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4 February, 2026
EN
In a simulator session, every movement is recorded: when a warning appeared, when the trainee reacted, and whether procedures were followed. Yet even the most detailed playback can leave instructors asking the same question: why did they respond that way?
Traditional training systems capture performance, not perception. They show what happened, but not what the trainee actually noticed or overlooked in the moment.
That missing context makes it harder to assess core competencies like situational awareness, monitoring, and workload management — all skills emphasized in evidence-based and competency-based training frameworks.
Eye tracking makes those behaviors visible. By showing exactly where a trainee directs their attention during a scenario, it gives instructors objective insight into how information is processed and acted on under pressure.
Across aviation, air traffic control, and rail, training organizations are looking for more consistent, data-driven methods to evaluate human performance.
Attention data is an important part of that conversation, giving instructors something they’ve long wanted: tangible evidence of what was seen, what was missed, and how that awareness shaped each decision.
Even with detailed simulator data, instructors often find themselves guessing. A delayed response or missed cue might stem from distraction, incomplete scanning, or cognitive overload. The numbers alone don’t tell the full story.
That uncertainty leads to inconsistency in how performance is judged. Two instructors can watch the same session and draw different conclusions, which makes it difficult to compare results or ensure fairness across trainees. And as training programs grow and adopt more standardized approaches, the need for measurable, repeatable evaluation becomes clear.
Frameworks like Evidence-Based Training (EBT) and Competency-Based Training and Assessment (CBTA) are changing how simulator performance is assessed. They call for observable, quantifiable behaviors as proof of skill.
To meet these standards, training organizations need indicators that connect human behavior with performance outcomes. Attention has become one of the most important indicators: a measurable sign of how operators process information and maintain awareness in complex environments.
Eye tracking turns attention into something measurable. By recording where and for how long a trainee looks, it captures patterns that reflect how information is processed during a task.
These metrics can be visualized in several ways:
• Scan paths show the sequence of eye movements across instruments or displays
• Dwell time indicates how long attention lingers on each area
• Missed-cue detection highlights critical elements that were never looked at
Together, these patterns help instructors see how effectively a trainee distributes their attention under pressure.
When analyzed alongside simulator data, attention metrics provide direct evidence of key competencies such as situational awareness, workload management, and error detection:
| Competency | Attention Metric | Example in Training |
|---|---|---|
| Situational awareness | Scan coverage across displays. | Pilot maintains periodic checks of altitude, heading, and warning indicators. |
| Workload management | Dwell balance and fixation duration. | Air traffic controller maintains even attention between radar sectors. |
| Error detection | Missed-cue identification. | Train driver fails to look at a changing signal before braking event. |
Instead of replacing instructor judgment, these insights reinforce it by giving instructors objective data that supports what they observe in real time.
Instructors who use gaze replay often describe it as seeing a familiar session from a completely new angle. The simulator data stays the same, but now they can also follow the trainee’s line of sight through every second of the event.
During debrief, this makes key moments unmistakable. When a warning light appears, the instructor can see exactly when the trainee’s attention reached it, or if it never did. They can trace how focus shifted between instruments, or how long the trainee fixated on a single display while missing changes elsewhere.
That clarity leads to more targeted feedback. Instead of guessing whether a delayed response was caused by distraction or misunderstanding, instructors can point to the exact moment attention drifted. Trainees learn faster, errors repeat less often, and feedback becomes grounded in observable behavior rather than interpretation.
The challenges of attention and awareness aren’t unique to flight decks. Air traffic controllers, rail operators, and maritime crews all work in environments where seconds matter and situational awareness keepspeople safe.
Eye tracking applies naturally to each of these settings. In control rooms, it helps evaluate how operators manage multiple displays under workload. In rail training, it shows whether drivers maintain visual contact with critical signals. On a ship’s bridge, it highlights how crews monitor changing conditions across complex instrument panels.
Wherever safety depends on maintaining focus in dynamic conditions, attention data gives training teams a clear way to measure it and to improve it.
Performance data shows what happened. Attention data shows how it happened, and what the operator actually saw in the moment. With that context, instructors can see not just the outcome of a task, but the reasoning that led to it.
For training organizations, this means a more objective way to evaluate awareness, reduce repeated errors, and reach the root of performance issues faster.
If you’re exploring ways to integrate attention data into your training systems, Smart Eye’s eye tracking solutions are built to fit naturally into existing simulators and analysis tools.
