Abstract
This study analyzed the impact of adaptive human--machine interfaces based on artificial intelligence and neuroergonomic principles on the reduction of human errors in complex industrial systems. A quasi-experimental design was developed using an industrial process supervision simulation, comparing a conventional interface with an intelligent adaptive interface. The results showed a significant decrease in the number of operational errors, an improvement in response time, and a reduction in the cognitive load perceived by operators. Likewise, a positive relationship was observed between the reduction of cognitive load and the decrease in errors. The findings suggest that adaptive interfaces can improve human performance and safety in advanced industrial environments.
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