Using fair AI to predict students' math learning outcomes in an online platform

As instruction shifts away from traditional approaches, online learning has grown in popularity in K-12 and higher education. Artificial intelligence (AI) and learning analytics methods such as machine learning have been used by educational scholars to support online learners on a large scale. However, the fairness of AI prediction in educational contexts has received insufficient attention, which can increase educational inequality. This study aims to fill this gap by proposing a fair logistic regression (Fair-LR) algorithm. Specifically, we developed Fair-LR and compared it with fairness-unaware AI models (Logistic Regression, Support Vector Machine, and Random Forest). We evaluated fairness with equalized odds that caters to statistical type I and II errors in predictions across demographic subgroups. The results showed that the Fair-LR could generate desirable predictive accuracy while achieving better fairness. The findings implied that the educational community could adopt a methodological shift to achieve accurate and fair AI to support learning and reduce bias.