Performance Trust in AI Reduces Cognitive Workload: Evidence from Structural Equation Modeling and Item-Level Analysis

Abstract

Generative AI is increasingly used in work settings, where users often iteratively refine prompts to obtain outputs that match their intentions, potentially increasing cognitive workload. Although trust in AI is considered important for effective human--AI collaboration, how trust relates to cognitive workload---and which trust components matter most---remains unclear. This study experimentally examined trust--workload relationships in prompt-based interaction with an image-generation system. Twenty-three employees performed task-oriented image-generation tasks under two interaction conditions (Automatic vs. Prompt) designed to induce workload differences. Trust was measured using eight MDMT Performance Trust items, and cognitive workload was assessed using the Gas Tank Questionnaire. Analyses proceeded in three steps: (1) item-level correlations, (2) structural equation modeling (SEM) of Performance Trust predicting cognitive workload while controlling for Condition and Theme, and (3) a trust-items-only regression reporting standardized coefficients (\(\beta\)) with 95\% confidence intervals. SEM showed that higher Performance Trust was associated with lower cognitive workload (\(\beta=-0.385, p<.001\)), explaining 35.0\% of the variance (\(R^2=0.350\)). Item-level regression further indicated unequal contributions among trust components. These findings suggest that strengthening Performance Trust and prioritizing workload-relevant trust components can support low-burden human--AI collaboration.

Keywords: Human--AI Interaction, Generative AI, Performance Trust, Cognitive Workload, SEM

DOI: 10.54941/ahfe1007505