Are We On Track? AI-Assisted Active and Passive Goal Reflection During Meetings
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910906234961920 |
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| author | Chen, Xinyue Tankelevitch, Lev Vanukuru, Rishi Scott, Ava Elizabeth Panda, Payod Rintel, Sean |
| author_facet | Chen, Xinyue Tankelevitch, Lev Vanukuru, Rishi Scott, Ava Elizabeth Panda, Payod Rintel, Sean |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01082 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Are We On Track? AI-Assisted Active and Passive Goal Reflection During Meetings Chen, Xinyue Tankelevitch, Lev Vanukuru, Rishi Scott, Ava Elizabeth Panda, Payod Rintel, Sean Human-Computer Interaction 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. |
| title | Are We On Track? AI-Assisted Active and Passive Goal Reflection During Meetings |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.01082 |