Do MLLMs Capture How Interfaces Guide User Behavior? A Benchmark for Multimodal UI/UX Design Understanding

TL;DR

This paper introduces WiserUI-Bench, a benchmark to evaluate how well Multimodal Large Language Models understand the influence of UI/UX design on user behavior, revealing current model limitations.

Contribution

The paper presents WiserUI-Bench, a new benchmark with real-world UI data to assess MLLMs' understanding of UI/UX impact on user actions and explanations.

Findings

MLLMs show limited understanding of UI/UX influence on behavior.

Models struggle to predict the more effective UI in A/B tests.

Expert-curated interpretations aid post-hoc explanations.

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 empirically validated winners that induced more user actions. For future design progress in practice, post-hoc understanding of why such winners succeed with mass users is also required; we support this via expert-curated key interpretations for each instance. Experiments across multiple MLLMs on WiserUI-Bench for two main tasks, (1) predicting the more effective UI image between an A/B-tested pair, and (2) explaining it post-hoc in alignment with expert interpretations, show that models exhibit limited understanding of the behavioral impact of UI/UX design. We believe our work will foster research on leveraging MLLMs for visual design in user behavior contexts.