Sep 18, 2026J/K move · Enter open · S save
H. D. E. Maduranga, S. K. Munasinghe, K. P. T. I. Weerasekara, Surangika Ranathunga+1
Dept. of Computer Science & Engineering, University of Moratuwa, Sri Lanka · School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand
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.