CausalMapper: Challenging designers to think in systems with Causal Maps and Large Language Model
Ziheng Huang, K. Quan, Joel Chan, Stephen MacNeil
Abstract
Professional designers often construct and explore conceptual representations (e.g.: design spaces) to help them reason about complex design situations and consider potential design pitfalls. However, it is often challenging, even for professional designers, to exhaustively consider the many pitfalls that might result from design activity. We present CausalMapper, a mixed-initiative system, that leverages a large language model (LLM) and a causal map representation to teach design students how to reason about the relationships between problems and solutions. Where creativity support tools often focus on ideating creative solutions, our mixed-initiative approach focuses on ideating ecosystems of solutions that holistically address a set of related problems. By leveraging the generative creativity of LLMs, designers are inspired to consider solutions and potential consequences that emerge when solutions are adopted. At the same time, leveraging the designers’ domain knowledge to account for and correct the biases inherent in LLMs. Through a case study, we demonstrate the functionality of this mixed-initiative system. The goal of this demo is to present a creativity support tool that is intended to teach design students to think more systematically by generating ideas that challenge their thinking rather just augmenting their creative potential.