Exploring the Perceptions and Continuance Intention of AI-Based Text-to-Image Technology in Supporting Design Ideation

Artificial intelligence (AI)-based text-to-image technologies have recently gained considerable attention, but their specific applications for educational purposes remain relatively unexplored. This research aims to bridge this gap by developing a theoretical model that combines constructs from the Expectation Confirmation Model (ECM) with the Technology Acceptance Model (TAM) to understand the sustainable use of AI-driven visual synthesis in design ideation. Data was collected via a survey involving 106 vocational university students who were enrolled in a user interface (UI) design course to test the proposed model. The hypotheses analysis demonstrated that confirmation positively influenced perceived usefulness, perceived ease of use, and satisfaction. Furthermore, perceived usefulness had a positive impact on satisfaction. Students' perceptions of the utility, usability, and satisfaction of AI-driven visual synthesis also positively affected their intention to continue using the technology. However, the hypothesis proposing a positive relationship between perceived ease of use and user satisfaction did not find support. A moderation analysis revealed that novice design students were susceptible to effort expectancy, negatively affecting their satisfaction with the technology. These findings offer valuable practical implications for developers, designers, and instructors interested in utilizing AI-driven visual synthesis for educational purposes in UI design.