FACET: Multi-Agent AI Supporting Teachers in Scaling Differentiated Learning for Diverse Students

TL;DR

FACET introduces a teacher-facing multi-agent framework that operationalizes differentiation beyond performance by coordinating four specialized agents—learner simulation, diagnostic assessment, material generation, and evaluation—within a teacher-in-the-loop workflow. The approach emphasizes curriculum alignment, accessibility, and longitudinal context to generate differentiated, interface-ready worksheets while preserving educator autonomy. Mixed-methods evaluation with $N=30$ principals and $N=70$ teachers demonstrates strong perceived value for inclusive differentiation, though enrichment for advanced learners and dyslexia-specific formatting require refinement. The work highlights practical deployment considerations and ongoing partnerships for longitudinal classroom implementation, underscoring FACET's potential to reduce teacher workload while maintaining pedagogical rigor.

Abstract

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.

Figures (3)

• Figure 1: Core Principle for individualized classroom material using a multi-agent framework
• Figure 2: Core workflow: Teacher-defined learner profiles drive multi-agent generation of differentiated materials with human review
• Figure 3: Example worksheet for high performance and low motivation