Abstract
In human-centered design (HCD) projects, designers select and use a variety of design methods in pursuit of a desired outcome. Given the prominence of method selection in designer behavior, what distinguishes a design team’s method selections from design method selection based on frequency or probability? To explore this question, we compare HCD methods suggested by the publicly-available large-language model, GPT-3.5, to 402 novice design team method selections over five offerings of a design projectbased learning course at a large public university. We observe that GPT-3.5 appears to represent design method knowledge held in method repositories like theDesignExchange well. We also observe that GPT-3.5’s method selection recommendations appear to poorly distinguish between HCD phases, and appear limited to highly specific aspects of HCD phases. These findings highlight the unique contribution of human design cognition in design decision-making relative to LLM’s, and herald the promise of human-AI teaming in design method selection.
Keywords
design theory and methodology; human-ai collaboration; human-centered design; design methods
DOI
https://doi.org/10.21606/drs.2024.956
Citation
Rao, V., Zhu, Y., Yang, T., Kim, E., Agogino, A., and Goucher-Lambert, K. (2024) Exploring human-centered design method selection strategies with large language models, in Gray, C., Ciliotta Chehade, E., Hekkert, P., Forlano, L., Ciuccarelli, P., Lloyd, P. (eds.), DRS2024: Boston, 23–28 June, Boston, USA. https://doi.org/10.21606/drs.2024.956
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Conference Track
Research Paper
Included in
Exploring human-centered design method selection strategies with large language models
In human-centered design (HCD) projects, designers select and use a variety of design methods in pursuit of a desired outcome. Given the prominence of method selection in designer behavior, what distinguishes a design team’s method selections from design method selection based on frequency or probability? To explore this question, we compare HCD methods suggested by the publicly-available large-language model, GPT-3.5, to 402 novice design team method selections over five offerings of a design projectbased learning course at a large public university. We observe that GPT-3.5 appears to represent design method knowledge held in method repositories like theDesignExchange well. We also observe that GPT-3.5’s method selection recommendations appear to poorly distinguish between HCD phases, and appear limited to highly specific aspects of HCD phases. These findings highlight the unique contribution of human design cognition in design decision-making relative to LLM’s, and herald the promise of human-AI teaming in design method selection.