@inproceedings{aguirre-etal-2025-conversational,
    title = "Conversational Tutoring in {VR} Training: The Role of Game Context and State Variables",
    author = "Aguirre, Maia  and
      M{\'e}ndez, Ariane  and
      Garc{\'i}a-Pablos, Aitor  and
      Cuadros, Montse  and
      del Pozo, Arantza  and
      Lopez de Lacalle, Oier  and
      Salaberria, Ander  and
      Barnes, Jeremy  and
      Mart{\'i}nez, Pablo  and
      Afzal, Muhammad Zeshan",
    editor = "Torres, Maria Ines  and
      Matsuda, Yuki  and
      Callejas, Zoraida  and
      del Pozo, Arantza  and
      D'Haro, Luis Fernando",
    booktitle = "Proceedings of the 15th International Workshop on Spoken Dialogue Systems Technology",
    month = may,
    year = "2025",
    address = "Bilbao, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.iwsds-1.23/",
    pages = "215--224",
    ISBN = "979-8-89176-248-0",
    abstract = "Virtual Reality (VR) training provides safe, cost-effective engagement with lifelike scenarios but lacks intuitive communication between users and the virtual environment. This study investigates the use of Large Language Models (LLMs) as conversational tutors in VR health and safety training, examining the impact of game context and state variables on LLM-generated answers in zero- and few-shot settings. Results demonstrate that incorporating both game context and state information significantly improves answer accuracy, with human evaluations showing gains of up to 0.26 points in zero-shot and 0.18 points in few-shot settings on a 0-1 scale."
}