Enabling Multi-Agent Systems as Learning Designers: Applying Learning Sciences to AI Instructional Design

This paper evaluates three multi-agent LLM systems that embed the Knowledge-Learning-Instruction (KLI) framework to generate secondary Math and Science learning activities, comparing them against a baseline single-agent system through teacher evaluations and LLM-as-a-judge assessments using Quality Matters K-12 standards. The study demonstrates that collaborative multi-agent systems can produce more pedagogically sound, creative, and classroom-ready activities by encoding learning science principles directly into the AI architecture.

K-12 educators are increasingly using Large Language Models (LLMs) to create instructional materials. These systems excel at producing fluent, coherent content, but often lack support for high-quality teaching. The reason is twofold: first, commercial LLMs, such as ChatGPT and Gemini which are among the most widely accessible to teachers, do not come preloaded with the depth of pedagogical theory needed to design truly effective activities; second, although sophisticated prompt engineering can b

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