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：由于亲子对话的敏感性和开放性，家长通过AI协作支持实时儿童对话极具挑战性。现有系统常将协作简化为静态模式，无法使AI动态适应不断演变的对话情境。为弥补这一不足，我们系统探究了实时亲子对话中家长-AI协作模式的动态机制。通过与八位家长的协同设计研究，我们开发了研究探针COMPASS——该工具可在对话过程中灵活组合家长支持功能。借助COMPASS，我们开展了一项包含21组亲子对的实验室研究。研究表明，家长-AI协作通过随情境因素动态演化的模式展开。我们进一步识别出三类家长策略——以家长为中心、以儿童为中心及以关系为中心，这些策略塑造了家长与AI的交互方式。这些发现深化了对高风险关系场景中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启示。