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Artificial Intelligence (AI) has the potential to fundamentally change theeducational landscape. So far, much of the physics education research relatingto AI has focused on lecture-based assessment and the ability of ChatGPT toanswer conceptual surveys and traditional exam-style questions. In this study,we shift the focus by investigating ChatGPT's ability to complete anintroductory mechanics laboratory activity by using Code Interpreter, a recentplugin that allows users to generate and analyse data by writing and runningPython code `behind the scenes'. By uploading a common `spring constant' labactivity using Code Interpreter, we investigate the ability of ChatGPT tointerpret the activity, generate realistic model data, produce a line-fit, andcalculate the reduced chi square statistic. By analysing our interactions withChatGPT, along with the Python code generated by Code Interpreter, we assesshow the quality and accuracy of ChatGPT's responses depends on different levelsof prompt detail. We find that although ChatGPT is capable of completing thelab activity and generating plausible-looking data, the quality of the outputis highly dependent on the detail and specificity of the text prompts provided.We find that the data generation process adopted by ChatGPT in this study leadsto heteroscedasticity in the simulated data, which may be difficult for novicelearners to spot. We also find that when real experimental data is uploaded viaCode Interpreter, ChatGPT is capable of correctly plotting and fitting thedata, calculating the spring constant and associated uncertainty, andcalculating the reduced chi square statistic. This work offers new insightsinto the capabilities of Code Interpreter within a laboratory setting andhighlights a variety of text-prompt strategies for the effective use of CodeInterpreter in a lab context.

Data Dialogue with ChatGPT: Using Code Interpreter to Simulate and Analyse Experimental Data