Are We On Track? AI-Assisted Active and Passive Goal Reflection During Meetings

要旨

Meetings often suffer from a lack of intentionality, such as unclear goals and straying off-topic. Identifying goals and maintaining their clarity throughout a meeting is challenging, as discussions and uncertainties evolve. Yet meeting technologies predominantly fail to support meeting intentionality. AI-assisted reflection is a promising approach. To explore this, we conducted a technology probe study with 15 knowledge workers, integrating their real meeting data into two AI-assisted reflection probes: a passive and active design. Participants identified goal clarification as a foundational aspect of reflection. Goal clarity enabled people to assess when their meetings were off-track and reprioritize accordingly. Passive AI intervention helped participants maintain focus through non-intrusive feedback, while active AI intervention, though effective at triggering immediate reflection and action, risked disrupting the conversation flow. We identify three key design dimensions for AI-assisted reflection systems, and provide insights into design trade-offs, emphasizing the need to adapt intervention intensity and timing, balance democratic input with efficiency, and offer user control to foster intentional, goal-oriented behavior during meetings and beyond.

著者
Xinyue Chen
University of Michigan, Ann Arbor, Michigan, United States
Lev Tankelevitch
Microsoft Research, Cambridge, United Kingdom
Rishi Vanukuru
University of Colorado Boulder, Boulder, Colorado, United States
Ava Elizabeth. Scott
UCL, London, London, United Kingdom
Payod Panda
Microsoft Research, Cambridge, United Kingdom
Sean Rintel
Microsoft Research, Cambridge, United Kingdom
DOI

10.1145/3706598.3714052

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714052

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Meeting and Collaboration

G418+G419
6 件の発表
2025-04-29 18:00:00
2025-04-29 19:30:00
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