Date
Publisher
arXiv
This paper presents a dynamic gamification architecture for an Extended Reality Artificial Intelligence virtual training environment designed to enhance STEM education through immersive adaptive, and kinesthetic learning. The proposed system can be introduced in four phases: Introduction Phase, Component Development Phase, Fault Introduction and Correction Phase and Generative AI XR scenarios Phase. Security and privacy are discussed via a defense-in-depth approach spanning client, middleware, and backend layers, incorporating AES 256 encryption, multi-factor authentication, role-based access control and GDPR or FERPA compliance. Risks such as sensor exploitation, perceptual manipulation, and virtual physical harm are identified, with mitigation strategies embedded at the design stage. Potential barriers to large scale adoption-including technical complexity, cost of deployment, and need for cybersecurity expertise are discussed.
Study at a glance
- Question: How can extended reality and artificial intelligence be integrated to support remote STEM education and assessment?
- Setting and data: DeVry University engineering courses; conceptual platform design using the Unity Asset Store School Asset package (145 items).
- Method: System design and architecture description
Why it matters
STEM disciplines remain underrepresented in online learning because traditional platforms lack hands-on laboratory experiences, interactivity, and real-time feedback essential for STEM pedagogy.
Key findings
- The authors design a dynamic XR-AI architecture across four phases: introduction, component development, fault introduction and correction, and generative AI scenarios.
- The system uses a three-tier architecture connecting a client VR interface, a Photon Fusion middleware networking layer, and a cloud-based backend analytics database.
- Threat mitigation requires layered security including AES-256 encryption, role-based access control, multi-factor authentication, and privacy opt-out mechanisms for regulatory compliance.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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Study design
