G-FOCUS: Towards a Robust Method for Assessing UI Design Persuasiveness
Authors: name: Jaehyun Jeon, name: Jang Han Yoon, name: Min Soo Kim, name: Sumin Shim, name: Yejin Choi, name: Hanbin Kim, name: Youngjae Yu

Evaluating user interface (UI) design effectiveness extends beyond aesthetics
to influencing user behavior, a principle central to Design Persuasiveness. A/B
testing is the predominant method for determining which UI variations drive
higher user engagement, but it is costly and time-consuming. While recent
Vision-Language Models (VLMs) can process automated UI analysis, current
approaches focus on isolated design attributes rather than comparative
persuasiveness-the key factor in optimizing user interactions. To address this,
we introduce WiserUI-Bench, a benchmark designed for Pairwise UI Design
Persuasiveness Assessment task, featuring 300 real-world UI image pairs labeled
with A/B test results and expert rationales. Additionally, we propose G-FOCUS,
a novel inference-time reasoning strategy that enhances VLM-based
persuasiveness assessment by reducing position bias and improving evaluation
accuracy. Experimental results show that G-FOCUS surpasses existing inference
strategies in consistency and accuracy for pairwise UI evaluation. Through
promoting VLM-driven evaluation of UI persuasiveness, our work offers an
approach to complement A/B testing, propelling progress in scalable UI
preference modeling and design optimization. Code and data will be released
publicly.