This project analyses pre- and post-test data from students learning quantum computing through the spin-first approach. The aim is to evaluate question reliability, identify learning gains, and help develop a validated concept inventory tailored to this increasingly common teaching method.

This project develops a new concept inventory for astronomy and astrophysics. The key stages include expert interviews, question design and stastical validation with student cohorts.

A new quantum information concept inventory, QISCIT, has been validated by experts but never tested on students. This project administers it to learners in an online quantum computing course and performs the psychometric analysis needed to turn it into a genuine research-based assessment.

Since 2024, Large Language Models have become the standard tool used for automated marking of physics exams, especially for hand-written exams and questions which involve diagrams. Nobody has tested them on astronomy questions where students annotate a projection of the night sky. This project benchmarks LLMs against classical computer-vision methods on marking constellation and object identification tasks.

What is the environmental impact of using an AI chatbot? It is possible to model this from first principles by considering the GPU energy usage and the water required for cooling data centres. This project aims to estimate the joules of energy, litres of water and grams of carbon dioxide released from each LLM chatbot response.