We introduce Textbook2KG for building knowledge graphs from lengthy textbooks using large language models (LLMs) and smart prompts. Our framework uses a step-by-step approach: splitting texts, extracting key facts, and connecting concepts through reasoning. Tested on three real textbooks, it achieved 85.5% accuracy in direct fact extraction but 65% for inferred links, showing where LLMs shine and where they need help. The textbook datasets we created with 528k+ words analyzed give researchers a solid base to improve educational KG tools. While current models make KG creation easier for teachers, smarter reasoning remains a challenge. This work shows how simple prompt tweaks can unlock LLMs’ hidden skills for organizing academic knowledge, blending AI power with classroom needs.