Volume 11 • Issue 2 • PP: 16–21 • 2026
Adaptive Interface Personalization through Real-Time Cognitive Load Detection
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High-stakes computer work often requires users to interpret dense visual information while responding to timesensitive events. Static interfaces can become counterproductive in such conditions because the amount of information presented to the user does not change when mental demand rises. This paper presents an adaptive interface personalization approach that detects cognitive load from pupillometry, heart-rate variability, gaze behaviour, and interaction traces, then selects a transparent interface response. The proposed approach does not simply reduce screen content; it chooses between full, highlighted, simplified, and critical-only modes while preserving user control and explanation cues. A feature-level experimental analysis was conducted using a multimodal workload table structured around public cognitive-load datasets and high-stakes monitoring tasks. The results show that pupil expansion, lower HRV, response delay, gaze dispersion, and screen density jointly indicate rising cognitive load. The adaptation policy reduced predicted interaction errors and shortened response latency in high-load windows while maintaining explanation support for user trust. The findings suggest that cognitive-load detection should be treated as a personalization service rather than a hidden automation layer.
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References
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