Multi-Agent Collaborative Framework For Math Problem Generation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909889603829760 |
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| author | Karbasi, Kia Hong, Kevin Samadi, Mohammad Amin Pottie, Gregory |
| author_facet | Karbasi, Kia Hong, Kevin Samadi, Mohammad Amin Pottie, Gregory |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03958 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Agent Collaborative Framework For Math Problem Generation Karbasi, Kia Hong, Kevin Samadi, Mohammad Amin Pottie, Gregory Multiagent Systems Computation and Language Human-Computer Interaction I.2.11; I.2.6; K.3.1 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. |
| title | Multi-Agent Collaborative Framework For Math Problem Generation |
| topic | Multiagent Systems Computation and Language Human-Computer Interaction I.2.11; I.2.6; K.3.1 |
| url | https://arxiv.org/abs/2511.03958 |