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        <title>Medical Item Difficulty Prediction Using Machine Learning</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Medical Item Difficulty Prediction Using Machine Learning
%A Adegoke, Hope Oluwaseun
%A Du, Ying
%A Dwyer, Andrew
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
%D 2025
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-218-84229-1
%F adegoke-etal-2025-medical
%X 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.
%U https://aclanthology.org/2025.aimecon-wip.22/
%P 185-190

Markdown (Informal)

[Medical Item Difficulty Prediction Using Machine Learning](https://aclanthology.org/2025.aimecon-wip.22/) (Adegoke et al., AIME-Con 2025)

• Medical Item Difficulty Prediction Using Machine Learning (Adegoke et al., AIME-Con 2025)

ACL

• Hope Oluwaseun Adegoke, Ying Du, and Andrew Dwyer. 2025. Medical Item Difficulty Prediction Using Machine Learning. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 185–190, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).