GAN-Prototype: Generative Adversarial Networks for Rapid UX Prototype Generation

Abstract Generative Adversarial Networks (GANs) have shown remarkable potential in various domains, and their application in user experience (UX) design offers a transformative approach. In this paper, we present GAN-Prototype, a novel framework designed to automate UX prototype generation. By employing an adversarial model, GAN-Prototype consists of a generator that creates realistic prototypes grounded on specified design parameters, while a discriminator assesses these designs for authenticity and usability. This dual mechanism allows for rapid development of high-fidelity prototypes, significantly reducing the time typically needed in conventional design practices. The framework's learning process utilizes a rich dataset of existing UX designs, capturing the complex interplay between design features and user preferences. Additionally, incorporating user feedback mechanisms facilitates iterative improvements to prototypes based on actual user interactions. Experimental results confirm that GAN-Prototype accelerates the design process without compromising user satisfaction, evidencing its role in enhancing efficiency and innovation within UX design. Practical case studies further illustrate the framework's applicability across diverse design scenarios, affirming its potential impact on designers' productivity and creativity.