AI Insight
This study examined how undergraduate e-commerce students' perceptions of AI agent capabilities relate to their cognitive load, self-efficacy, and engagement in systems development courses. Results showed that perceiving AI agents as more capable was associated with reduced extraneous cognitive load and increased academic self-efficacy, both of which were linked to higher student engagement. The research applies Cognitive Load Theory and Social Cognitive Theory to understand the psychological mechanisms through which generative AI may support learning in complex problem-solving contexts.
Why it matters
The findings suggest that integrating generative AI tools in technical education courses may help students manage cognitive demands and build confidence, potentially improving engagement in challenging subjects. This provides practical guidance for educators designing AI-supported learning environments in systems development and similar technical fields.
Understand the Science
Generative AI agents are increasingly emerging as important learning partners for supporting complex problem-solving in higher education; however, the psychological processes associated with student engagement remain insufficiently understood. Grounded in Cognitive Load Theory and Social Cognitive Theory, this study examines how perceived Agentic AI Capability is associated with students’ extraneous cognitive load, academic self-efficacy, and overall student engagement in systems development courses. An empirical study was conducted among undergraduate students majoring in e-commerce who participated in systems development-related courses. The results indicate that perceived Agentic AI Capability is negatively associated with extraneous cognitive load and positively associated with academic self-efficacy. Extraneous cognitive load is negatively associated with academic self-efficacy and student engagement, whereas academic self-efficacy is positively associated with student engagement. The indirect associations through academic self-efficacy and the sequential pathway are statistically significant. The findings provide evidence on cognitive and motivational correlates of AI-supported learning while, given the cross-sectional design, not establishing causal or temporal relationships. This study extends the application of Cognitive Load Theory and Social Cognitive Theory to human–AI collaborative learning environments and provides theoretical insights and practical implications for integrating generative AI into systems development education.