Facilitating Longitudinal Interaction Studies of AI Systems

Tao Long; Sitong Wang; Émilie Fabre; Tony Wang; Anup Sathya; Jason Wu; Savvas Petridis; Dingzeyu Li; Tuhin Chakrabarty; Yue Jiang; Jingyi Li; Tiffany Tseng; Ken Nakagaki; Qian Yang; Nikolas Martelaro; Jeffrey V. Nickerson; Lydia B. Chilton

arXiv · 2508.10252

Facilitating Longitudinal Interaction Studies of AI Systems

Tao Long·Sitong Wang·Émilie Fabre·Tony Wang·Anup Sathya·Jason Wu·Savvas Petridis·Dingzeyu Li·Tuhin Chakrabarty·Yue Jiang·Jingyi Li·Tiffany Tseng·Ken Nakagaki·Qian Yang·Nikolas Martelaro·Jeffrey V. Nickerson·Lydia B. Chilton

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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.

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Tao Long, Sitong Wang, Émilie Fabre, Tony Wang, Anup Sathya, Jason Wu, Savvas Petridis, Dingzeyu Li, Tuhin Chakrabarty, Yue Jiang, Jingyi Li, Tiffany Tseng, Ken Nakagaki, Qian Yang, Nikolas Martelaro, Jeffrey V. Nickerson, Lydia B. Chilton. 2025-08-14. Facilitating Longitudinal Interaction Studies of AI Systems. https://arxiv.org/abs/2508.10252

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