@inproceedings{mojoyinola-etal-2025-enhancing,
    title = "Enhancing Item Difficulty Prediction in Large-scale Assessment with Large Language Model",
    author = "Mojoyinola, Mubarak  and
      Kehinde, Olasunkanmi James  and
      Tang, Judy",
    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.27/",
    pages = "218--222",
    ISBN = "979-8-218-84229-1",
    abstract = "Field testing is a resource-intensive bottleneck in test development. This study applied an interpretable framework that leverages a Large Language Model (LLM) for structured feature extraction from TIMSS items. These features will train several classifiers, whose predictions will be explained using SHAP, providing actionable, diagnostic insights insights for item writers."
}