FACET: Teacher-Centred LLM-Based Multi-Agent Systems— Towards Personalized Educational Worksheets

Jana Gonnermann-Müller

Abstract:

The increasing heterogeneity of student populations poses
significant challenges for teachers, particularly in mathematics education, where cognitive, motivational, and emotional
differences strongly influence learning outcomes. While AIdriven personalization tools have emerged, most remain
performance-focused, offering limited support for teachers and neglecting broader pedagogical needs. This paper
presents the FACET framework, a teacher-facing, large language model (LLM)-based multi-agent system designed to
generate individualized classroom materials that integrate
both cognitive and motivational dimensions of learner profiles. The framework comprises three specialized agents:
(1) learner agents that simulate diverse profiles incorporating topic proficiency and intrinsic motivation, (2) a teacher
agent that adapts instructional content according to didactical principles, and (3) an evaluator agent that provides automated quality assurance. We tested the system using authentic grade 8 mathematics curriculum content and evaluated its
feasibility through a) automated agent-based assessment of output quality and b) exploratory feedback from K-12 inservice teachers. Results from ten internal evaluations highlighted high stability and alignment between generated materials and learner profiles, and teacher feedback particularly highlighted structure and suitability of tasks. The findings demonstrate the potential of multi-agent LLM architectures
to provide scalable, context-aware personalization in heterogeneous classroom settings, and outline directions for extending the framework to richer learner profiles and real-world classroom trials.