Do MLLMs Capture How Interfaces Guide User Behavior? A Benchmark for Multimodal UI/UX Design Understanding
arXiv:2505.05026v5 Announce Type: replace Abstract: User interface (UI) design goes beyond visuals to shape user experience (UX), underscoring the shift toward UI/UX as a unified concept. While recent studies have explored UI evaluation using Multimodal Large Language Models (MLLMs), they largely focus on surface-level features, overlooking how design choices influence user behavior at scale. To fill this gap, we introduce WiserUI-Bench, a novel benchmark for multimodal understanding of how UI/UX design affects user behavior, built on 300 real-world UI image pairs from industry A/B tests, with
The proliferation of advanced MLLMs and the increasing complexity of digital interfaces necessitate better evaluation methods for UI/UX, pushing for more sophisticated benchmarks.
This development allows for a more nuanced understanding of how AI can not only design but also interpret the behavioral impact of UI/UX, moving beyond superficial aesthetic evaluation.
The ability of MLLMs to assess user behavior from UI/UX designs marks a shift towards more intelligent and data-driven interface development, integrating behavioral insights directly into AI design processes.
- · AI/ML researchers
- · Software companies
- · Product designers
- · Digital advertisers
- · Traditional UI/UX testing firms
- · Manual A/B testing processes
- · Inefficient design methodologies
MLLMs will become more capable of predicting user interactions and optimizing interface designs for specific behavioral outcomes.
This capability leads to a new generation of AI-driven design tools that autonomously iterate and improve user interfaces based on simulated or real-world behavioral data.
The enhanced efficiency in UI/UX design could accelerate the development and adoption of new digital products and services, further blurring the lines between human and AI design.
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