Family learning takes place in everyday routines where children and caregivers read, practice, and develop new skills together. Although AI is increasingly present in learning environments, most systems remain child-centered and overlook the collaborative, distributed nature of family education. This paper investigates how AI can mediate family collaboration by addressing tensions of coordination, uneven workloads, and parental mediation. From a formative study with families using AI in daily learning, we identified challenges in responsibility sharing and recognition of contributions. Building on these insights, we designed FamLearn, an LLM-powered prototype that distributes tasks, visualizes contributions, and provides individualized support. A one-week field study with 11 families shows how this prototype can ease caregiving burdens, foster recognition, and enrich shared learning experiences. Our findings suggest that LLMs can move beyond the role of tutor to act as family mediators - balancing responsibilities, scaffolding intergenerational participation, and strengthening the relational fabric of family learning.

翻译：家庭学习发生于日常惯例中，儿童与照料者通过共同阅读、练习和发展新技能来实现。尽管人工智能在学习环境中的应用日益增多，但多数系统仍以儿童为中心，忽视了家庭教育协作式、分布式的本质。本文探讨了人工智能如何通过协调任务分配、平衡不均等工作负荷及促进家长引导来调解家庭协作。通过对家庭在日常学习中使用人工智能的构成性研究，我们识别了责任分担与贡献认可方面的挑战。基于这些发现，我们设计了FamLearn——一个由大语言模型驱动的原型系统，该系统能分配任务、可视化贡献并提供个性化支持。对11个家庭开展的为期一周的实地研究表明，该原型能够减轻照护负担、促进贡献认可并丰富共享学习体验。我们的研究结果表明，大语言模型能够超越辅导角色，成为家庭学习的协调者——平衡责任、搭建代际参与的支架，并增强家庭学习的关系纽带。