# Qualitative Evaluation of LLM-Designed GUI

arXiv: https://arxiv.org/abs/2601.22759 · HTML: https://mlsanity.com/paper/2601.22759/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 3 of 3)

- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [Low-code LLM: Graphical User Interface over Large Language Models](https://mlsanity.com/paper/2304.08103/) (2023)
- [TextLap: Customizing Language Models for Text-to-Layout Planning](https://mlsanity.com/paper/2410.12844/) (2024)

## Abstract (arXiv)

As generative artificial intelligence advances, Large Language Models (LLMs) are being explored for automated graphical user interface (GUI) design. This study investigates the usability and adaptability of LLM-generated interfaces by analysing their ability to meet diverse user needs. The experiments included utilization of three state-of-the-art models from January 2025 (OpenAI GPT o3-mini-high, DeepSeek R1, and Anthropic Claude 3.5 Sonnet) generating mockups for three interface types: a chat system, a technical team panel, and a manager dashboard. Expert evaluations revealed that while LLMs are effective at creating structured layouts, they face challenges in meeting accessibility standards and providing interactive functionality. Further testing showed that LLMs could partially tailor interfaces for different user personas but lacked deeper contextual understanding. The results suggest that while LLMs are promising tools for early-stage UI prototyping, human intervention remains critical to ensure usability, accessibility, and user satisfaction.