@inproceedings{wu-yang-2025-automated,
    title = "Automated Item Neutralization for Non-Cognitive Scales: A Large Language Model Approach to Reducing Social-Desirability Bias",
    author = "Wu, Sirui  and
      Yang, Daijin",
    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.1/",
    pages = "1--13",
    ISBN = "979-8-218-84229-1",
    abstract = "This study explores an AI-assisted approach for rewriting personality scale items to reduce social desirability bias. Using GPT-refined neutralized items based on the IPIP-BFM-50, we compare factor structures, item popularity, and correlations with the MC-SDS to evaluate construct validity and the effectiveness of AI-based item refinement in Chinese contexts."
}