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

Abstract

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.

Keywords:

• Artificial intelligence in design
• user interface design
• expectation confirmation model
• technology acceptance model
• idea generation

Acknowledgments

We would like to thank the students in this study. This study would not have been possible without their participation and feedback. Finally, we would like to thank the unknown reviewers for their insightful comments and valuable improvements to our study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Funding for this research work was granted by the National Science and Technology Council, Taiwan, under grant number NSTC 112-2635-H-218-001.

Notes on contributors

Yi-Lin Elim Liu

Yi-Lin Elim Liu is an associate professor at the Department of Information and Communication, Southern Taiwan University of Science and Technology, Taiwan. Her research areas include technology-enhanced design thinking and application of advanced technologies to support design education.

Yueh-Min Huang

Yueh-Min Huang is a chair professor at the Department of Engineering Science, National Cheng Kung University, Taiwan. His research interests focus on e-Learning, multimedia communications, artificial intelligence, and embedded systems.