Doctoral research · In progress

What if students learned to work with uncertainty, instead of classrooms trying to get rid of it?

My PhD explores Quantum-Inspired Pedagogy (QIPA) as a possible approach to K–12 STEM, AI and robotics education. Put simply: how can we help learners keep more than one possibility in mind, see how systems connect, and think clearly when they don’t have all the information?

This research is still in progress. QIPA is a proposed approach, so the links and outcomes described here haven’t been proven yet.

The problem I’m looking at

Education policy keeps asking for inquiry, cross-subject learning and better preparation for a tech-filled world. Yet many students meet coding, robotics and AI as a recipe: build the model, run the code, get the output. Done. Next.

The interesting questions often get skipped. What assumptions shaped this result? How does the data change the model? What happens to the whole system if one part changes? How do you reason when there isn’t one right answer?

My hunch is that how we organise learning matters as much as which technology we bring in. The study asks whether quantum-inspired teaching metaphors can help build lessons around those questions.

So what does “quantum-inspired” mean here?

Relax, nobody is teaching quantum physics to ten-year-olds. The research borrows a few ideas from quantum thinking and uses them as teaching metaphors, to try out fresh ways of approaching learning.

Superposition

Keeping more than one possible explanation or approach open before deciding.

Entanglement

Noticing relationships and dependencies between ideas, subjects or parts of a system.

Uncertainty

Treating incomplete information and unpredictable results as part of the reasoning process, rather than automatically as failure.

These are metaphors, not physics lessons. Whether they actually help in the classroom is exactly what the research needs to find out, not assume.

Questions guiding the study

  1. How can quantum-inspired pedagogical approaches be conceptualised and applied to learning in STEM, AI and robotics in South Indian K–12 schools?
  2. How does applying these approaches relate to students’ cognitive flexibility, systems thinking, future-ready problem-solving, and STEM and AI self-efficacy?

Why this inquiry matters

AI and robotics education is about more than operating tools. Learners need to read data, understand systems, check outputs and make decisions without all the facts. This research tests one possible teaching approach for those skills, with a close focus on K–12 schools in South India.

Current progress

Literature reviewAccepted
MethodologyIn progress
Thesis submissionTargeted for 2027

Last updated October 2026.

From research to classroom

The proposed Q-STEM Framework is where this research meets the classroom. It turns the research questions into a draft lesson structure: start with an open question, try different approaches, connect the ideas, test them, and explain your decisions. It’s still under investigation.

Explore the Q-STEM Framework →

Questions, critique and collaboration are all welcome here.

If you’re a researcher, teacher or school interested in inquiry-led AI and STEM education, I’d love to hear from you.

Get in touch