MyPrincipia: A Personalized First-Principles Learning Content Generator based on Inter-Agent Dialogue

Theoretical physical sciences-such as theoretical physics, chemistry, and materials science-feature deep abstraction hierarchies and long chains of logical dependency, making systematic learning reliant on prolonged engagement with multiple textbooks. While rigorous, such resources are largely non-personalized and inefficient for learners seeking rapid construction of a coherent physical picture. Recent advances in large language models (LLMs) offer new opportunities for educational support, but existing LLM-based educational applications-primarily centered on short-text reorganization and human-machine interaction-are most effective in domains with shallow abstraction hierarchies and are ill-suited to fields with densely interconnected conceptual structures. Grounded in theories of meaningful learning and concept mapping, we propose MyPrincipia, an LLM-based multi-agent framework for personalized first-principles knowledge explanation, particularly suited for systematic learning in theoretical physical sciences. The framework integrates an expert explainer agent with a metacognitive questioner agent that iteratively identifies unresolved conceptual ("what") and propositional ("why") gaps and generates targeted follow-up queries. Through controlled, round-based refinement with lightweight factual verification, the system reconstructs the underlying conceptual scaffold of a target learning topic and produces a personalized first-principles micro-textbook grounded in the learner's prior knowledge, accelerating the acquisition of coherent physical intuition. We welcome collaborations to further improve, extend, and apply this framework.