Developing a ChatGPT-Based Tool for Physics Experiment Teaching
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915449587892224 |
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| author | Liu, Yifeng Li, Min Zhang, Zhaojun Fang, Youkang Qin, Meibao |
| author_facet | Liu, Yifeng Li, Min Zhang, Zhaojun Fang, Youkang Qin, Meibao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13011 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Developing a ChatGPT-Based Tool for Physics Experiment Teaching Liu, Yifeng Li, Min Zhang, Zhaojun Fang, Youkang Qin, Meibao Physics Education 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. |
| title | Developing a ChatGPT-Based Tool for Physics Experiment Teaching |
| topic | Physics Education |
| url | https://arxiv.org/abs/2508.13011 |