多智能體協作框架於數學問題生成之應用

摘要

自動化數學教育問題生成（AQG）對於智能導學系統及教育工作者而言，仍是一個難以企及的目標。儘管基於預訓練轉換器的語言模型在自然語言生成方面取得了顯著進展，但它們往往難以精確控制問題的複雜性與認知需求。本文提出了一種協作式多智能體框架，作為將推理時計算融入AQG的新穎方法。該方法利用多個智能體迭代優化生成的問題-答案對，以更好地平衡複雜性與認知需求。我們從五個元評估標準——相關性、重要性、清晰度、難度匹配度及可解答性——對生成問題進行評估，以檢驗系統在控制所需複雜性與問題質量方面的能力。初步評估結果表明，此協作式多智能體框架通過促進認知挑戰與清晰度之間更細膩的平衡，提升了生成教育內容的質量。這些積極成果表明，整合協作式多智能體工作流程能夠產出更為可控、具教學價值的內容，從而推動自動化教育內容生成與適應性學習環境的發展。

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.

多智能體協作框架於數學問題生成之應用

Multi-Agent Collaborative Framework For Math Problem Generation

摘要

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