In collaborative settings, sustaining momentum and engagement between checkpoints (e.g., meetings) can be challenging, often leading to task drift and reduced preparedness. To address this gap, we developed ReflectEd, an AI-assisted system that supports between-checkpoint reflection through theory-driven prompts with progressively structured levels and mechanism-based scaffolding. We evaluated ReflectEd in a mixed-method study comparing two reflection configurations: a regular reflection workflow and a deeper reflection workflow that included an additional transformative reflection activity. Across conditions, participants reported steady engagement early in the week. In the deeper configuration, later reflections tended to exhibit higher actionability and richer forward-looking planning, while also being harder to sustain and more effortful during periods of active work. Partner-visible reflections were frequently described as supporting coordination by surfacing differences in focus and facilitating accountability. Overall, the findings characterize trade-offs between reflection depth, feasibility, and perceived preparedness for subsequent checkpoints. We discuss implications for the design of AI-assisted systems that support collaboration readiness and reflection-oriented regulation in time-constrained collaborative workflows.

翻译：在协作场景中，两次检查点（如会议）之间维持动力与参与度常具挑战性，易导致任务偏离与准备不足。为填补这一空白，我们开发了ReflectEd——一种AI辅助系统，通过基于理论的提示（具有渐进式结构化层级）和机制化支架，支持检查点间的反思。我们采用混合方法研究评估ReflectEd，对比两种反思配置：常规反思工作流与包含额外转化性反思活动的深度反思工作流。实验发现，参与者在各周初均保持稳定参与；在深度配置中，后期反思表现出更高的可操作性及更丰富的前瞻性规划，但同时更难维持且在繁忙工作阶段需付出更多努力。同伴可见的反思常被描述为通过揭示关注点差异并促进问责来支持协调。总体而言，研究结果揭示了反思深度、可行性及对后续检查点感知准备度之间的权衡。我们讨论了AI辅助系统在时间受限的协作工作流中支持协作准备度与反思导向调节的设计启示。