Ying Du

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2025

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Medical Item Difficulty Prediction Using Machine Learning
Hope Oluwaseun Adegoke | Ying Du | Andrew Dwyer
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress

This project aims to use machine learning models to predict a medical exam item difficulty by combining item metadata, linguistic features, word embeddings, and semantic similarity measures with a sample size of 1000 items. The goal is to improve the accuracy of difficulty prediction in medical assessment.
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