Modern militaries are investing heavily in advanced weapons, autonomous systems, and battlefield technologies. Yet preparing personnel to operate these systems presents a growing challenge, particularly as forces seek to expand training capacity without proportionately increasing personnel and resources.
The pressure is driving investment in AI-assisted training. In July 2026, the UK Ministry of Defense announced a £2 billion program designed to train up to 60,000 soldiers annually using AI, analytics, and virtual environments. NATO is also exploring AI to reduce the personnel and time required to prepare and assess military exercises.
But increasing training capacity raises another question regarding how militaries can provide individualized instruction when experienced instructors cannot monitor every trainee continuously.
Israeli defense technology company Eyeviation is addressing this gap through Human-Aware AI, combining artificial intelligence, sensor data, and behavioral analytics to identify factors affecting individual performance and deliver personalized instruction.
Its Virtual Cognitive-Aware Trainer (VCAT) applies this approach to military firearms training, analyzing how soldiers perceive, react, and perform during simulated exercises to identify weaknesses and guide improvement.
Beyond Conventional Simulation
Simulation allows military personnel to practice repeatedly while reducing dependence on ammunition, firing ranges, and other physical resources. However, additional practice does not necessarily address the causes of poor performance.
In firearms training, instructors can measure accuracy, speed, and consistency, but understanding why a soldier misses a target is more difficult. The cause might involve visual attention, delayed reactions, or inconsistent physical technique, each requiring a different correction.
A March 2026 Council on Geostrategy report identifies AI-powered performance assessment and personalized feedback as important developments in military training, enabling more detailed analysis of trainee movements, decisions, and reactions.
Eyeviation’s VCAT combines visual, cognitive, behavioral, and motor signals with shooting performance data. Using technologies including eye-tracking and reaction-time analysis, the platform identifies patterns that conventional scoring alone may overlook.
These insights help tailor feedback to the factors limiting each trainee, rather than relying exclusively on standardized exercises or generalized corrections.
Scaling Individualized Training Across Military Units
Eyeviation recently released performance data from VCAT evaluations conducted across multiple NATO member-state military units.
According to the company, trainees demonstrated average improvements of 42% in shooting accuracy, 19% in speed, and 20% in precision, while reducing the live-fire ammunition required to meet training objectives by at least 30%.
Trainees also completed the simulated equivalent of more than 1,200 live-fire rounds in approximately three hours, allowing repeated practice and corrective feedback within a compressed training period.
Beyond individual performance, VCAT enables a single operator to oversee up to 12 soldiers simultaneously, increasing training capacity without requiring a proportional expansion of instructional personnel.
While broader validation across different training conditions remains important, the reported results illustrate the potential for AI-assisted instruction to improve proficiency and resource efficiency.
Extending the Reach of Instructors
Experienced instructors remain essential to military training, but training requires years to build expertise and practical knowledge, and their attention is finite. As training groups grow, opportunities for individual observation and correction become more limited.
AI-assisted systems can help address this constraint by continuously monitoring performance, identifying weaknesses, and automating elements of assessment and feedback.
This approach does not eliminate the need for human expertise or replace instructors. Instead, it allows them to concentrate on complex training needs and situations requiring human judgment and direct intervention.
For military organizations, the potential benefit extends beyond ammunition savings or shorter training sessions. It lies in delivering more consistent, individualized instruction across larger groups while preserving established training standards and integrating with existing training frameworks.
Human-Aware AI Beyond Firearms Training
The underlying technology also has applications in operational environments where attention, decision-making, and physical responses influence performance.
Spain’s 2026 Defence Technology and Innovation Strategy identifies human-performance monitoring, cognitive training, and simulation-based assessment as priorities for improving soldier capabilities.
Eyeviation applies its Human-Aware AI approach beyond firearms training, including specialized simulation-based, cognitive-heavy military screening and assessment through additional platforms.
All of these applications reflect a broader shift toward understanding not only what personnel accomplish, but the human factors influencing their performance. Understanding how individuals process information and respond to demanding situations can provide insights that conventional performance measurements alone may miss.
The Next Stage of Military Readiness
While much of defense AI development focuses on autonomous systems, intelligence processing, and operational decision support, human-performance analysis represents another increasingly relevant application.
Eyeviation’s VCAT illustrates how combining behavioral analytics with simulation can help translate performance data into individualized instruction.
As militaries seek to improve readiness while managing training capacity and resource constraints, technologies that extend expert guidance across larger groups could become an increasingly important part of military training and operational readiness.
The next advancement in military training may be less about creating more sophisticated simulations and more about understanding the people training within them.
