Teacher-centered LLM-based multi-agent systems–towards differentiated educational worksheets

Classrooms are increasingly heterogeneous with respect to students' language backgrounds, prior knowledge, and motivational dispositions, making differentiated instruction essential for equitable learning. Yet implementing differentiation remains challenging, as teachers face high workloads, and existing Artificial Intelligence (AI) tools mainly focus on performance while neglecting motivational and emotional factors. This paper presents a teacher-facing, large language model (LLM)-based multi-agent system for generating differentiated mathematics worksheets that account for both cognitive and motivational learner characteristics, supporting teacher-centered, AI-driven personalization in mathematics education. The framework includes 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 in-service teachers. Preliminary findings from ten evaluations highlight stability and alignment between generated materials and learner profiles. Teacher feedback particularly emphasized the structure and suitability of tasks. Findings suggest that multi-agent LLM architectures have the potential to enable scalable, context-aware differentiation in heterogeneous classroom settings and outline directions for extending the framework to richer learner profiles and real-world classroom trials.