This study reviews the role of Generative AI in advancing mobile UI/UX design from 2020 to 2025. Following the PRISMA protocol, we screened 1,005 articles from reputable academic sources, selecting 55 studies through a rigorous four-stage process (identification, screening, eligibility, and inclusion). Our analysis shows a 63% annual publication growth until 2023, stabilizing at a high level, with ACM and IEEE as leading publishers. Keyword analysis revealed a shift from transformers, GANs, and layout generation in 2021–2022 to diffusion models, large language models, and AI-assisted prototyping by 2023–2025. We classified methods into six categories: generative modeling, LLM-based approaches, prototyping support, benchmark dataset creation, multimodal integration, and user-experience evaluation. Eight key challenges were identified, with image quality, layout consistency, and data limitations being the most prominent. We propose eight future research directions, including hybrid VAE–GAN–diffusion architectures, context-aware personalization, and multidimensional UX evaluation frameworks. This study provides a comprehensive overview and actionable roadmap for advancing generative AI in mobile UI/UX design, promoting innovative and effective solutions.

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A Systematic Review of Generative AI Applications in UI/UX Design for Mobile Applications

  • Ngoc-Phuong Doan,
  • The-Vinh Nguyen,
  • Thi-Thuong Pham,
  • Thi Thu-Hang Nguyen,
  • Xuan-Kien Pham

摘要

This study reviews the role of Generative AI in advancing mobile UI/UX design from 2020 to 2025. Following the PRISMA protocol, we screened 1,005 articles from reputable academic sources, selecting 55 studies through a rigorous four-stage process (identification, screening, eligibility, and inclusion). Our analysis shows a 63% annual publication growth until 2023, stabilizing at a high level, with ACM and IEEE as leading publishers. Keyword analysis revealed a shift from transformers, GANs, and layout generation in 2021–2022 to diffusion models, large language models, and AI-assisted prototyping by 2023–2025. We classified methods into six categories: generative modeling, LLM-based approaches, prototyping support, benchmark dataset creation, multimodal integration, and user-experience evaluation. Eight key challenges were identified, with image quality, layout consistency, and data limitations being the most prominent. We propose eight future research directions, including hybrid VAE–GAN–diffusion architectures, context-aware personalization, and multidimensional UX evaluation frameworks. This study provides a comprehensive overview and actionable roadmap for advancing generative AI in mobile UI/UX design, promoting innovative and effective solutions.