# UXAgent: A System for Simulating Usability Testing of Web Design with LLM Agents

arXiv: https://arxiv.org/abs/2504.09407 · HTML: https://mlsanity.com/paper/2504.09407/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in GUI and mobile agents, one of 16 directions in LLM agents and tool use.
- Builds on 25 of its 96 references that are in the map.
- First posted on arXiv 13 Apr 2025.

## Placed in

- LLM agents and tool use / [GUI and mobile agents](https://mlsanity.com/agents/gui-and-mobile-agents/)

## Builds on (most cited first, 25 of 25)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022)
- [A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis](https://mlsanity.com/paper/2307.12856/) (2023)
- [LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals](https://mlsanity.com/paper/2411.10109/) (2024)
- [AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments](https://mlsanity.com/paper/2405.07960/) (2024)
- [PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action](https://mlsanity.com/paper/2409.00138/) (2024)
- [AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents](https://mlsanity.com/paper/2410.13825/) (2024)
- [LASER: LLM Agent with State-Space Exploration for Web Navigation](https://mlsanity.com/paper/2309.08172/) (2023)
- [Agents Thinking Fast and Slow: A Talker-Reasoner Architecture](https://mlsanity.com/paper/2410.08328/) (2024)
- [CollabCoder: A Lower-barrier, Rigorous Workflow for Inductive Collaborative Qualitative Analysis with Large Language Models](https://mlsanity.com/paper/2304.07366/) (2023)
- [AXNav: Replaying Accessibility Tests from Natural Language](https://mlsanity.com/paper/2310.02424/) (2023)
- ['Simulacrum of Stories': Examining Large Language Models as Qualitative Research Participants](https://mlsanity.com/paper/2409.19430/) (2024)
- [Can LLM Agents Simulate Multi-Turn Human Behavior? Evidence from Real Online Customer Behavior Data](https://mlsanity.com/paper/2503.20749/) (2025)
- [Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents](https://mlsanity.com/paper/2502.13012/) (2025)
- [An Empathy-Based Sandbox Approach to Bridge the Privacy Gap among Attitudes, Goals, Knowledge, and Behaviors](https://mlsanity.com/paper/2309.14510/) (2023)
- [PersonaFlow: Designing LLM-Simulated Expert Perspectives for Enhanced Research Ideation](https://mlsanity.com/paper/2409.12538/) (2024)
- [Privacy Leakage Overshadowed by Views of AI: A Study on Human Oversight of Privacy in Language Model Agent](https://mlsanity.com/paper/2411.01344/) (2024)
- [Collaboration with Conversational AI Assistants for UX Evaluation: Questions and How to Ask them (Voice vs. Text)](https://mlsanity.com/paper/2303.03638/) (2023)
- [The State of Pilot Study Reporting in Crowdsourcing: A Reflection on Best Practices and Guidelines](https://mlsanity.com/paper/2312.08090/) (2023)
- [Hardhats and Bungaloos: Comparing Crowdsourced Design Feedback with Peer Design Feedback in the Classroom](https://mlsanity.com/paper/2101.09161/) (2021)
- ["Merging Results Is No Easy Task": An International Survey Study of Collaborative Data Analysis Practices Among UX Practitioners](https://mlsanity.com/paper/2204.02823/) (2022)
- [ScreenAudit: Detecting Screen Reader Accessibility Errors in Mobile Apps Using Large Language Models](https://mlsanity.com/paper/2504.02110/) (2025)

## Built on by (most cited first, 10 of 10)

- [Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation](https://mlsanity.com/paper/2507.21028/) (2025)
- [Shop-R1: Rewarding LLMs to Simulate Human Behavior in Online Shopping via Reinforcement Learning](https://mlsanity.com/paper/2507.17842/) (2025)
- [DPRF: A Generalizable Dynamic Persona Refinement Framework for Optimizing Behavior Alignment Between Personalized LLM Role-Playing Agents and Humans](https://mlsanity.com/paper/2510.14205/) (2025)
- [See, Think, Act: Online Shopper Behavior Simulation with VLM Agents](https://mlsanity.com/paper/2510.19245/) (2025)
- [Do MLLMs Capture How Interfaces Guide User Behavior? A Benchmark for Multimodal UI/UX Design Understanding](https://mlsanity.com/paper/2505.05026/) (2025)
- [PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation](https://mlsanity.com/paper/2606.05697/) (2026)
- [UXBench: Measuring the Actionability of LLM-Generated UX Critiques](https://mlsanity.com/paper/2606.16262/) (2026)
- [Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs](https://mlsanity.com/paper/2602.06197/) (2026)
- [Logarithmic Scores, Power-Law Discoveries: Disentangling Measurement from Coverage in Agent-Based Evaluation](https://mlsanity.com/paper/2604.00477/) (2026)
- [Automatic multimodal UX improvement recommendations from LLM agent user simulations](https://mlsanity.com/paper/2609.22971/) (2026)

## Abstract (arXiv)

Usability testing is a fundamental research method that user experience (UX) researchers use to evaluate and iterate their new designs. But what about evaluating and iterating the usability testing study design itself? Recent advances in Large Language Model-simulated Agent (LLM Agent) research inspired us to design UXAgent to support UX researchers in evaluating and iterating their study design before they conduct the real human-subject study. Our system features a Persona Generator module, an LLM Agent module, and a Universal Browser Connector module to automatically generate thousands of simulated users and to interactively test the target website. The system also provides a Result Viewer Interface so that the UX researchers can easily review and analyze the generated qualitative (e.g., agents' post-study surveys) and quantitative data (e.g., agents' interaction logs), or even interview agents directly. Through a heuristic evaluation with 16 UX researchers, participants praised the innovation of our system but also expressed concerns about the future of LLM Agent usage in UX studies.