A foundation model enhanced approach for generative design in combinational creativity

Abstract

In creativity theory, combining two unrelated concepts into a novel idea is a common means of enhancing creativity. Designers can integrate the Additive concept into the Base concept to inspire and facilitate creative tasks. However, conceiving high-quality combinational ideas poses a challenge that combinational creativity itself demands the consideration of conceptual reasoning and synthesis. We propose an AI foundation model enhanced approach for supporting combinational creativity. This approach derives combinational embodiments, and assists humans in verbalising and externalising combinational ideas. Our experimental study demonstrates that the generated combinational ideas by the approach obtained highest scores compared to those ideas generated without an AI foundation model or combinational strategy. We built a combinational creativity tool called CombinatorX based on this approach to generate ideas. In a study with the comparison of an existing combinational creativity tool and Internet search, we validated that our approach improves the effectiveness of combinational idea generation, enables a reduction in labour force, and facilitates the refinement of combinational ideation.