FACET: Multi-Agent AI Supporting Teachers in Scaling Differentiated Learning for Diverse Students
This addresses the problem of scaling differentiated learning for diverse students to reduce teacher workload, though it appears incremental as it builds on existing AI educational tools by focusing on teacher-facing design.
The paper tackles the challenge of differentiated instruction in heterogeneous classrooms by introducing FACET, a teacher-facing multi-agent AI framework that supports differentiation based on motivation, performance, and learning differences, with evaluation showing strong perceived value from practitioners.
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.