## 3 Methodology
### System Development
#### 3.3.3 Multi-Examiner System Design  
The Multi-Examiner module is grounded in modern educational assessment theory, integrating cognitive diagnostic theory, item generation theory, and formative assessment theory to construct a theory-driven intelligent agent collaborative assessment framework. This framework innovatively designs four types of intelligent agents: Identificador, Explorador, Fabricator, and Revisor, forming a complete question generation pipeline. Through four specialized intelligent agents, the system achieves precise control over the question generation process, with each agent designed based on specific educational theories, collectively constituting a comprehensive intelligent educational test item generation system (Figure 3). This theory-based multi-agent system framework design not only ensures the educational value of generated questions but also provides a new technical paradigm for educational measurement and evaluation. The Identificador, designed based on schema theory and learned by each intelligent agent through prompts, is responsible for deep semantic understanding and cognitive feature analysis of knowledge points. This agent implements knowledge retrieval through the function:  
\[f(S_{id})=Identificador_{retrieve}(k), \tag{4}\]  
where \(S_{id}\) represents the set of synonyms and related terms, \(k\) is the original knowledge point input by the user, and \(Identificador_{retrieve}\) denotes the large language model's operation to fetch and generate synonymous and related terms using its trained capabilities on vast corpora and search engine integration. The Identificador not only identifies surface features of knowledge points but also, more importantly, analyzes cognitive structures and semantic networks based on schema theory. For example, when processing the knowledge point \"Operating System\", the Identificador first constructs its cognitive schema, including core attributes (such as system software characteristics), process features (such as resource management mechanisms), and relational features (such as interactions with hardware and application software), thereby providing a complete cognitive framework for subsequent question generation. The Exploratory adopts constructivist learning theory to guide knowledge association exploration, implementing multidimensional semantic connections in the knowledge graph through the function:  
\[f(S_{exp}=Explorador_{retrieve}(S_{id},KG)), \tag{5}\]  
where \(S_{exp}\) represents the detailed knowledge entries, \(S_{id}\) is the set of input terms from the Identificador, and \(KG\) is the knowledge graph. This agent innovatively implements directed retrieval strategies based on cognitive levels, capable of selecting corresponding knowledge nodes according to different cognitive levels of [PERSON]'s taxonomy. For example, when generating higher-order thinking questions, the Explorador prioritizes knowledge nodes related to advanced cognitive processes such as analysis, evaluation, and creation, establishing logical connections between these nodes to provide knowledge support for generating complex assessment tasks. The Fabricator integrates cognitive load theory and question type design theory, implementing dynamic question generation through the function:  
\[Q(k,t)=Fabricator_{t}(LLM,P_{t},k), \tag{6}\]  
where \(Q(k,t)\) represents the question generated, \(t\) is the type of knowledge (factual, conceptual, procedural, metacognitive), \(k\) is the knowledge point, and \(P_{t}\) is the tailored prompt for  
Figure 3: Architecture of Multi-Examiner. type \(t\). The Fabricator's innovation is reflected in its ability to dynamically adjust question complexity according to learning objectives. This agent adopts specific generation strategies (\(P_{t}\)) for different cognitive objectives (\(t\)), ensuring assessment validity while controlling question cognitive load levels. The Fabricator's innovation is reflected in its ability to dynamically adjust question complexity according to learning objectives.