AI-Driven Support For Military Training Documentation Design

Abstract

Military documentation plays a central role in training and operational practice, yet its quality is rarely evaluated from the perspective of actual use. Documents tend to prioritise completeness and regulatory compliance over clarity. This results in materials that are information dense, cognitively demanding, and prone to misinterpretation, particularly under time pressure. This article proposes a conceptual framework that integrates eye-tracking research with generative large language models (LLMs) to support the assessment and improvement of military training documentation. The framework envisions eye-tracking data, including fixation durations, regression patterns, and pupil dynamics, collected from representative military personnel, as empirical ground truth for identifying specific structural and linguistic barriers in documents.