用大模型生成带语义标注的课程专属习题，提升自动学习模型更新能力。
Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects
- 结合检索增强生成技术，确保题目贴合具体课程背景。
- 结构与语义标注生成效果良好，但关系型标注仍不理想。
- 适合教育内容自动化生产，需人工校验才能投入实际教学。
过去几十年，自动化题目生成（AQG）的方法经历了显著演变。生成式自然语言模型的进展为教育内容生成带来了新可能。本文探索大语言模型（LLMs）在生成计算机科学课程专属、具备充分语义标注以支持自动学习模型更新，并体现认知维度的题目方面的潜力。不同于以往使用ChatGPT等基础方法，本研究采用更精准的检索增强生成（RAG）策略，生成具有上下文相关性和教学意义的学习对象。结果表明，结构化与语义标注的生成表现良好，但关系型标注效果不佳。生成题目的质量普遍未达教育标准，说明尽管LLMs能丰富学习材料库，但当前性能仍需大量人工干预进行修正与验证。
原文摘要 · Abstract (English)
Background: Over the past few decades, the process and methodology of automated question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational content. Objectives: This paper explores the potential of large language models (LLMs) for generating computer science questions that are sufficiently annotated for automatic learner model updates, are fully situated in the context of a particular course, and address the cognitive dimension understand. Methods: Unlike previous attempts that might use basic methods like ChatGPT, our approach involves more targeted strategies such as retrieval-augmented generation (RAG) to produce contextually relevant and pedagogically meaningful learning objects. Results and Conclusions: Our results show that generating structural, semantic annotations works well. However, this success was not reflected in the case of relational annotations. The quality of the generated questions often did not meet educational standards, highlighting that although LLMs can contribute to the pool of learning materials, their current level of performance requires significant human intervention to refine and validate the generated content.
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