The answer may vary: large language model response patterns challenge their use in test item analysis

These findings suggest LLMs have limited utility in predicting MCQ performance metrics. Notably, higher-scoring models showed less correlation with human performance, suggesting that as models become more powerful, their ability to predict human performance may decrease. Understanding the consistency of an LLM's response pattern is critical for both research methodology and practical applications in test development. Future work should focus on leveraging the language-processing capabilities of LLMs for overall assessment optimization (e.g., inter-item correlation) rather than predicting item characteristics.