@inproceedings{tran-etal-2025-using,
    title = "Using Large Language Models to Analyze Students' Collaborative Argumentation in Classroom Discussions",
    author = "Tran, Nhat  and
      Litman, Diane  and
      Godley, Amanda",
    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/ingest-emnlp/2025.aimecon-main.13/",
    pages = "111--125",
    ISBN = "979-8-218-84228-4",
    abstract = "Collaborative argumentation enables students to build disciplinary knowledge and to think in disciplinary ways. We use Large Language Models (LLMs) to improve existing methods for collaboration classification and argument identification. Results suggest that LLMs are effective for both tasks and should be considered as a strong baseline for future research."
}