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An AI-based intervention for improving undergraduate STEM learning

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  • Mohammad Rashedul Hasan
  • Bilal Khan

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.

Suggested Citation

  • Mohammad Rashedul Hasan & Bilal Khan, 2023. "An AI-based intervention for improving undergraduate STEM learning," PLOS ONE, Public Library of Science, vol. 18(7), pages 1-9, July.
    • Handle: RePEc:plo:pone00:0288844
    DOI: 10.1371/journal.pone.0288844
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