Modelling generative Al's influence on students' perceived decision capability: A cognitive load and decision augmentation approach

Abstract

There is a resonant association between the implementation of Generative Artificial Intelligence (GenAI) and students’ ability to identify, analyse, and select the best options among alternatives. Accordingly, this study examined the impact of GenAI adoption on business students' perception of their decision-making capability through the lenses of decision augmentation theory (DAT) and cognitive load theory (CLT). A survey was administered to 350 university students studying business in the United Arab Emirates. Using structural equation modelling, the study examined key constructs, including cognitive load reduction, information quality, and decision confidence as mediators, and GenAI trust as a moderator. The results revealed that GenAI significantly improves business students' perceived decision capability by improving confidence in decision-making and information quality. Although GenAI effectively reduces cognitive load, this reduction is negatively associated with students' perceived decision capability. Mediation analysis confirmed that decision confidence and information quality significantly mediate the relationship between GenAI adoption and perceived decision capability. Additionally, moderation analysis revealed that high trust in GenAI weakens the positive effects of GenAI on cognitive load reduction and information quality. These findings emphasise the importance of balanced GenAI integration, critical user engagement, and responsible trust development. The study provides valuable insights for students, educators, academic institutions, and industries seeking to use GenAI tools for enhanced learning and decision-making, and identifies limitations and directions for future research on GenAI-enabled education.