2026-04-28T21:51:26Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/109012025-09-30T00:05:36Zcom_10259_4219com_10259_5086com_10259_2604col_10259_7349

2025-09-29T11:59:57Z urn:hdl:10259/10901 Personalising the Training Process with Adaptive Virtual Reality: A Proposed Framework, Challenges, and Opportunities Lucas Pérez, Gadea Ramírez Sanz, José Miguel Serrano Mamolar, Ana Arnaiz González, Álvar Bustillo Iglesias, Andrés Machine learning Immersive virtual reality Game-based learning Eye-tracking Stress Comunicación presentada en: International Conference on Extended Reality, XR Salento 2024, held in Lecce, Italy during September 4–7, 2024 This work presents a conceptual framework that integrates Artificial Intelligence (AI) into immersive Virtual Reality (iVR) training systems, aiming to enhance adaptive learning environments that dynamically respond to individual users’ physiological states. The framework uses real-time data acquisition from multiple sources, including physiological sensors, eye-tracking and user interactions, processed through AI algorithms to personalise the training experience. By adjusting the complexity and nature of training tasks in real time, the framework seeks to maintain an optimal balance between challenge and skill, fostering an immersive learning environment. This work details some methodologies for data acquisition, the preprocessing required to synchronise and standardise diverse data streams, and the AI training techniques essential for effective real-time adaptation. It also discusses logistical considerations of computational load management in adaptive systems. Future work could explore the scalability of these systems and their potential for self-adaptation, where models are continuously refined and updated in real-time based on incoming data during user interactions. 2025-09-29T11:59:57Z 2025-09-29T11:59:57Z 2024-09-11 info:eu-repo/semantics/conferenceObject 978-3-031-71707-9 978-3-031-71706-2 https://hdl.handle.net/10259/10901 10.1007/978-3-031-71707-9_32 eng Extended Reality: XR Salento 2024, proceedings, Part I, V. 15027, p 376–384 https://doi.org/10.1007/978-3-031-71707-9_32 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119894GB-I00/ES/APRENDIZAJE AUTOMATICO CON DATOS ESCASAMENTE ETIQUETADOS PARA LA INDUSTRIA 4.0/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/TED2021-129485B-C43/ES/Sistemas dinámicos inteligentes centrados en el usuario para la Prevención de Riesgos Laborales/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/CPP2022-009724/ES/Simuladores inteligentes adaptativos en Realidad Extendida para la mejora de procesos de Mantenimiento de Alto Riesgo/REMAR info:eu-repo/semantics/openAccess Springer