## 6 Conclusions  
This research addressed three critical gaps in automated assessment for K-12 IT education: the shortage of contextually relevant distractors, the lack of question diversity and cognitive challenge, and the limited generation of higher-order thinking questions. Our comprehensive evaluation demonstrates that the Multi-Examiner system successfully addresses these gaps through its innovative architecture combining KGs, domain-specific search tools, and LLMs. Our contributions are threefold: First, we demonstrated that Multi-Examiner significantly outperforms GPT-4 in generating contextually relevant distractors across multiple knowledge types, achieving quality comparable to human experts. Second, we established that Multi-Examiner enhances question diversity and cognitive challenge, producing assessment sets that closely align with human-generated standards while surpassing conventional LLM approaches. Third, we provided empirical evidence that Multi-Examiner effectively generates higher-order thinking questions, particularly excelling at the evaluation and creation levels of Bloom's taxonomy. In conclusion, the Multi-Examiner system effectively generates high-quality, higher-order thinking questions for K-12 IT education, outperforming GPT-4 across cognitive levels, especially in evaluation and creation. This highlights the value of integrating KGs and domain-specific tools to enhance question diversity and complexity, aligning closely with human-crafted quality. These findings have significant implications for educational assessment practices, offering educators a reliable tool that reduces workload while maintaining high standards. The Multi-Examiner architecture provides a reproducible framework that can potentially transform assessment generation across educational domains. Despite promising results, this study has several limitations. The sample size of 30 questions per cognitive level may limit generalizability, and the subjective nature of expert scoring introduces potential bias. Additionally, our focus on IT education may not fully represent the challenges in other subject areas with different knowledge structures. Future research should address these limitations by expanding to diverse subjects and incorporating more objective evaluation metrics. We also recommend exploring the integration of Multi-Examiner with adaptive learning systems to personalize assessment based on individual student needs. Further investigation into combining Multi-Examiner with other AI technologies like automated feedback generation could create more comprehensive educational tools. Finally, longitudinal studies examining the impact of Multi-Examiner-generated assessments on student learning outcomes would provide valuable insights into its practical effectiveness. Conceptualization, Y.W. and Z.Y. ([PERSON]); Methodology, Y.W. and Z.Y. ([PERSON]); Software, Y.W. and Z.Y. ([PERSON]); Validation, [PERSON] ([PERSON]) and Z.W. ; Formal analysis, [PERSON] and Z.Y. ([PERSON]); Investigation, Y.W., Z.Y. ([PERSON]) and Z.W. ; Resources, [PERSON] ([PERSON]) and Z.W. ; Data curation, [PERSON] ([PERSON]) and Z.W. ; Writing--original draft, Y.W., Z.Y. ([PERSON]) and Z.Y. ([PERSON]); Writing--review & editing, Y.W., [PERSON] ([PERSON]) and J.W. ; Supervision, Y.W. ; Project administration, Y.W. ; Funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant No. 6217021982). The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Institute of Applied Psychology at ZhejiangUniversity of Technology (No. 2024D024) (15 September 2024). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data presented in this study are available on request from the corresponding author due to privacy. During the preparation of this study, the author used GPT-4 (released in March 2023) for the purposes of programming.