@inproceedings{johnson-etal-2025-predicting,
    title = "Predicting and Evaluating Item Responses Using Machine Learning, Text Embeddings, and {LLM}s",
    author = "Johnson, Evelyn  and
      Wei, Hsin-Ro  and
      Wu, Tong  and
      Liu, Huan",
    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.8/",
    pages = "66--70",
    ISBN = "979-8-218-84229-1",
    abstract = "This work-in-progress study compares the accuracy of machine learning and large language models to predict student responses to field-test items on a social-emotional learning assessment. We evaluate how well each method replicates actual responses and examine the item parameters generated by synthetic data to those derived from actual student data."
}