Researchers at Carnegie Mellon University in Pittsburgh are working on ways to incorporate different cognitive styles into large language models (LLMs) in order to create AI ‘teammates’ that better reflect human designers.
A work environment that supports diverse problem solvers is a non-negotiable element for successful design teams. Chris McComb, an expert on human-AI teaming, and his team of researchers are bridging the cognitive gaps between members of a team by incorporating cognitive styles into large language models, empowering teams to more easily harness individuals’ unique strengths.
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‘Broadly speaking, we are so caught up with humanoid robots because so much of our world is built for human-shaped things,’ said McComb, an associate professor of mechanical engineering. ‘So, when we think about what AI should look like for designers, it needs to be designer-shaped, which means that it needs to be reflective of different problem-solving styles.’
Based on the cognitive continuum introduced by Kirton’s Adaption-Innovation theory, McComb’s team prompted an off-the-shelf large language model to emulate two cognitive styles – adaptive and innovative – while generating solutions to design problems. More adaptive thinkers prefer to solve problems with a highly structured process, whereas more innovative thinkers prefer a more flexible structure to solve problems with ground-breaking ideas.
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Vasvi Agarwal explained that the team used what’s known as a zero shot prompting method to get design solutions, demonstrating that the model can adopt a cognitive style with little guidance. Acting both as a more adaptive thinker and a more innovative thinker, it produced designs for a lidded food container that could be opened using only one hand, a lightweight, portable exercise machine that could be used while travelling, and a way to secure people’s belongings in public.
The researchers found that designs produced under the adaptive prompt were more feasible –just as seen in human designers. Likewise, designs produced under the innovative prompt were more paradigm-breaking – again emulating human characteristics. Agarawl believes that although some of the innovative designs were ‘out of the box’, the LLMs can be fine-tuned for better results.
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‘The main purpose of this study was to advance human–AI teaming,’ she said. ‘By using AI on design teams, we can decrease workload and generate more innovative solutions.’
‘The world is so exciting when it comes to AI right now. We’ve reached a point where it’s much easier to build systems and test how designers interact with them,’ said McComb. ‘This work is indicative of a paradigm of research that is rapid and iterative and engaged with users. We’re pushing forward not just language models for design but a new paradigm of design research.’
The research has been published in ASME Journal of Computing and Information Science in Engineering.
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