This study analyses the multimodal evaluation of cognitive load in complex visuomotor
test, specifically focusing on implicit bias within human-IA interaction in the context of
Industry 5.0. The research aims to understand how unconscious prejudices, measured
through the Implicit Association Test (IAT or TAI, Spanish version), modulate user
experience and how intelligent systems can act as emotional regulators. Using a multiple
single case experimental design (n=3) three female university students aged 20 were
evaluated. The methodology integrated subjective measures via NASA-TLX,
physiological responses using Galvanic Skin Response (GSR) and
electroencephalography (EEG), and visual behaviour through eye-tracking technology.
In addition, a conversational chatbot named EsencIA was developed to provide
psychological support based on cognitive-behavioural therapy for participants
experiencing high frustration. Results indicate that participants perceive the TAI as a
demanding task with medium-high mental load, where the temporal demand and effort
are prominent. Quantitative data show a correlation between the intensity of implicit bias
and visual exploration patterns, suggesting that more consolidated biases require greater
cognitive supervision and increased saccadic movements. These findings highlight the
importance of integrating multimodal metrics to understand cognitive and emotional
processing, confirming the potential of IA as a support tool in high-demand environment
to mitigate stress and improve user interaction.
