@inproceedings{mikeska-etal-2025-generative,
    title = "Generative {AI} Teaching Simulations as Formative Assessment Tools within Preservice Teacher Preparation",
    author = "Mikeska, Jamie N.  and
      Bhatia, Aakanksha  and
      Halder, Shreyashi  and
      Maxwell, Tricia  and
      Beigman Klebanov, Beata  and
      Longwill, Benny  and
      Behl, Kashish  and
      Shekell, Calli",
    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.23/",
    pages = "212--220",
    ISBN = "979-8-218-84228-4",
    abstract = "This paper examines how generative AI (GenAI) teaching simulations can be used as a formative assessment tool to gain insight into elementary preservice teachers' (PSTs') instructional abilities. This study investigated the teaching moves PSTs used to elicit student thinking in a GenAI simulation and their perceptions of the simulation{'}s"
}