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2024 | OriginalPaper | Chapter

Personalising the Training Process with Adaptive Virtual Reality: A Proposed Framework, Challenges, and Opportunities

Authors : Gadea Lucas-Pérez, José Miguel Ramírez-Sanz, Ana Serrano-Mamolar, Álvar Arnaiz-González, Andrés Bustillo

Published in: Extended Reality

Publisher: Springer Nature Switzerland

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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.

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Metadata
Title
Personalising the Training Process with Adaptive Virtual Reality: A Proposed Framework, Challenges, and Opportunities
Authors
Gadea Lucas-Pérez
José Miguel Ramírez-Sanz
Ana Serrano-Mamolar
Álvar Arnaiz-González
Andrés Bustillo
Copyright Year
2024
DOI
https://doi.org/10.1007/978-3-031-71707-9_32

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