Leveraging generative artificial intelligence for simulation-based physics experiments: A new approach to virtual learning about the real world

This study investigates the impact of a novel application of generative artificial intelligence (AI) in physics instruction: engaging students in prompting, refining, and validating AI-constructed simulations of physical phenomena. In a second-semester physics course for life science majors, we conducted a comparative study of three instructional approaches in a laboratory focused on electric potentials: (i) students using physical equipment, (ii) students using a prebuilt simulator, and (iii) students using AI to generate a simulation. Among the groups, we found significant differences in performance on conceptual assessments of the laboratory content ( <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mrow> <a:msup> <a:mi>η</a:mi> <a:mn>2</a:mn> </a:msup> <a:mo>=</a:mo> <a:mn>0.3</a:mn> <a:mn>5</a:mn> <a:mn>9</a:mn> </a:mrow> </a:math> ). analysis showed that students in both the AI-generated and prebuilt simulation conditions scored significantly higher on the conceptual assessments than students in the physical equipment condition. Students in these groups also reported more favorable perceptions of the learning experience. Finally, this preliminary study highlights opportunities for developing students’ modeling skills through the processes of designing, refining, and validating AI-generated simulations.