Using Large Language Models to Create Educational Materials for the “Databases” Discipline

Abstract

This paper examines the use of large language models (LLM) for creating educational materials. A practical methodology for generating high-quality educational content for the specific discipline of “Databases” is proposed and verified. A multi-stage methodology is presented, in which one LLM generates content, and a second, independent “reasoning” model verifies its quality and correctness. A comparison method with an authoritative source and a modified “verification chain” algorithm was used to check the generated materials for factual errors. The results confirm that this approach, when used with modern, high-performance LLMs (such as DeepSeek and Gemini), enables the creation of high-quality educational texts with a low probability of hallucinations. The methodology can significantly accelerate the development of reliable educational materials and can be optimized by reducing the number of iterations while maintaining a high-quality initial response.