Electrical & Computer Engineering, Department of


Date of this Version



Hasan MR, Khan B (2023) An AI-based intervention for improving undergraduate STEM learning. PLoS ONE 18(7): e0288844. https://doi. org/10.1371/journal.pone.0288844


Open access.


We present results from a small-scale randomized controlled trial that evaluates the impact of just-in-time interventions on the academic outcomes of N = 65 undergraduate students in a STEM course. Intervention messaging content was based on machine learning forecasting models of data collected from 537 students in the same course over the preceding 3 years. Trial results show that the intervention produced a statistically significant increase in the proportion of students that achieved a passing grade. The outcomes point to the potential and promise of just-in-time interventions for STEM learning and the need for larger fully-powered randomized controlled trials.