Beschreibung
This paper explores the application of generative AI for systematic innovation and inventive engineering problem solving. Using an automated prompt generation approach, including iterative problem definition and multi-directional promting with numerous elementary solution principles, the study investigates the ability of AI chatbots to autonomously generate solution ideas and create and evaluate innovative concepts based on one or more partial solution ideas. Through several experiments with different Large Language Models, the results show that while generative AI can quickly generate a large number of ideas, it often overestimates the feasibility and usefulness of its solutions and tends to generate overly complex concepts. The varying performance of different AI tools throughout the innovation process provides an opportunity to form mixed AI innovation teams, where different generative chatbots can complement, monitor and correct each other as needed. Using case studies to illustrate different strategies for generating solution concepts, this paper attempts to determine the optimal level of human involvement in the AI-assisted innovation process. The currently observed discrepancy between AI-generated textual descriptions and the practical implementation of engineering solutions underscores a fundamental challenge in the current capabilities of generative AI.
