Facilitating Longitudinal Interaction Studies of AI Systems
Abstract
UIST researchers develop tools to address user challenges. However, user interactions with AI evolve over time through learning, adaptation, and repurposing, making one time evaluations insufficient. Capturing these dynamics requires longer-term studies, but challenges in deployment, evaluation design, and data collection have made such longitudinal research difficult to implement. Our workshop aims to tackle these challenges and prepare researchers with practical strategies for longitudinal studies. The workshop includes a keynote, panel discussions, and interactive breakout groups for discussion and hands-on protocol design and tool prototyping sessions. We seek to foster a community around longitudinal system research and promote it as a more embraced method for designing, building, and evaluating UIST tools.
Cite
@article{arxiv.2508.10252,
title = {Facilitating Longitudinal Interaction Studies of AI Systems},
author = {Tao Long and Sitong Wang and Émilie Fabre and Tony Wang and Anup Sathya and Jason Wu and Savvas Petridis and Dingzeyu Li and Tuhin Chakrabarty and Yue Jiang and Jingyi Li and Tiffany Tseng and Ken Nakagaki and Qian Yang and Nikolas Martelaro and Jeffrey V. Nickerson and Lydia B. Chilton},
journal= {arXiv preprint arXiv:2508.10252},
year = {2025}
}
Comments
Accepted workshop proposal @ UIST 2025 Busan, Korea. Workshop website: https://longitudinal-workshop.github.io/