Classrooms are becoming increasingly heterogeneous, comprising learners with diverse performance and motivation levels, language proficiencies, and learning differences such as dyslexia and ADHD. While teachers recognize the need for differentiated instruction, growing workloads create substantial barriers, making differentiated instruction an ideal that is often unrealized in practice. Current AI educational tools, which promise differentiated materials, are predominantly student-facing and performance-centric, ignoring other aspects that shape learning outcomes. We introduce FACET, a teacher-facing multi-agent framework designed to address these gaps by supporting differentiation that accounts for motivation, performance, and learning differences. Developed with educational stakeholders from the outset, the framework coordinates four specialized agents, including learner simulation, diagnostic assessment, material generation, and evaluation within a teacher-in-the-loop design. School principals (N = 30) shaped system requirements through participatory workshops, while in-service K-12 teachers (N = 70) evaluated material quality. Mixed-methods evaluation demonstrates strong perceived value for inclusive differentiation. Practitioners emphasized both the urgent need arising from classroom heterogeneity and the importance of maintaining pedagogical autonomy as a prerequisite for adoption. We discuss implications for future school deployment and outline partnerships for longitudinal classroom implementation.

翻译：课堂环境正日益呈现出异质性特征，包含在学业表现、动机水平、语言熟练度以及阅读障碍、注意力缺陷多动障碍等学习差异方面各不相同的学习者。尽管教师普遍认识到差异化教学的必要性，但日益增长的工作负荷构成了实质性障碍，使得差异化教学往往成为实践中难以实现的理想。当前承诺提供差异化材料的AI教育工具主要面向学生且以学业表现为中心，忽视了影响学习结果的其他关键维度。我们提出了FACET——一个面向教师的多智能体框架，旨在通过支持兼顾动机、表现与学习差异的差异化教学来弥补这些不足。该框架从设计之初即与教育相关方协同开发，在教师参与循环的设计中协调了四个专项智能体，包括学习者模拟、诊断性评估、材料生成与效果评估。学校管理者（N = 30）通过参与式研讨会确定了系统需求，在职K-12教师（N = 70）则对生成材料的质量进行了评估。混合方法评估表明，该框架在促进包容性差异化教学方面具有显著的感知价值。教育实践者既强调了课堂异质性所带来的迫切需求，也指出维护教学自主权是采用该系统的必要前提。我们讨论了未来在学校部署的实践意义，并概述了开展纵向课堂实施研究的合作计划。