Modelling Generative AI's Influence on Students' Perceived Decision Capability: A Cognitive Load and Decision Augmentation Approach

Synthesis: Rai, Kathuria, Kaur, Singh, and Itani (2026) examine 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 of 350 university business students in the United Arab Emirates, analyzed with structural equation modeling, examined cognitive load reduction, information quality, and decision confidence as mediators, and GenAI trust as a moderator. Results revealed that GenAI significantly improves 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 perceived decision capability. Mediation analysis confirmed that decision confidence and information quality significantly mediate the relationship between GenAI adoption and perceived decision capability, while moderation analysis revealed that high trust in GenAI weakens the positive effects of GenAI on cognitive load reduction and information quality.

Key Findings

• GenAI adoption significantly improves business students' perceived decision capability by improving decision confidence and information quality.
• Although GenAI effectively reduces cognitive load, this reduction is negatively associated with perceived decision capability.
• Decision confidence and information quality significantly mediate the relationship between GenAI adoption and perceived decision capability.
• High trust in GenAI weakens the positive effects of GenAI on cognitive load reduction and information quality.
• Findings emphasize the importance of balanced GenAI integration, critical user engagement, and responsible trust development.

What this means for practice

• Instructors. Build tasks in which students analyze and justify GenAI recommendations, because GenAI's reduction of cognitive load was negatively associated with perceived decision capability in the model.
• Instructors. Teach the tool's limitations explicitly rather than promoting trust for its own sake: high Trust in GenAI weakened its positive effects on both cognitive load reduction and information quality.
• Instructors. Give students repeated practice with GenAI so that decision confidence develops — confidence was the strongest mediator from adoption to perceived decision capability.
• Administrators. Embed GenAI literacy, ethical reasoning, and critical evaluation of AI output in curriculum frameworks, since the study ties balanced integration — AI use that supports rather than substitutes for student cognition — to perceived decision capability, alongside critical engagement and responsible trust development.
• Researchers. Pair perceived capability with objective indicators such as task accuracy or an experimental decision-making task, since the study's task-agnostic measure leaves perceived and actual decision quality untested against each other.

Limitations

• The design was purely quantitative, which the authors state may have prevented the study from fully capturing the depth of students' attitudes and cognitive behaviors related to GenAI adoption.
• The sample is 350 business students in the United Arab Emirates, one educational category in one country, which the authors say limits generalizability to other institutions, disciplines, and geographical areas.
• Perceived decision capability was defined within a task-agnostic model rather than a comparison of performance on a single business analytical task such as a case study or project evaluation.
• Every construct — cognitive load, information quality, decision confidence, trust, and perceived decision capability — was self-reported in one cross-sectional survey, with no objective performance indicator or experimental decision task.

Connected Concepts

• Generative AI
• Cognitive Psychology
• Business Education
• Technology Adoption Models
• Trust
• Cognitive Offloading
• Self-Efficacy

Connected Articles

• [genai-motivation-engagement-2026] — GenAI motivation and engagement research
• [acceptance-ai-english-tools-2026] — acceptance of AI English tools
• [student-dependency-on-ai-literacy-self-efficacy-2026] — AI literacy, self-efficacy and dependency

Citation

Rai, J. S., Kathuria, S., Kaur, H., Singh, A., & Itani, M. N. (2026). Modelling generative AI's influence on students' perceived decision capability: A cognitive load and decision augmentation approach. Computers and Education: Artificial Intelligence, 10, 100596.