Interdisciplinary-QG: An LLM-Based Framework for Generating High-Quality Interdisciplinary Test Questions with Knowledge Graphs and Chain-of-Thought Reasoning

As interdisciplinary education gains prominence in global educational reforms, the design of high-quality interdisciplinary test items remains a challenge due to the complexity of knowledge integration, question difficulty control, and the inefficiency of manual generation. To address these issues, this study introduces Interdisciplinary-QG, an automated interdisciplinary question generation framework based on GPT-4. The framework integrates knowledge graph-enhanced retrieval-based generation with chain-of-thought reasoning and employs a structured BRTE (Background-Role-Task-Example) prompt template, enhancing both accuracy and interdisciplinary coherence. A case study in chemistry demonstrates that Interdisciplinary-QG effectively constructs interdisciplinary knowledge structures and generates high-quality test items with both depth and breadth. Experimental results show that it outperforms the general-purpose LLM ChatGLM in validity, efficiency, and interdisciplinary integration. This study provides new insights into leveraging AI for interdisciplinary education.