Parent-AI collaboration to support real-time conversations with children is challenging due to the sensitivity and open-ended nature of such interactions. Existing systems often simplify collaboration into static modes, providing limited support for adapting AI to continuously evolving conversational contexts. To address this gap, we systematically investigate the dynamics of parent-AI collaboration modes in real-time conversations with children. We conducted a co-design study with eight parents and developed COMPASS, a research probe that enables flexible combinations of parental support functions during conversations. Using COMPASS, we conducted a lab-based study with 21 parent-child pairs. We show that parent-AI collaboration unfolds through evolving modes that adapt systematically to contextual factors. We further identify three types of parental strategies--parent-oriented, child-oriented, and relationship-oriented--that shape how parents engage with AI. These findings advance the understanding of dynamic human-AI collaboration in relational, high-stakes settings and inform the design of flexible, context-adaptive parental support systems.

翻译：亲子对话中，家长-AI协作（Parent-AI collaboration）因互动情境的敏感性与开放性而极具挑战性。现有系统常将协作简化为静态模式，难以支持AI对不断演化的对话语境进行动态适应。针对这一缺口，本研究系统探究了家长-AI在实时亲子对话中的协作模式动态机制。我们与八位家长展开协同设计研究，构建了研究探针COMPASS（一种可在对话过程中灵活组合家长支持功能的系统）。通过COMPASS，我们开展了包含21对亲子组合的实验室研究。结果表明，家长-AI协作通过随语境因素系统演化的动态模式展开。进一步识别出三类家长策略——以家长为中心、以儿童为中心、以关系为中心——这些策略塑造了家长与AI的互动方式。这些发现深化了对高利害关系情境下动态人机协作的理解，并为设计灵活、情境自适应的家长支持系统提供了指导。