User-centered evaluation of visual generative AI for city design: an exploratory technology acceptance model analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".