Leveraging LLMs to emulate the design processes of different cognitive styles

Cognitive styles, which shape designers’ thinking, problem-solving, and decision-making, influence strategies and preferences in design tasks. In team collaboration, diversity cognitive styles enhance problem-solving efficiency, foster creativity, and improve team performance.The ‘Co-evolution of problem–solution’ model serves as a key theoretical framework for understanding differences in designers’ cognitive styles. Based on this model, designers can be categorized into two cognitive styles: problem-driven and solution-driven. Problem-driven designers prioritize structuring the problem before developing solutions, while solution-driven designers generate solutions when design problems still ill-defined, and then work backward to define the problem. Designers with different expertise and disciplinary backgrounds exhibit distinct cognitive style tendencies. Different cognitive styles also adapt differently to design tasks, excelling in some more than others.As a rapidly advancing technology, large language models (LLMs) have shown considerable potential in the field of design. Their powerful generative capabilities position them as potential collaborators in design teams, emulating different cognitive styles. These emulations aim to bridge cognitive differences among team members, enable designers to leverage their individual strengths, and ultimately produce more feasible and high-quality design solutions.However, previous studies have been limited in leveraging LLMs to directly generate design outcomes based on different cognitive styles, neglecting the emulation of the design process itself. In fact, the evolutionary development between problem and solution spaces better reflects the core differences in cognitive styles. Moreover, communication and collaboration within design teams extend beyond simply exchanging solutions, but span multiple stages of the design process—from problem analysis, idea generation, to evaluation. To better integrate LLMs into design teams, it is necessary to consider the emulation of the design cognition process.To this end, our study, based on the cognitive style taxonomy proposed by Dorst and Cross (2001), explores how LLMs can be used to emulate the design processes of problem-driven and solution-driven designers. We develop a zero-shot chain-of-thought (CoT)-based prompting strategy that enables LLMs to emulate the step-by-step cognitive flow of both design styles. The prompt design is inspired by Jiang et al. (2014) and Chen et al. (2023), who analyzed cognitive differences in conceptual design process using the FBS ontology model. Furthermore, to evaluate the effectiveness of LLMs in emulating cognitive styles, this study establishes a three-dimentional evaluation metrics: static distribution (the proportion and preference of cognitive issues), dynamic transformation (behavioral transition patterns), and the creativity of the design outcomes. Using previous studies identified human design behaviours as a benchmark, we compare the cognitive styles emulated by LLMs under different design constraints against human performance to assess their alignment and differences.The results show that LLM-generated design processes align well with human cognitive styles, effectively emulate static cognitive characteristics. Moreover, enhancing novelty and integrity in solutions and demonstrating superior creativity compared to baseline methods. However, LLMs lack the fully complex nonlinear transitions between problem and solution spaces observed in human designers.This process-based emulation has the potential to enhance the application of LLMs in design teams, enabling them to not only serve as tools for generating solutions but also provide support for collaboration during key stages of the design process. Future research should enhance LLMs' reasoning flexibility through fine-tuning or the GoT approach and explore their impact on human-AI collaboration across diverse design tasks to refine their role in design teams.