@inproceedings{weatherholtz-etal-2025-cognitive,
    title = "Cognitive Engagement in {G}en{AI} Tutor Conversations: At-scale Measurement and Impact on Learning",
    author = "Weatherholtz, Kodi  and
      Hill, Kelli Millwood  and
      Dicerbo, Kristen  and
      Wells, Walt  and
      Grimaldi, Phillip  and
      Miller-Vedam, Maya  and
      Hogg, Charles  and
      Yamkovenko, Bogdan",
    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.6/",
    pages = "40--48",
    ISBN = "979-8-218-84229-1",
    abstract = "We developed and validated a scalable LLM-based labeler for classifying student cognitive engagement in GenAI tutoring conversations. Higher engagement levels predicted improved next-item performance, though further research is needed to assess distal transfer and to disentangle effects of continued tutor use from true learning transfer."
}