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On AI-Inspired UI-Design

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arxiv 2406.13631 v2 pith:ELFCXNTU submitted 2024-06-19 cs.HC cs.AIcs.SE

classification cs.HCcs.AIcs.SE
keywords designersmodelai-inspiredapproachesdiscussgeneratelargeadjust
verification ladder T0 review T1 audit T2 compute T3 formal
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Graphical User Interface (or simply UI) is a primary mean of interaction between users and their devices. In this paper, we discuss three complementary Artificial Intelligence (AI) approaches for triggering the creativity of app designers and inspiring them create better and more diverse UI designs. First, designers can prompt a Large Language Model (LLM) to directly generate and adjust UIs. Second, a Vision-Language Model (VLM) enables designers to effectively search a large screenshot dataset, e.g. from apps published in app stores. Third, a Diffusion Model (DM) can be trained to specifically generate UIs as inspirational images. We present an AI-inspired design process and discuss the implications and limitations of the approaches.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art Text-to-Image Models

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A new evaluation suite finds that CLIPScore, HPSv2, and Aesthetic Score misjudge challenging text-to-image outputs, while GPT-4o and human ratings favor FLUX.1 and Ideogram2.0.

  2. How Do Programming Students Use Generative AI?

    cs.HC 2025-01 conditional novelty 6.0 of 10

    A controlled study of 37 programming students finds that most who used a chatbot requested full code solutions and repeatedly relayed error messages back to it, evidence for over-reliance on generative AI in learning.

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