@inproceedings{meng-etal-2025-identifying,
    title = "Identifying Biases in Large Language Model Assessment of Linguistically Diverse Texts",
    author = "Meng, Lionel Hsien  and
      Karumbaiah, Shamya  and
      Saravanan, Vivek  and
      Bolt, Daniel",
    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.25/",
    pages = "204--210",
    ISBN = "979-8-218-84229-1",
    abstract = "The development of Large Language Models (LLMs) to assess student text responses is rapidly progressing but evaluating whether LLMs equitably assess multilingual learner responses is an important precursor to adoption. Our study provides an example procedure for identifying and quantifying bias in LLM assessment of student essay responses."
}