Human–AI Interaction as a Catalyst for Interdisciplinary Co-Creation: Exploring Prompt-Driven Visualization in Design Education

Abstract

This study investigates the role of generative artificial intelligence as a mediating tool in interdisciplinary design education, focusing on its impact on design communication and collaborative participation. The research was conducted within a university-level interdisciplinary design course involving both design and non-design students working on product design tasks. Text-based AI tools were introduced for prompt refinement, while image-generation tools were used to support early-stage visual ideation.A qualitative research approach was adopted, including classroom observation, analysis of student design artifacts, and semi-structured interviews. The results indicate that generative AI is most effective during the conceptual ideation phase, where AI-generated images function as shared visual references that facilitate discussion, negotiation, and collective decision-making. In particular, AI-supported visualization lowered participation barriers for non-design students by enabling visual articulation of ideas without reliance on traditional drawing skills. Design students assumed integrative roles, focusing on interpretation, refinement, and the translation of AI-generated concepts into feasible design outcomes.The study further identifies prompt authoring as a critical human–AI interaction layer, emphasizing its role in design reasoning and communication. While limitations remain in later-stage design refinement, generative AI demonstrates clear pedagogical value in supporting interdisciplinary collaboration.

Keywords: Human–AI Interaction, Prompt Engineering, Interdisciplinary Collaboration

DOI: 10.54941/ahfe1007499