@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Conversational Tutoring in VR Training: The Role of Game Context and State Variables
%A Aguirre, Maia
%A Méndez, Ariane
%A García-Pablos, Aitor
%A Cuadros, Montse
%A del Pozo, Arantza
%A Lopez de Lacalle, Oier
%A Salaberria, Ander
%A Barnes, Jeremy
%A Martínez, Pablo
%A Afzal, Muhammad Zeshan
%Y Torres, Maria Ines
%Y Matsuda, Yuki
%Y Callejas, Zoraida
%Y del Pozo, Arantza
%Y D’Haro, Luis Fernando
%S Proceedings of the 15th International Workshop on Spoken Dialogue Systems Technology
%D 2025
%8 May
%I Association for Computational Linguistics
%C Bilbao, Spain
%@ 979-8-89176-248-0
%F aguirre-etal-2025-conversational
%X 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.
%U https://aclanthology.org/2025.iwsds-1.23/
%P 215-224
Markdown (Informal)
[Conversational Tutoring in VR Training: The Role of Game Context and State Variables](https://aclanthology.org/2025.iwsds-1.23/) (Aguirre et al., IWSDS 2025)
ACL
- Maia Aguirre, Ariane Méndez, Aitor García-Pablos, Montse Cuadros, Arantza del Pozo, Oier Lopez de Lacalle, Ander Salaberria, Jeremy Barnes, Pablo Martínez, and Muhammad Zeshan Afzal. 2025. Conversational Tutoring in VR Training: The Role of Game Context and State Variables. In Proceedings of the 15th International Workshop on Spoken Dialogue Systems Technology, pages 215–224, Bilbao, Spain. Association for Computational Linguistics.