@inproceedings{arana-etal-2025-foundations,
    title = "Foundations of {PEERS}: Assessing {LLM} Role Performance in Educational Simulations",
    author = "Arana, Jasper Meynard  and
      Carandang, Kristine Ann M.  and
      Casin, Ethan Robert  and
      Alis, Christian  and
      Tan, Daniel Stanley  and
      Legara, Erika Fille  and
      Monterola, Christopher",
    editor = "Zhao, Jin  and
      Wang, Mingyang  and
      Liu, Zhu",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-srw.66/",
    pages = "908--918",
    ISBN = "979-8-89176-254-1",
    abstract = "In education, peer instruction (PI) is widely recognized as an effective active learning strategy. However, real-world evaluations of PI are often limited by logistical constraints and variability in classroom settings. This paper introduces PEERS (Peer Enhanced Educational Realistic Simulation), a simulation framework that integrates Agent-Based Modeling (ABM), Large Language Models (LLMs), and Bayesian Knowledge Tracing (BKT) to emulate student learning dynamics. As an initial step, this study focuses on evaluating whether LLM-powered agents can effectively assume the roles of teachers and students within the simulation. Human evaluations and topic-based metrics show that LLMs can generate role-consistent and contextually appropriate classroom dialogues. These results serve as a foundational milestone toward building realistic, AI-driven educational simulations. Future work will include simulating the complete PEERS framework and validating its accuracy through actual classroom-based PI sessions. This research aims to contribute a scalable, cost-effective methodology for studying instructional strategies in controlled yet realistic environments."
}