AI-MDD-UX: Revolutionizing E-Commerce User Experience with Generative AI and Model-Driven Development

Abstract

E-commerce applications have emerged as key drivers of digital transformation, reshaping consumer behavior and driving demand for seamless online transactions. Despite the growth of smart mobile technologies, existing methods rely on fixed UI content that cannot adjust to local cultural preferences and fluctuating user behaviors. This paper explores the combination of generative Artificial Intelligence (AI) technologies with Model-Driven Development (MDD) to enhance personalization, engagement, and adaptability in e-commerce. Unlike static adaptation approaches, generative AI enables real-time, adaptive interactions tailored to individual needs, providing a more engaging and adaptable user experience. The proposed framework follows a three-tier architecture: first, it collects and analyzes user behavior data from UI interactions; second, it leverages MDD to model and personalize user personas and interactions and third, AI techniques, including generative AI and multi-agent reinforcement learning, are applied to refine and optimize UI/UX design. This automation-driven approach uses a multi-agent system to continuously enhance AI-generated layouts. Technical validation demonstrated strong user engagement across diverse platforms and superior performance in UI optimization, achieving an average user satisfaction improvement of 2.3% compared to GAN-based models, 18.6% compared to Bootstrap-based designs, and 11.8% compared to rule-based UI adaptation. These results highlight generative AI-driven MDD tools as a promising tool for e-commerce, enhancing engagement, personalization, and efficiency.