@inproceedings{adegoke-etal-2025-medical,
    title = "Medical Item Difficulty Prediction Using Machine Learning",
    author = "Adegoke, Hope Oluwaseun  and
      Du, Ying  and
      Dwyer, Andrew",
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress",
    month = oct,
    year = "2025",
    address = "Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States",
    publisher = "National Council on Measurement in Education (NCME)",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.aimecon-wip.22/",
    pages = "185--190",
    ISBN = "979-8-218-84229-1",
    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."
}