@inproceedings{walsh-etal-2025-using,
    title = "Using {LLM}s to identify features of personal and professional skills in an open-response situational judgment test",
    author = "Walsh, Cole  and
      Ivan, Rodica  and
      Iqbal, Muhammad Zafar  and
      Robb, Colleen",
    editor = "Wilson, Joshua  and
      Ormerod, Christopher  and
      Beiting Parrish, Magdalen",
    booktitle = "Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers",
    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/name-variant-enfa-fane/2025.aimecon-main.24/",
    pages = "221--230",
    ISBN = "979-8-218-84228-4",
    abstract = "Current methods for assessing personal and professional skills lack scalability due to reliance on human raters, while NLP-based systems for assessing these skills fail to demonstrate construct validity. This study introduces a new method utilizing LLMs to extract construct-relevant features from responses to an assessment of personal and professional skills."
}