Date
Publisher
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.
Study at a glance
- Question: Can an AI tool help teachers create personalized math worksheets based on students' skill levels and motivation?
- Setting and data: Grade 8 mathematics curriculum content and feedback from K-12 in-service teachers.
- Method: Automated quality assessments of generated worksheets and exploratory feedback from teachers.
Why it matters
Increasing student heterogeneity poses significant challenges for teachers, especially in mathematics, where cognitive, motivational, and emotional differences influence learning. Meanwhile, most AI personalization tools remain performance-focused, offering teachers limited support and neglecting broader pedagogical needs.
Key findings
- In grade 8 mathematics, ten evaluations showed the AI system reliably created personalized worksheets aligned with students' proficiency levels and intrinsic motivation.
- K-12 in-service teachers particularly highlighted the clear structure and suitability of the AI-generated instructional tasks.
- The multi-agent architecture demonstrated potential to deliver scalable, context-aware personalization for classrooms with diverse student learning needs.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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