The Factors Influencing Learning Satisfaction and Perceived Learning Outcomes in Generative AI Mobile Applications

This study found that in generative artificial intelligence (AI) mobile application–supported learning environments, perceived usefulness and perceived enjoyment are the strongest predictors of learning engagement. However, surprisingly, perceived ease of use was not found to be influential among digital natives. This study aims to explore how generative AI mobile applications influence learning satisfaction and perceived learning outcomes through learning engagement and cognitive load by integrating the Technology Acceptance Model, Task–Technology Fit Theory, and Cognitive Load Theory. Structural equation modeling analysis of data from 612 interior design students revealed that personalized feedback reduced cognitive load. The results extend the Technology Acceptance Model in AI-driven educational contexts and indicate that digital natives place greater value on functional utility and experiential value than on perceived ease of use.