@inproceedings{betts-muntean-2025-evaluating,
    title = "Evaluating Deep Learning and Transformer Models on {SME} and {G}en{AI} Items",
    author = "Betts, Joe  and
      Muntean, William",
    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.17/",
    pages = "141--146",
    ISBN = "979-8-218-84229-1",
    abstract = "This study leverages deep learning, transformer models, and generative AI to streamline test development by automating metadata tagging and item generation. Transformer models outperform simpler approaches, reducing SME workload. Ongoing research refines complex models and evaluates LLM-generated items, enhancing efficiency in test creation."
}