arXiv:2606.05697v1 Announce Type: new Abstract: User interface (UI) and user experience (UX) evaluation is central to product development, yet reliable feedback still relies on recruiting human participants or running online A/B tests, making early-stage iteration slow and costly. In light of this, recent work has explored Multimodal Large Language Models as proxy evaluators. However, existing approaches either produce surface-level critiques or a judgment that reflects the model's own biases rather than the genuine response of a particular user. We introduce PerceptUI, a framework for persona
The rapid advancement of Large Language Models (LLMs) and their multimodal capabilities makes this an opportune moment for developing sophisticated AI agents that can simulate human behavior in complex tasks like UI/UX evaluation.
This development could significantly accelerate product development cycles and reduce costs by providing immediate, granular, and context-aware feedback, moving beyond superficial or biased automated assessments.
UI/UX evaluation shifts from slow, costly human-centric processes or limited automated methods to advanced AI agents that can act as human-aligned synthetic users, enabling faster and more iterative design.
- · Software Development Companies
- · UI/UX Designers
- · Product Management Teams
- · AI Agent Developers
- · Traditional A/B Testing Providers
- · Manual User Testing Agencies
Product development cycles will become significantly faster and more efficient, reducing time-to-market for new features.
Improved product quality and user satisfaction across various digital platforms as designs are iterated more rapidly and effectively.
The role of human UI/UX researchers may evolve, focusing more on strategic oversight and complex, nuanced qualitative analysis rather than repetitive testing.
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Read at arXiv cs.AI