Generative Artificial Intelligence as Creative Practice in Computational Physics Education

This project aims to serve the national interest by refining physics course materials and activities in response to the growth of generative artificial intelligence (gen-AI). This project intends to target the development of creativity among computational physics students at Oregon State University. By updating activities to engage students with gen-AI and carrying out interviews and observations with students in this environment, the project team seeks to generate knowledge about how students leverage gen-AI tools to develop their creativity and build proficiency as the computational scientists of tomorrow.

These findings will be used to create guidelines for curriculum development with the goal of leveraging modern tools for doing science that utilize technological advancements in gen-AI. The project will also iteratively develop a suite of classroom activities to support students in using gen-AI to solve computational physics problems.

This project seeks to characterize how students use gen-AI in a sequence of computational physics courses, with the overarching goal of developing and disseminating guidelines for updating course content to engage students with gen-AI in support of their creativity and learning. The Oregon State University computational physics curriculum provides a research environment where students focus primarily on computing in a disciplinary setting.

The project team seeks to utilize this context to produce a characterization of how students use gen-AI when learning advanced STEM content, which will in turn inform curriculum development. Qualitative research methods such as semi-structured interviews, observational field notes, and analysis of classroom artifacts will be utilized for this investigation.

By employing an alternating research design between characterizing student usage and updating the curriculum, the project team also aims to produce and refine a set of guidelines for keeping a computational physics curriculum attuned to the ways students are engaging with gen-AI and the year-to-year changes in gen-AI technological capabilities. Though the scope of these guidelines will apply mainly to computational physics curricula, the team will seek further input and generalization during the dissemination of these guidelines by directly engaging with researcher and practitioner communities who can benefit from and build on this work.

The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.