Field studies are irreplaceable but costly, time-consuming, and error-prone, which need careful preparation. Inspired by rapid-prototyping in manufacturing, we propose a fast, low-cost evaluation method using Vision-Language Model (VLM) personas to simulate outcomes comparable to field results. While LLMs show human-like reasoning and language capabilities, autonomous vehicle (AV)-pedestrian interaction requires spatial awareness, emotional empathy, and behavioral generation. This raises our research question: To what extent can VLM personas mimic human responses in field studies? We conducted parallel studies: 1) one real-world study with 20 participants, and 2) one video-study using 20 VLM personas, both on a street-crossing task. We compared their responses and interviewed five HCI researchers on potential applications. Results show that VLM personas mimic human response patterns (e.g., average crossing times of 5.25 s vs. 5.07 s) lack the behavioral variability and depth. They show promise for formative studies, field study preparation, and human data augmentation.
@article{arxiv.2602.16157,
title = {Peeking Ahead of the Field Study: Exploring VLM Personas as Support Tools for Embodied Studies in HCI},
author = {Xinyue Gui and Ding Xia and Mark Colley and Yuan Li and Vishal Chauhan and Anubhav Anubhav and Zhongyi Zhou and Ehsan Javanmardi and Stela Hanbyeol Seo and Chia-Ming Chang and Manabu Tsukada and Takeo Igarashi},
journal= {arXiv preprint arXiv:2602.16157},
year = {2026}
}