or a sneaky quantum physics course.
Teachers bringing AI, coding and robotics into class; curriculum designers building lessons that link up properly; and school leaders who want technology lessons to build real understanding, not just button skills.
In development
A practical guide to planning question-led STEM and AI lessons. Pop your email in and I’ll let you know when it’s ready.
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Coming next
An AI Lesson Planner and a Student Toolkit are in the works, to take the framework from lesson plans into the classroom and into students’ own reflections. More details soon.
Sample unit
Opening question: When should a decision follow the data?
Over five lessons, learners find out what makes data useful, how the same numbers can tell different stories, how data and model choices affect each other, how a simple classifier can get things wrong, and how to make a sensible recommendation. They finish with a one-page recommendation and a brand-new question to chase.
A suggested five-stage lesson cycle that starts with an open question. It’s linked to my ongoing PhD research on Quantum-Inspired Pedagogy.
It’s being designed for K–12 classrooms. Guidance for different grade bands is on its way.
Not yet, and I won’t pretend otherwise. It’s a proposed framework that’s still being researched. I’ll share findings as they come in.
Nope. QIPA borrows a few quantum ideas as teaching metaphors. Q-STEM itself is all about STEM, AI and robotics learning.
Let’s talk about the questions, ideas and learners behind it.
