Generalizability of AI-assisted simulation lab outcomes

Determine the extent to which the observed effects—namely improved immediate conceptual understanding and more favorable student attitudes—of using AI-engaged simulation development and prebuilt simulations in an introductory electric potential laboratory generalize to other physics topics, more diverse student populations, and different institutional contexts by conducting systematic, multi-site empirical studies across varied educational settings.

Background

The paper compared three instructional approaches in an introductory physics lab on electric potential: traditional physical equipment, a prebuilt simulation, and an AI-engaged activity where students prompted, refined, and validated an AI-generated simulation. Students in both simulation conditions outperformed those in the physical equipment condition on immediate conceptual assessments and reported more favorable attitudes.

The authors note that the investigation focused on a single concept (electric potential) with a relatively homogeneous population (primarily life sciences majors at one institution). They explicitly state that whether these findings extend to other physics topics, broader populations, or different institutional settings remains uncertain and requires systematic empirical examination.