@inproceedings{wang-2025-exploring,
    title = "Exploring the Psychometric Validity of {AI}-Generated Student Responses: A Study on Virtual Personas' Learning Motivation",
    author = "Wang, Huanxiao",
    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/author-page-yu-wang-polytechnic/2025.aimecon-main.39/",
    pages = "359--366",
    ISBN = "979-8-218-84228-4",
    abstract = "This study explores whether large language models (LLMs) can simulate valid student responses for educational measurement. Using GPT-4o, 2000 virtual student personas were generated. Each persona completed the Academic Motivation Scale (AMS). Factor analyses(EFA and CFA) and clustering showed GPT-4o reproduced the AMS structure and distinct motivational subgroups."
}