Design Creativity Bench: Measuring creativity in LLM-Generated UI

Aman Rusia, Abhijit Bhole, Prashank Gupta, Dipanjan Dey
10/8/2026
cs.HCcs.AI

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Abstract

As leading LLMs improve on capability evaluations, their limitations in producing creative outputs on design tasks remain insufficiently characterised. Our work introduces Design Creativity Bench, a benchmark that evaluates diversity and appropriateness in UI designs. It measures distinctiveness among models on the same prompt (originality), how much a model's designs change between two prompts for the same UI goal in different product domains (creative range), and the share of a brief's acceptance criteria each design meets (appropriateness). Originality is 0.592 for same-prompt design pairs from different models (95% CI [0.582, 0.602]), far below the 0.764 for same-prompt human-model pairs (95% CI [0.751, 0.778]). Creative range is 0.581 across models (95% CI [0.567, 0.597]), against 0.902 for human designs (95% CI [0.884, 0.919]). Appropriateness is above 90% for every model, and the best model reaches 99.2%, slightly above the 98.0% for human designs. Our work shows that the default output of LLMs, though generally appropriate, is substantially more repetitive than the human baseline. This calls for strong measures to address the issue.

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Cite this paper

@article{rusia2026design,
  title  = {Design Creativity Bench: Measuring creativity in LLM-Generated UI},
  author = {Aman Rusia and Abhijit Bhole and Prashank Gupta and Dipanjan Dey},
  year   = {2026},
  eprint = {2610.11539},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2610.11539v1}
}

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