Papers for

instructional content creators

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Math visuals generation framework improves elementary math problem learning

Math2Visual-X: A Modular Framework for Pedagogically Aligned Lower-Primary Math Visuals Generation

Abstract: Visual representations can help lower-primary learners understand Math Word Problems, but generating classroom-usable visuals remains difficult. Existing symbolic systems are controllable but limited in coverage, while end-to-end text-to-image systems often fail to satisfy exact mathematical constraints. This paper presents a symbolic visual generation framework for lower-primary MWP generation with broader problem coverage and more scalable asset generation. The framework includes an LLM-based routing layer, three worksheet-oriented generation modules, and two fallback mechanisms for open-world SVG asset acquisition. A human evaluation comparing Math2Visual-X with Stable Diffusion XL, Nano Banana, and GPT Image showed that the proposed method achieved the strongest overall performance. The results indicate that the framework offers a scalable and pedagogically grounded approach for automatic MWP visual generation.

Fri 18 SeptComputer Vision and Pattern RecognitionArtificial Intelligence

The gist

Understanding math word problems can be hard for young children, especially without helpful pictures. The authors created a new system called Math2Visual-X that automatically makes classroom-friendly math visuals for early learners. Their system combines smart text analysis and multiple modules to create accurate and useful pictures, outperforming other popular image generators. This approach helps teachers by providing better visuals that align with math lessons.

Open → 2609.22647v1