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Computer Science > Artificial Intelligence

arXiv:2607.22928 (cs)
[Submitted on 24 Jul 2026 (v1), last revised 31 Jul 2026 (this version, v2)]

Title:Design Theater: Evaluating the Gap Between User-Facing Design Reasoning and Implementation in Generative UI Tools

Authors:Kashif Imteyaz, Kaif Imteyaz, Nakul Rajpal, Kaif Shaikh, Michael Muller, Saiph Savage
View a PDF of the paper titled Design Theater: Evaluating the Gap Between User-Facing Design Reasoning and Implementation in Generative UI Tools, by Kashif Imteyaz and 5 other authors
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Abstract:Generative UI tools promise to democratize UI design by turning natural language descriptions into complete interfaces. Alongside the interface, these tools generate user-facing design rationales that explain their layout, accessibility, and design choices. However, it remains unclear whether these stated rationales are actually reflected in the interfaces they produce. We call this disconnect ``Design Theater'': plausible and confident design rationales that have little relationship to the actual implementation. To study this phenomenon, we introduce a benchmark and three metrics for measuring Design Theater. The benchmark includes 24 UI generation tasks spanning structural, styling, and functional design requirements. Using this benchmark, we evaluate 120 interfaces created by five generative UI tools. On average, over 25\% of user-facing design rationales are not implemented in the generated interface, and the implementation failure increases to 34\% for functional requirements. Tools recognize roughly half of the UX principles embedded in prompts (mean = 0.54), with four of five tools implementing 6\% or fewer functional principles. We also measure interface similarity across tools and find convergence in visual appearance and layout organization, with greater variation in color choices. Overall, we contribute: 1) the concept of Design Theater; 2) a benchmark with metrics for assessing whether the stated reasoning of generative UI tools is reflected in their implementations; 3) and findings from a systematic evaluation of these tools. We discuss what these findings mean for the design and evaluation of generative UI tools.
Comments: Accepted at AAAI/AIES
Subjects: Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.22928 [cs.AI]
  (or arXiv:2607.22928v2 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2607.22928
arXiv-issued DOI via DataCite

Submission history

From: Kashif Imteyaz [view email]
[v1] Fri, 24 Jul 2026 21:57:09 UTC (598 KB)
[v2] Fri, 31 Jul 2026 22:01:55 UTC (586 KB)
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