@inproceedings{zhang-etal-2025-llm-based,
    title = "{LLM}-Based Approaches for Detecting Gaming the System in Self-Explanation",
    author = "Zhang, Jiayi (Joyce)  and
      Baker, Ryan S.  and
      McLaren, Bruce M.",
    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/name-variant-enfa-fane/2025.aimecon-main.10/",
    pages = "91--98",
    ISBN = "979-8-218-84228-4",
    abstract = "This study compares two LLM-based approaches for detecting gaming behavior in students' open-ended responses within a math digital learning game. The sentence embedding method outperformed the prompt-based approach and was more conservative. Consistent with prior research, gaming correlated negatively with learning, highlighting LLMs' potential to detect disengagement in open-ended tasks."
}