Technology Title
Multiverse: An AI-Enabled XR Training Ecosystem for Data-Driven Maintenance Readiness
Tech Focus Area
Training
Abstract
Problem Statement: Increasingly complex defense systems require maintainers to develop and sustain proficiency across equipment, procedures, and technical information. Traditional training often depends on access to physical equipment, instructors, and static technical documentation, while providing limited objective insight into individual performance and competency. This can limit hands-on practice, skill-gap identification, and readiness for complex maintenance tasks.
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Description of the Innovation Solution: Multiverse is an AI-enabled XR training ecosystem that creates a continuous, data-driven learning cycle. High-fidelity XR environments provide realistic, repeatable maintenance training without continuous access to operational equipment. During training, xAPI captures learner interactions and performance, including procedural progression, task completion, errors, and repetitions. An AI SME Chatbot provides contextual assistance grounded in parsed, approved Technical Orders (TOs). An analytics dashboard supports after-action review and competency tracking. A Personalized Learning Engine uses training data to identify skill gaps and recommend targeted follow-on training, creating a closed loop from immersive practice through measurement, assessment, and personalized Training.
Benefits to the DoW: Multiverse can expand access to repeatable maintenance training, reduce dependence on operational equipment, and enable personnel to practice complex or hazardous procedures in controlled environments. Objective xAPI data enables evidence-based competency assessment and after-action review. AI-supported TO access assists learners during training, while personalized recommendations target identified deficiencies. The architecture can be adapted across ground, aviation, maritime, and other defense maintenance domains, supporting cross-service applicability and improved workforce readiness.
Innovation Challenges: Implementation requires accurate, current technical data and SME validation of XR procedures, competency measures, and AI responses. Technical content must remain configuration-controlled, and AI assistance must remain grounded in approved source material. Transition also requires alignment with DoW cybersecurity, integration, and deployment requirements. A modular open systems architecture supports incremental implementation across different environments.
Technical Maturity / Demonstration Results: The technologies underlying Multiverse are above TRL 5 and have progressed beyond conceptual development. Core capabilities have been prototyped and demonstrated, including high-fidelity XR training, xAPI performance tracking, AI-assisted retrieval from technical documentation, analytics dashboards, competency tracking, personalized learning capabilities, and deployable infrastructure. These mature components provide the foundation for an integrated maintenance training environment and further validation against representative DoW maintenance use cases.
