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.

翻译：会议常常因缺乏意图性而受到影响，例如目标不明确和偏离主题。由于讨论和不确定性不断演变，识别目标并在整个会议期间保持其清晰度具有挑战性。然而，会议技术大多未能支持会议的意图性。AI辅助反思是一种有前景的方法。为了探索这一点，我们对15名知识工作者进行了一项技术探针研究，将他们的真实会议数据整合到两个AI辅助反思探针中：一个被动设计和一个主动设计。参与者将目标澄清确定为反思的一个基本方面。目标清晰度使人们能够评估会议何时偏离正轨，并相应地重新确定优先级。被动的AI干预通过非侵入性反馈帮助参与者保持专注，而主动的AI干预虽然在触发即时反思和行动方面有效，但有可能破坏对话的流畅性。我们确定了AI辅助反思系统的三个关键设计维度，并提供了对设计权衡的见解，强调需要调整干预的强度和时机，平衡民主输入与效率，并提供用户控制，以在会议期间及之后培养有意图的、以目标为导向的行为。