## 5 Discussions  
### Discussion of Distractor Contextual Relevance and Generation Method Effectiveness  
Based on the above analysis, we conclude that Multi-Examiner demonstrates a significant advantage in generating contextually relevant distractors, especially for factual and procedural knowledge types. The effectiveness of Multi-Examiner suggests that leveraging structured domain-specific information enhances the generation of distractors, achieving a quality level comparable to that of human-generated questions across multiple knowledge types. The higher scores observed for conceptual and metacognitive knowledge types indicate that these areas may be less challenging for automated systems, likely due to the more flexible nature of the knowledge involved, which allows for more variance in distractor generation. The limitations of GPT-4 in generating high-quality distractors, particularly for factual knowledge, suggest a need for further development of LLMs tailored to educational assessment tasks. This performance gap may stem from GPT-4's lack of structured, domain \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cognitive Level** & **Comparison** & **Mean Difference** & _p-Value_ & **95\% CI** \\ \hline \multirow{3}{*}{Evaluation} & Multi-Examiner vs. GPT-4 & 0.97 & \(<\)0.001 & [0.54, 1.40] \\  & Multi-Examiner vs. human & 1.32 & \(<\)0.001 & [0.89, 1.75] \\  & GPT-4 vs. human & 0.35 & 0.135 & [\(-\)0.08, 0.78] \\ \hline \multirow{3}{*}{Creation} & Multi-Examiner vs. GPT-4 & 0.53 & 0.120 & [\(-\)0.10, 1.17] \\  & Multi-Examiner vs. human & 1.40 & \(<\).001 & [0.76, 2.03] \\ \cline{1-1}  & GPT-4 vs. human & 0.87 & 0.005 & [0.23, 1.50] \\ \hline \hline \end{tabular}
\end{table} 
Table 15: [PERSON]’s HSD post-hoc test results for evaluation and creation levels. specific knowledge, affecting its ability to generate distractors that are closely related to specific knowledge points. Additionally, the lower performance in factual knowledge highlights the inherent difficulty in generating distractors for this knowledge type, as it often has clear right and wrong distinctions, making it challenging to create distractors that are both relevant and misleading. These findings have practical implications, indicating that while automated systems like Multi-Examiner show great potential, further optimization, particularly in factual knowledge areas, is needed to enhance the system's application in educational settings. For educators and assessment designers not involved in this study, our findings suggest several practical implementation paths. First, the Multi-Examiner system could be integrated into existing learning management systems as a question generation plugin, where teachers simply input topic areas and receive diverse distractor sets. Second, the contextual relevance metrics developed in this study could serve as evaluation criteria for teachers to quickly assess the quality of automatically generated assessment items before deployment. Additionally, our system architecture provides a template for institutions developing their own assessment tools--the combination of domain-specific KGs with LLMs represents a reproducible approach that can be adapted across various subject areas. Discussion on Enhancing Question Diversity and Cognitive Challenge Through Automated Generation Methods  
The analysis indicates that Multi-Examiner has a clear advantage in enhancing the diversity, challenge, and higher-order thinking of generated question sets, particularly when compared to GPT-4. This finding highlights the potential of integrating KGs and domain-specific search tools with large language models to improve the quality and diversity of automatically generated questions. The performance of Multi-Examiner is not significantly different from human-generated question sets, suggesting that it has the potential to be an effective support tool for educators, capable of producing question sets that closely match human standards in diversity and cognitive complexity.