Incentivizing supplemental math assignments and using AI-generated hints is associated with improved exam performance

TL;DR

This study shows that incentivized math assignments and AI-generated hints improve exam scores and reduce disparities in undergraduate physics courses, promoting greater equity in student performance.

Contribution

The paper introduces a combined intervention of scaled extra credit and AI hints, grounded in motivation theory, to enhance equity and performance in physics education.

Findings

Both interventions increased exam scores.

Scaled extra credit reduced disparities in assignment completion.

AI hints lowered time and social barriers for students.

Abstract

Inequities in student access to trigonometry and calculus are often associated with racial and socioeconomic privilege, and often influence introductory physics course performance. To mitigate these disparities in student preparedness, we developed a two-pronged intervention consisting of (1) incentivized supplemental math assignments and (2) AI-generated learning support tools in the form of optional hints embedded in the physics homework assignments. Both interventions are grounded in the Situated Expectancy-Value Theory of Achievement Motivation, which posits that students are more likely to complete a task that they expect to do well in and whose outcomes they think are valuable. For the supplemental math assignments, the extra credit was scaled to make it worth more points for students with lower exam scores, thereby creating even greater value for students who might benefit most from the assignments. AI-generated hints were integrated into the homework assignments, thereby reducing or eliminating the cost to the student, in terms of time, energy, and social barriers or fear of judgment. Our findings indicate that both these interventions are associated with increased exam scores; in particular, the scaled extra credit reduced disparities in completion of supplemental math assignments. These interventions, which are relatively simple for any instructor to implement, are therefore very promising for creating more equitable undergraduate quantitative courses.