Educational content generation using multi-LLM agents
- International Robotics & Automation Journal
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Ishmam Ahmed Solaiman, Tanzila Sultana Maria, Mariofanna Milanova
Abstract
This paper presents a framework for generating educational content using a multi-agent
architecture powered by Large Language Models (LLMs). Leveraging the Crew AI
framework, the system coordinates multiple autonomous agents, each with specific roles,
to produce cohesive educational materials. The agents work collaboratively to generate,
detail, direct, and refine content aimed at children under 13, ensuring the inclusion of moral
lessons and engaging narratives. This approach enhances the efficiency and effectiveness of
content creation, providing a scalable solution for educational material generation.
Keywords
LLama-3, GPT4o, multi-agent systems, crew AI, langchain, langgraph, dall-E
