Abstract
The evolution of AI technologies—encompassing large language models (LLMs), compound AI systems, and large multimodal models (LMMs)—is reshaping the landscape of UX design. These advancements enable AI agents to achieve deeper context awareness by integrating diverse data streams, such as text, images, and voice, to deliver tailored, real-time solutions. To prepare future designers for these challenges, UX design education must evolve, equipping students with the skills to design adaptive, ethical, and user-centered AI-driven systems. This paper introduces a reimagined educational framework for UX design that emphasizes human-AI collaboration and multimodal interaction design. By teaching students to design systems where AI agents leverage LMMs for rich context understanding, the framework prepares them to create personalized, intuitive, and dynamic solutions. The curriculum integrates cutting-edge tools and techniques, empowering students to address challenges like transparency, adaptability, and system-level coherence in their designs. Two case studies—an AI-driven medication management system for older adults and an AI-supported creative workflow assistant—will be briefly introduced to illustrate how this approach can be applied. These examples demonstrate the potential of multimodal AI agents to address complex user needs, from improving accessibility to streamlining creative processes. This study offers educators practical strategies for updating UX curricula to reflect the opportunities and challenges of advanced AI models. We offer insights into fostering the next generation of designers equipped to lead the development of impactful, multimodal AI solutions for diverse domains.
Presenters
Min KangAssistant Professor, Industrial Design, University of Houston, Texas, United States
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
UX Design, AI, HCI, AI Agent-driven Solutions, UX Design Education
