Understanding the Effects of Supplemental Mathematics Assignments and Artificial Intelligence Tools on the Learning Outcomes of Students in Introductory Physics Courses

This project aims to serve the national interest by improving students' mathematical skills and performance in introductory undergraduate physics courses. Students' mathematical skills play an important role in their learning in physics courses. However, students enter introductory courses with wide variation in their prior exposure to advanced math. Those differences in preparation lead to differences in performance, which can cause some students to decide to leave the physical sciences. In this project, the investigators will enhance introductory physics courses with some features that will support students in learning some of the critical math concepts. Namely, they will incorporate supplemental math assignments and carefully constructed hints created by generative artificial intelligence (AI). In their education research, they will explore how to motivate the students who could benefit most from the supports to make use of them, and they will study the effect of the supports on students' learning and performance in the courses. This work is supported as a Level 1 project in the Engaged Student Learning track of NSF's Improving Undergraduate STEM Education (IUSE: EDU) program. The investigators aim to implement and study two optional supports, supplemental math assignments (which include practice problems on a set of math skills related to physics content) and AI-generated hints for homework questions, in two introductory physics course series, one for physical science majors and one for life science majors. The project team will use the framework of Situated Expectancy-Value Theory of Achievement Motivation (SEVT) to examine the factors that shape the use and impact of the optional supports. They will use a mixed methods approach of both qualitative and quantitative analysis to understand the perceived value and costs of the optional supports as defined by SEVT and to inform structural modifications to increase students' use of the supports. The education research will address questions such as the following: In introductory physics courses, what are the impacts on student exam performance associated with incentivized optional supports of supplemental math assignments and AI-generated hints? In introductory physics courses, what factors influence students' decisions to use optional supports? 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.