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

arXiv: https://arxiv.org/abs/2508.16659 · HTML: https://mlsanity.com/paper/2508.16659/

## Key facts

- Cited by 1 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 7 of its 27 references that are in the map.
- First posted on arXiv 20 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, 7 of 7)

- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [Open Source Language Models Can Provide Feedback: Evaluating LLMs' Ability to Help Students Using GPT-4-As-A-Judge](https://mlsanity.com/paper/2405.05253/) (2024)
- [EduPlanner: LLM-Based Multi-Agent Systems for Customized and Intelligent Instructional Design](https://mlsanity.com/paper/2504.05370/) (2025)
- [A Systematic Review on Prompt Engineering in Large Language Models for K-12 STEM Education](https://mlsanity.com/paper/2410.11123/) (2024)
- [FACET: Teacher-Centred LLM-Based Multi-Agent Systems-Towards Personalized Educational Worksheets](https://mlsanity.com/paper/2508.11401/) (2025)
- [Generating AI Literacy MCQs: A Multi-Agent LLM Approach](https://mlsanity.com/paper/2412.00970/) (2024)
- [To what extent is ChatGPT useful for language teacher lesson plan creation?](https://mlsanity.com/paper/2407.09974/) (2024)

## Built on by (most cited first, 1 of 1)

- [AI-accelerated End-to-End Framework for Rapid Professional Upskilling](https://mlsanity.com/paper/2607.14044/) (2026)

## Abstract (arXiv)

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 bridge this gap, most teachers lack the time or expertise and find it difficult to encode such pedagogical nuance into their requests. This study shifts pedagogical expertise from the user's prompt to the LLM's internal architecture. We embed the well-established Knowledge-Learning-Instruction (KLI) framework into a Multi-Agent System (MAS) to act as a sophisticated instructional designer. We tested three systems for generating secondary Math and Science learning activities: a Single-Agent baseline simulating typical teacher prompts; a role-based MAS where agents work sequentially; and a collaborative MAS-CMD where agents co-construct activities through conquer and merge discussion. The generated materials were evaluated by 20 practicing teachers and a complementary LLM-as-a-judge system using the Quality Matters (QM) K-12 standards. While the rubric scores showed only small, often statistically insignificant differences between the systems, the qualitative feedback from educators painted a clear and compelling picture. Teachers strongly preferred the activities from the collaborative MAS-CMD, describing them as significantly more creative, contextually relevant, and classroom-ready. Our findings show that embedding pedagogical principles into LLM systems offers a scalable path for creating high-quality educational content.