An AI-based intervention for improving undergraduate STEM learning

Mohammad Rashedul Hasan and Bilal Khan

PLOS ONE, 2023, vol. 18, issue 7, 1-9

Abstract: 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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0288844

DOI: 10.1371/journal.pone.0288844

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