Abstract
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.
This work was supported by the Ministry of Science and Innovation of Spain under project PID2020-119894GB-I00, co-financed through European Union FEDER funds and the project Humanaid (TED2021-129485B-C43) cofunded by “NextGenerationEU”/PRTR funds. It was also supported through REMAR Project (CPP2022-009724) funded by the Ministry of Science and Innovation of Spain (MCIN/AEI/ 10.13039/501100011033) and by the European Union NextGenerationEU/PRTR. And, finally, it was supported through the Consejería de Educación of the Junta de Castilla y León and the European Social Fund through a pre-doctoral grant (EDU/875/2021).
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Lucas-Pérez, G., Ramírez-Sanz, J.M., Serrano-Mamolar, A., Arnaiz-González, Á., Bustillo, A. (2024). Personalising the Training Process with Adaptive Virtual Reality: A Proposed Framework, Challenges, and Opportunities. In: De Paolis, L.T., Arpaia, P., Sacco, M. (eds) Extended Reality. XR Salento 2024. Lecture Notes in Computer Science, vol 15027. Springer, Cham. https://doi.org/10.1007/978-3-031-71707-9_32
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