Multi-Agent Collaborative Framework For Math Problem Generation

TL;DR

This paper presents a novel multi-agent framework for automatic math question generation that improves control over question complexity and cognitive demands, enhancing educational content quality.

Contribution

It introduces a collaborative multi-agent approach that refines question-answer pairs to better balance difficulty and clarity in automated question generation.

Findings

Improved relevance and clarity of generated questions.

Enhanced control over question difficulty matching.

Positive preliminary evaluation results.

Abstract

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.