提出长期互动研究方法，解决AI工具使用随时间演变的评估难题
Facilitating Longitudinal Interaction Studies of AI Systems
- 设计可落地的长期研究框架与工具原型
- 通过多轮交互数据捕捉用户对AI系统的适应与再利用过程
- 适合关注人机长期互动的UIST研究者和系统设计者
UIST研究人员开发了应对用户挑战的工具，但用户与AI的交互会随学习、适应和重新用途而动态演变，一次性的评估已不足以反映真实使用情况。捕捉这些演化过程需要长期研究，但部署、评估设计和数据收集方面的挑战使得此类纵向研究难以实施。本研讨会旨在解决这些问题，为研究者提供实用策略。活动包括主题报告、圆桌讨论以及互动分组讨论与协议设计、工具原型制作环节。我们希望推动建立长期系统研究社区，促进其成为设计、构建和评估UIST工具更广泛接受的方法。
原文摘要 · Abstract (English)
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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