ScholarWorks@숙명여자대학교

초록

This study aims to apply generative AI tools throughout the entire process of design thinking-based UX projects in UX/UI design education and to analyze their efficiency and user satisfaction compared to traditional UX design methods. Twenty design students sequentially carried out the same project tasks using traditional methods and generative AI tools. Survey data on efficiency and satisfaction at each UX stage were analyzed using the Wilcoxon signed-rank test. The analysis revealed that generative AI tools contributed to improving efficiency in structured tasks such as research during the empathy stage, but were less effective in creative and context-based tasks like ideation, persona creation, and UI design. Grounded in the design thinking process, the study found that these tools struggled to maintain contextual continuity across stages, and students experienced significant cognitive load in prompt creation and result interpretation. While generative AI tools are useful for repetitive and structured work, they need improvement for tasks requiring creativity and contextual understanding. This highlights the need to shift AI’s role from a simple assistant to a learning partner, and to minimize data flow interruptions by systematizing AI intervention in UX design education. Educators can reduce students’ cognitive burden and enhance engagement by designing and applying AI tools more effectively, allowing students to focus on critical thinking, creative problem-solving, and reflective decision-making. The study clarifies the possibilities and limitations of AI in UX education and calls for further research on AI-based learning modules, curriculum design, and strategies that position AI as a partner in learning.

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