Can LLMs personalize quantum computing lessons?
Toward Personalizing Quantum Computing Education: An Evolutionary LLM-Powered Approach
This paper proposes a novel, two-agent LLM-powered system for personalized quantum computing education. It aims to address limitations in existing platforms by incorporating a Teaching Agent for real-time interaction, a Lesson Planning Agent for dynamic lesson adaptation, a shared Knowledge Graph for persistent memory and context awareness, and a user-driven tag system to mitigate LLM hallucinations and enhance student control. Key points relevant to LLM-based multi-agent systems include: separation of concerns between agents, knowledge graph-mediated communication and state management, and user-driven interaction for enhanced control and reduced LLM reliance. Preliminary results in a simulated environment demonstrate the system's capacity for context-aware, dynamically adaptive learning experiences and data collection for future insights.