Research on Automatic Question Generation Methods for Niche Subjects Based on Large Language Models

High-quality question generation is crucial for ensuring the fairness and validity of examinations. To address the challenges of data scarcity and semantic complexity in automatic question generation (AQG) for niche subjects, including the arts, this study develops a domain-specific large language model (LLM) with a three-tiered optimization mechanism, incorporating prompt tuning, knowledge enhancement, and data augmentation. The model's effectiveness was validated through a case study conducted on a calligraphy course. The results showed that the generated questions achieved a usability rate of 91%, whereas the proposed data augmentation strategy expanded the question bank by 132.56%. This work provides both technical solutions and practical reference for automatic question generation methods targeting niche disciplines. The key contributions of this study encompass the creation of an innovative three-tiered optimization framework, the effective integration of external domain knowledge, and an iterative data augmentation approach that enhances question generation for niche subjects. This research offers a technological pathway and serves as a valuable reference for AQG in niche disciplines.