Abstract:

In many contexts, trainees need individualized, on-demand content to refresh skills. To provide this capability, we designed and prototyped an intelligent user interface (IUI) that uses a context-aware system for just-in-time training to convert dense, static technical documentation into dynamic, actionable training content. Traditional technical manuals and training materials are often lengthy and difficult to navigate, making it hard for users to quickly access the critical information they need, whether for routine maintenance or unexpected repairs. Furthermore, existing training materials has been bound by fixed formats and device dependencies, which limits accessibility and flexibility.
This research presents preliminary work on a system designed to overcome the usability constraints of current technical manuals, which impede efficient information retrieval during both standard operations and time-sensitive scenarios. Our system employs computational methods including deep learning, natural language processing techniques, and multimodal artificial intelligence to extract critical features of technical content—including procedural workflows, safety protocols, equipment specifications, and multimedia resources—from heterogeneous documentation repositories. This enables the creation of customized training modules through an integrated multimodel architecture that evaluates task requirements, training content, user expertise, environmental constraints, and device capabilities. This context-aware framework enables the system to deliver personalized training interventions— comprehensive guides for novices, concise refreshers for experienced personnel, and just-in-time support for immediate operational support—optimized for the specific operational setting and context (e.g., noise level, available interaction modalities, time constraints, and others).
We will describe our initial demonstration of a capability to systematically structure and remix training content from diverse sources into a standardized, machine-interpretable format suitable for dynamic distribution across multiple technological platforms, including mobile devices and tablets, desktop interfaces, and immersive reality environments. We will also discuss future applications of this work in both defense operations and commercial sectors, such as automotive repair, maintenance services, and safety procedures.

Keywords: ADAPTIVE;INTELLIGENCE;MACHINE LEARNING;PERSONALIZED TRAINING;REAL-TIME

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Mr. Matthew Miller