Abstract
Artificial intelligence (AI) is transforming education, but its current application in the classroom tends to be at the substitution or augmentation level, where creativity or productivity is enhanced without redefining or modifying existing processes. Design education, being more subjective, has fewer constraints in adopting AI, making it ideal for deeper experimentation with AI tools. In this paper, we propose a search methodology using generative-AI at the modification level of the SAMR model. The approach integrates ChatGPT and Midjourney into the creative process and fundamentally redesigns how design students research and ideate. It aims to enable students to generate precise keywords, explore unfamiliar art styles, and produce visual mock-ups more efficiently. The generative-AI search methodology was piloted with 45 year 2 students in a visual communication diploma. Students worked in pairs on five design briefs through the semester. For baseline comparison, Brief 1 was completed without instruction or guidance on AI tools. Subsequently, students were trained in the AI-search methodology and applied it to Briefs 2–5. Content analysis of their works showed an expansion in students’ design vocabulary and a slight improvement in their ability to explain and justify their choices of visual references, but only for students with a higher readiness towards AI. Post-module interviews with lecturers and students highlighted additional opportunities to leverage on AI to further enhance traditional creative workflows and to foster innovation in an AI-driven design landscape. These findings suggest that integrating generative-AI at the modification level can enhance design education by promoting creative exploration.
Presenters
Jeffrey KohLecturer, Media Arts and Design, Singapore Polytechnic, North East, Singapore
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Generative AI, Design education, SAMR model
