Title: "Learning Together": AI-Mediated Support for Parental Involvement in Everyday Learning Show Chinese title

Title: “一起学习”：人工智能辅助的家长日常学习参与支持

Abstract: 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. Show Chinese abstract

Abstract: 家庭学习发生在日常惯例中，孩子和照顾者一起阅读、练习并发展新技能。 尽管人工智能在学习环境中越来越普遍，但大多数系统仍然是以儿童为中心，忽视了家庭教育的协作性和分布式特性。 本文研究了人工智能如何通过解决协调、工作量不均和家长指导等方面的矛盾来促进家庭合作。 通过对使用人工智能进行日常学习的家庭进行形成性研究，我们发现了责任分担和贡献认可方面的挑战。 基于这些见解，我们设计了FamLearn，这是一个由大语言模型驱动的原型，能够分配任务、可视化贡献并提供个性化支持。 一项针对11个家庭的一周实地研究展示了该原型如何减轻照护负担，促进认可，并丰富共同学习体验。 我们的研究结果表明，大语言模型可以超越导师的角色，作为家庭中介——平衡责任，搭建代际参与的脚手架，并加强家庭学习的关系网络。