# FACET: Teacher-Centred LLM-Based Multi-Agent Systems-Towards Personalized Educational Worksheets arXiv: https://arxiv.org/abs/2508.11401 ยท HTML: https://mlsanity.com/paper/2508.11401/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use. - Builds on 18 of its 72 references that are in the map. - First posted on arXiv 15 Aug 2025. ## Placed in - LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/) ## Builds on (most cited first, 18 of 18) - [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023) - [Simulating Classroom Education with LLM-Empowered Agents](https://mlsanity.com/paper/2406.19226/) (2024) - [EduAgent: Generative Student Agents in Learning](https://mlsanity.com/paper/2404.07963/) (2024) - [MathVC: An LLM-Simulated Multi-Character Virtual Classroom for Mathematics Education](https://mlsanity.com/paper/2404.06711/) (2024) - [Empowering Personalized Learning through a Conversation-based Tutoring System with Student Modeling](https://mlsanity.com/paper/2403.14071/) (2024) - [LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models](https://mlsanity.com/paper/2402.02896/) (2024) - [LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Education](https://mlsanity.com/paper/2410.14012/) (2024) - [Evaluating Large Language Models with Psychometrics](https://mlsanity.com/paper/2406.17675/) (2024) - [LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System](https://mlsanity.com/paper/2501.15749/) (2025) - [Can LLMs Reliably Simulate Human Learner Actions? A Simulation Authoring Framework for Open-Ended Learning Environments](https://mlsanity.com/paper/2410.02110/) (2024) - [Classroom Simulacra: Building Contextual Student Generative Agents in Online Education for Learning Behavioral Simulation](https://mlsanity.com/paper/2502.02780/) (2025) - [Exploring Reasoning Biases in Large Language Models Through Syllogism: Insights from the NeuBAROCO Dataset](https://mlsanity.com/paper/2408.04403/) (2024) - [Designing AI-Agents with Personalities: A Psychometric Approach](https://mlsanity.com/paper/2410.19238/) (2024) - [AI-Driven Virtual Teacher for Enhanced Educational Efficiency: Leveraging Large Pretrain Models for Autonomous Error Analysis and Correction](https://mlsanity.com/paper/2409.09403/) (2024) - [How Far Are LLMs from Believable AI? A Benchmark for Evaluating the Believability of Human Behavior Simulation](https://mlsanity.com/paper/2312.17115/) (2023) - [Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research](https://mlsanity.com/paper/2506.01839/) (2025) - [Which Type of Students can LLMs Act? Investigating Authentic Simulation with Graph-based Human-AI Collaborative System](https://mlsanity.com/paper/2502.11678/) (2025) - [The Good, the Bad, and the Hulk-like GPT: Analyzing Emotional Decisions of Large Language Models in Cooperation and Bargaining Games](https://mlsanity.com/paper/2406.03299/) (2024) ## Built on by (most cited first, 7 of 7) - [Towards Valid Student Simulation with Large Language Models](https://mlsanity.com/paper/2601.05473/) (2026) - [Subliminal Transfer of Unsafe Behaviors in AI Agent Distillation](https://mlsanity.com/paper/2604.15559/) (2026) - [When Verification Hurts: Asymmetric Effects of Multi-Agent Feedback in Logic Proof Tutoring](https://mlsanity.com/paper/2603.27076/) (2026) - [Enabling Multi-Agent Systems as Learning Designers: Applying Learning Sciences to AI Instructional Design](https://mlsanity.com/paper/2508.16659/) (2025) - [Lect=uraAgents: A Multi-Agent Framework for Adaptive Personalized AI-Assisted Learning and Embodied Teaching](https://mlsanity.com/paper/2606.16428/) (2026) - [Wisdom Of The (AI) Crowd: Investigating Artificial Swarm Intelligence In Large Language Models](https://mlsanity.com/paper/2606.31404/) (2026) - [An Educator-Guided LLM Pedagogical Agent for Scaffolded Feedback in Conceptual Database Design](https://mlsanity.com/paper/2610.00870/) (2026) ## Abstract (arXiv) 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 AI-driven 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 in-service 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.