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Title: Generalized Structure for Adaptable Immersive Learning Environments
Immersive Learning Environments (ILEs) developed in Virtual and Augmented Reality (VR/AR) are a novel pro- fessional training platform. An ILE can facilitate an Adaptive Learning System (ALS), which has proven beneficial to the learning process. However, there is no existing AI-ready ILE that facilitates collecting multimedia multimodal data from the environment and users for training AI models, nor allows for the learning contents and complex learning process to be dynamically adapted by an ALS. This paper proposes a novel multimedia system in VR/AR to dynamically build ILEs for a wide range of use-cases, based on a description language for the generalizable ILE structure. It will detail users’ paths and conditions for completing learning activities, and a content adaptation algorithm to update the ILE at runtime. Human and AI systems can customize the environment based on user learning metrics. Results show that this framework is efficient and low- overhead, suggesting a path to simplifying and democratizing the ILE development without introducing bloat. Index Terms—virtual reality, augmented reality, content generation, immersive learning, 3D environments  more » « less
Award ID(s):
2029557
PAR ID:
10339243
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI)
Page Range / eLocation ID:
294 to 301
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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