DeepSeek AI: The tool I wished I had while building my photoelectric effect simulation
When I developed my photoelectric effect simulation, I spent three months working hard to develop an interactive experience that accurately represents this fundamental quantum phenomenon. The biggest challenge was modeling the relation between the photo-current and the cathode potential factoring the saturation current phenomenon into consideration. This was a complexity that goes beyond the standard equations taught in high school or undergraduate physics courses.
During development, I used ChatGPT to assist with the technical research. When I asked it for the equations that model current-voltage relationships in the photoelectric effect, ChatGPT could only provide basic undergraduate-level equations, not the complexity I needed. This led me to conduct research, analyzing experimental data to derive empirical formulas that could represent the phenomenon. I gathered data for different cathode substances and used graphing applications to extrapolate the curve of the current-voltage relation. Then I performed curve fitting to derive an empirical equation that governs this relation. I acknowledge that there must be a theoretical equation somewhere, but I was not able to reach it since I did not have the time to research scientific papers on the photoelectric effect.
Looking back, I wish I had access to tools like DeepSeek during my development process. In my recent experimenting with this open-source AI model, I’ve found it provides structured, research-paper-like responses with meaningful technical information from the first interaction. This would have significantly streamlined my research phase. Definitely, DeepSeek wouldn’t have appeared if ChatGPT hadn’t existed in the first place. However, the super capabilities that DeepSeek came with as it came into existence, plus being open-source, makes it super competitor to the existing AI tools.
The emergence of open-source AI projects like DeepSeek represent an exciting opportunity for educational technology development. As these tools continue to grow through contributions from diverse scientific communities, they could make the development of educational simulations richer, more efficient, and accessible. While I had to spend considerable time patching together empirical formulas from experimental data, future developers might be able to access this specialized knowledge more easily, allowing them to focus more on developing innovative, interactive learning experiences.
