## 3 Methodology
### Experimental Design  
To ensure a rigorous and systematic evaluation of the effectiveness of the Multi-Examiner system in automatically generating high-quality MCQs for IT education, we designed a comprehensive experimental protocol that includes three key components: participant selection, experimental materials and procedures, and evaluation metrics. The study used an expert evaluation methodology for a comparative analysis of the quality of the question generated by Multi-Examiner relative to alternative generation methods. If the questions produced by the Multi-Examiner system receive scores that differ significantly from those generated by GPT-4 while closely aligning with scores from human-generated questions, this would demonstrate the effectiveness of the system. Prior to the main experiment, we conducted ten semi-structured interviews with experienced IT educators to validate and refine the initial assessment framework, significantly informing the final experimental design. As shown in Figure 4, the Multi-Examiner system features a four-layer architectural design that integrates the knowledge graph and question generation modules. The User Interface Layer serves as the entry point for processing question generation requests through the Application Service Layer. The Core Service Layer contains the system's essential intelligent components: the Knowledge Graph Service, which provides Ontology Model Management, the Knowledge Inference Engine, and Cypher Query Optimization; the Knowledge Base Service, which manages Knowledge Formatting, Knowledge Vectorization, and Hybrid Search Index construction; and the Agent Framework, based on the Langchain framework, which comprises Identificador, Explorador, Fabricator, and Revisor, collaborating in knowledge identification, exploration, construction, and validation. The Infrastructure Layer includes the Neo4 Database and Vector Storage, forming a complete knowledge-driven question generation system through standardized component communication and configuration management. Furthermore, the system creates query tools for both the Identificador and Explorador, employs prompt engineering strategies for agents to perform their respective roles, and implements an event bus based on the publish-subscribe pattern for asynchronous communication. The system architecture employs the Neo4j graph database (version 4.3.11) as the core platform for storing and managing the knowledge graph, facilitating efficient knowledge retrieval, and reasoning via the Cypher query language. The database is deployed within Docker containers to ensure system stability and portability. Figure 4: System architecture of Multi-Examiner, illustrating the three-tier design of the knowledge graph and its integration with question generation modules. To enhance retrieval efficiency, we designed a set of optimized cypher query templates, categorized into two main types: (1) Knowledge Point Queries, which extract detailed conceptual information and knowledge types of specific knowledge points, serving as foundational data for question generation; and (2) Knowledge Path Queries, which explore relationships among related knowledge points to generate contextually relevant distractors. Integration with the system is facilitated through Python's Neo4j driver, providing efficient and stable access to knowledge. The knowledge graph consists of three hierarchical layers:  
1. Ontology Layer: This layer, designed based on the characteristics of IT education and [PERSON]'s taxonomy, defines four core ontology classes: _Knowledge_ (basic knowledge points), _Concept_ (conceptual knowledge), _Procedure_ (procedural knowledge), and _Metacognition_ (metacognitive knowledge). It includes semantic relation types that support different cognitive levels, such as CONTAINS (memory and understanding), BELONGS_TO (application and analysis), PREREQUISITE (evaluation), and RELATED (creation). These relations empower the knowledge graph to facilitate multi-level cognitive question generation aligned with [PERSON]'s taxonomy. 2.