EDUMATH: Generating Standards-aligned Educational Math Word Problems

This paper develops EDUMATH, a system for generating math word problems aligned with K-12 education standards using LLMs, and evaluates over 11,000 generated problems with teacher annotations for solvability, accuracy, educational appropriateness, and standards alignment. The work includes a student study comparing performance and preference between LLM-generated and human-written problems.

Math word problems (MWPs) are critical K-12 educational tools, and customizing them to students'interests and ability levels can increase learning outcomes. However, teachers struggle to find time to customize MWPs for each student given large class sizes and increasing burnout. We propose that LLMs can support math education by generating MWPs customized to student interests and math education standards. To this end, we use a joint human expert-LLM judge approach to evaluate over 11,000 MWPs ge

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