Accurate Predictions in Education with Discrete Variational Inference

This paper introduces probabilistic models using Item Response Theory and novel discrete variational inference to predict whether students will answer mathematics exam questions correctly, releasing a large dataset of marked formal mathematics exam responses. The work focuses on adaptive learning systems that personalize question difficulty to maintain student engagement in the optimal learning zone.

One of the largest drivers of social inequality is unequal access to personal tutoring, with wealthier individuals able to afford it, while the majority cannot. Affordable, effective AI tutors offer a scalable solution. We focus on adaptive learning, predicting whether a student will answer a question correctly, a key component of any effective tutoring system. Yet many platforms struggle to achieve high prediction accuracy, especially in data-sparse settings. To address this, we release the lar

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