Facilitating Longitudinal Interaction Studies of AI Systems
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866911105384710144 |
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| author | Long, Tao Wang, Sitong Fabre, Émilie Wang, Tony Sathya, Anup Wu, Jason Petridis, Savvas Li, Dingzeyu Chakrabarty, Tuhin Jiang, Yue Li, Jingyi Tseng, Tiffany Nakagaki, Ken Yang, Qian Martelaro, Nikolas Nickerson, Jeffrey V. Chilton, Lydia B. |
| author_facet | Long, Tao Wang, Sitong Fabre, Émilie Wang, Tony Sathya, Anup Wu, Jason Petridis, Savvas Li, Dingzeyu Chakrabarty, Tuhin Jiang, Yue Li, Jingyi Tseng, Tiffany Nakagaki, Ken Yang, Qian Martelaro, Nikolas Nickerson, Jeffrey V. Chilton, Lydia B. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10252 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Facilitating Longitudinal Interaction Studies of AI Systems Long, Tao Wang, Sitong Fabre, Émilie Wang, Tony Sathya, Anup Wu, Jason Petridis, Savvas Li, Dingzeyu Chakrabarty, Tuhin Jiang, Yue Li, Jingyi Tseng, Tiffany Nakagaki, Ken Yang, Qian Martelaro, Nikolas Nickerson, Jeffrey V. Chilton, Lydia B. Human-Computer Interaction Artificial Intelligence Computers and Society 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. |
| title | Facilitating Longitudinal Interaction Studies of AI Systems |
| topic | Human-Computer Interaction Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2508.10252 |