User-centered evaluation of visual generative AI for city design: an exploratory technology acceptance model analysis
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s10111-025-00816-7.pdf
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2.5 MB
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Checksum (MD5)
ec092fc379be72e1a042252ed46ae2d1
Author(s)
Haddad, Fadi Ghassan
Jang, Kee Moon
Duarte, Fábio
Rajaonson, Juste
Ratti, Carlo
Date Issued
July 13, 2025
Journal
Cognition, Technology & Work
Publisher
Springer Science and Business Media LLC
Citation
Haddad, F.G., Jang, K.M., Duarte, F. et al. User-centered evaluation of visual generative AI for city design: an exploratory technology acceptance model analysis. Cogn Tech Work (2025).
Version
Final published version
Abstract
This study explores the potential of visual generative artificial intelligence (visual GenAI) in augmenting city design workflows. Using customized DALL-E 3 interfaces, we facilitated engagement sessions with members of an academic planning community to assess their perceptions of AI-generated imagery before and after its use, with a focus on main street revitalization (n = 24 qualitative, n = 17 quantitative). Drawing on the Technology Acceptance Model, we assessed cognitive, operational, and participatory dimensions influencing user attitudes toward AI-assisted urban design. Perceived usefulness in cognitive and participatory tasks emerged as the strongest predictors of attitudes toward visual GenAI use, explaining up to 71% and 44% of the variance, respectively. While participants valued the ability to generate visuals and stimulate dialogue rapidly, challenges with prompt precision, output predictability, and interface usability limited broader accessibility. User expertise moderated perceptions, with higher proficiency participants generally expressing more positive attitudes toward its use. Our preliminary findings suggest that while visual GenAI may offer new opportunities to augment cognitive and co-design processes, its integration into city design workflows may also depend on diverse training datasets to address biases; human-centered design with clearer affordances and support for non-expert users; and, validation processes that maintain human oversight. This study contributes to the emerging research on human-AI work integration by providing initial empirical evidence on the opportunities and constraints of visual GenAI tools in city design contexts, while establishing a foundation for future research.
MIT Department
Senseable City Laboratory
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Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10111-025-00816-7
