ReflecToMeet: An AI-Assisted Reflection Based System to Enhance Collaborative Preparedness

Abstract

In collaborative settings, difficulties in sustaining a consistent pace and engagement often lead to task drift, reducing preparedness and overall effectiveness between meetings. To address this challenge, we conducted a formative study and developed ReflecToMeet, an AI assisted system that integrates theory driven reflective prompts with mechanisms for sharing teammates reflections. Informed by ten formative interviews, the system was evaluated in a mixed method study across three conditions: deeper reflection, regular reflection, and a control condition with unstructured reflection. Participants in the control condition demonstrated less deliberate thought and weaker collaboration, which led to stress and misalignment during team meetings. In contrast, structured reflection supported greater organization and steadier progress. The deeper reflection condition further facilitated confidence, teamwork, and idea generation, although it imposed a higher cognitive load. We conclude by discussing design implications for AI agents that facilitate reflection to enhance collaboration and broader considerations for AI assisted systems aimed at sustaining collaborative goals.