多智能体协作框架下的数学问题生成

摘要

数学教育中的自动问题生成（AQG）对于智能辅导系统和教育工作者而言，仍是一个难以企及的目标。尽管基于预训练变换器的语言模型在自然语言生成方面取得了显著进展，但它们往往难以精确控制问题的复杂性和认知需求。本文提出了一种协作多智能体框架，作为一种将推理时计算融入AQG的新方法。该框架利用多个智能体迭代优化生成的问题-答案对，以更好地平衡复杂性与认知需求。我们从五个元评估标准——相关性、重要性、清晰度、难度匹配和可回答性——对生成的问题进行评估，以衡量系统在控制所需复杂性和问题质量方面的能力。初步评估表明，这种协作多智能体框架通过促进认知挑战与清晰度之间更为细致的平衡，提升了生成教育内容的质量。这些积极成果表明，整合协作多智能体工作流能够产生更为可控、具有教学价值的内容，从而推动自动化教育内容生成和适应性学习环境的发展。

English

Automatic question generation (AQG) for mathematics education remains an elusive goal for Intelligent Tutoring Systems and educators. While pre-trained transformer-based language models have significantly advanced natural language generation, they often struggle to precisely control problem complexity and cognitive demands. In this paper, we introduce a collaborative multi-agent framework as a novel method of incorporating inference-time computation into AQG. This approach leverages multiple agents that iteratively refine generated question-answer pairs to better balance complexity and cognitive demand. We evaluate the generated questions on five meta-evaluation criteria: relevance, importance, clarity, difficulty matching, answerability, to assess the system's ability to control the required complexity and quality of the questions. Preliminary evaluations show that this collaborative multi-agent framework elevates the quality of generated educational content by fostering a more nuanced balance between cognitive challenge and clarity. These promising outcomes suggest that integrating collaborative multi-agent workflows can yield more controlled, pedagogically valuable content that can help advance automated educational content generation and adaptive learning environments.

多智能体协作框架下的数学问题生成

Multi-Agent Collaborative Framework For Math Problem Generation

摘要

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