Generating TRIZ-inspired guidelines for eco-design using Generative Artificial Intelligence

Abstract

Environmental considerations are emerging as stimuli for innovation during the eco-design ideation process. Integrating TRIZ (Teoriya Resheniya Izobretatelskikh Zadatch─Theory of Inventive Problem Solving) methodology into eco-design offers a structured problem-solving approach to address sustainability challenges. However, developing innovative designs requires expertise in TRIZ concepts and access to resources, which makes it a time-consuming process and can limit its application for eco-design innovation quickly. This study leverages the analytical and generative capabilities of large language models (LLMs) to enhance the TRIZ methodology and automate the ideation process in eco-design. An intelligent tool, “Eco-innovate Assistant,” is designed to provide users with eco-innovative solutions with design sketches. Its effectiveness is validated and evaluated through comparative studies. The findings demonstrate the potential of LLMs in automating design processes, catalyzing a transformation in AI-driven innovation and ideation in eco-design.