# The role of large language models in UI/UX design: A systematic literature review

arXiv: https://arxiv.org/abs/2507.04469 · HTML: https://mlsanity.com/paper/2507.04469/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 84 references that are in the map.
- First posted on arXiv 6 Jul 2025.

## Builds on (most cited first, 21 of 21)

- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT](https://mlsanity.com/paper/2302.09419/) (2023)
- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [Multi-Step Reasoning with Large Language Models, a Survey](https://mlsanity.com/paper/2407.11511/) (2024)
- [pix2code: Generating Code from a Graphical User Interface Screenshot](https://mlsanity.com/paper/1705.07962/) (2017)
- [ChartGPT: Leveraging LLMs to Generate Charts from Abstract Natural Language](https://mlsanity.com/paper/2311.01920/) (2023)
- [VINS: Visual Search for Mobile User Interface Design](https://mlsanity.com/paper/2102.05216/) (2021)
- [Machine Learning-Based Prototyping of Graphical User Interfaces for Mobile Apps](https://mlsanity.com/paper/1802.02312/) (2018)
- [VisionTasker: Mobile Task Automation Using Vision Based UI Understanding and LLM Task Planning](https://mlsanity.com/paper/2312.11190/) (2023)
- [Farsight: Fostering Responsible AI Awareness During AI Application Prototyping](https://mlsanity.com/paper/2402.15350/) (2024)
- [UICrit: Enhancing Automated Design Evaluation with a UICritique Dataset](https://mlsanity.com/paper/2407.08850/) (2024)
- [Modeling Mobile Interface Tappability Using Crowdsourcing and Deep Learning](https://mlsanity.com/paper/1902.11247/) (2019)
- [PromptInfuser: How Tightly Coupling AI and UI Design Impacts Designers' Workflows](https://mlsanity.com/paper/2310.15435/) (2023)
- [AI Assistance for UX: A Literature Review Through Human-Centered AI](https://mlsanity.com/paper/2402.06089/) (2024)
- [Optimizing User Interface Layouts via Gradient Descent](https://mlsanity.com/paper/2002.10702/) (2020)
- [Interlinking User Stories and GUI Prototyping: A Semi-Automatic LLM-based Approach](https://mlsanity.com/paper/2406.08120/) (2024)
- [On AI-Inspired UI-Design](https://mlsanity.com/paper/2406.13631/) (2024)
- [BlackBox Toolkit: Intelligent Assistance to UI Design](https://mlsanity.com/paper/2004.01949/) (2020)
- [In Situ AI Prototyping: Infusing Multimodal Prompts into Mobile Settings with MobileMaker](https://mlsanity.com/paper/2405.03806/) (2024)
- [The Influence of UX Design on User Retention and Conversion Rates in Mobile Apps](https://mlsanity.com/paper/2501.13407/) (2025)

## Built on by (most cited first, 2 of 2)

- [Reasoning for Mobile User Experience with Multimodal LLMs: Task, Benchmark, and Approach](https://mlsanity.com/paper/2606.13192/) (2026)
- [Progressive Ideation using an Agentic AI Framework for Human-AI Co-Creation](https://mlsanity.com/paper/2601.00475/) (2026)

## Abstract (arXiv)

This systematic literature review examines the role of large language models (LLMs) in UI/UX design, synthesizing findings from 38 peer-reviewed studies published between 2022 and 2025. We identify key LLMs in use, including GPT-4, Gemini, and PaLM, and map their integration across the design lifecycle, from ideation to evaluation. Common practices include prompt engineering, human-in-the-loop workflows, and multimodal input. While LLMs are reshaping design processes, challenges such as hallucination, prompt instability, and limited explainability persist. Our findings highlight LLMs as emerging collaborators in design, and we propose directions for the ethical, inclusive, and effective integration of these technologies.