Date
Publisher
arXiv
This paper examines how advanced AI assistants can help physics educators
create practical teaching tools without specialized programming skills. Using
the square-wave synthesis experiment as a case, we target common obstacles in
laboratory instruction-complex setup, unstable signals, and limited class
time-and show how AI-assisted development can shift attention from wiring and
calibration to core physical ideas.
To address this need, we guided an AI assistant through iterative prompts to
generate a clean, runnable program that visualizes square-wave synthesis from
its component sine waves. The tool supports step-by-step construction of the
waveform, adjustable parameters (amplitude, frequency, and phase), and
immediate comparison with an ideal reference using simple error measures. We
packaged the result as a standalone application so it runs reliably on standard
classroom computers, enabling pre-lab demonstrations and interactive
exploration that reduce procedural friction and highlight underlying concepts.
Building on this proof of concept, we argue the same workflow extends to
other topics-such as simple harmonic motion and optical interference-where
adjustable parameters and real-time visualization deepen understanding. We
conclude that AI-assisted co-design can improve teaching efficiency, enhance
student engagement with foundational principles, and provide a scalable path
for developing customizable physics education tools.
What is the application?
Who is the user?
Who age?
Why use AI?
