VISTA: Visual Integrated System for Tailored Automation in Math Problem Generation Using LLM

Jeongwoo Lee, Kwangsuk Park, Jihyeon Park
Proceedings of Large Foundation Models for Educational Assessment, PMLR 264:136-156, 2025.

Abstract

Generating accurate and consistent visual aids is a critical challenge in mathematics education, where visual representations like geometric shapes and functions play a pivotal role in enhancing student comprehension. This paper introduces a novel multi-agent framework that leverages Large Language Models (LLMs) to automate the creation of complex mathematical visualizations alongside coherent problem text. Our approach not only simplifies the generation of precise visual aids but also aligns these aids with the problem’s core mathematical concepts, improving both problem creation and assessment. By integrating multiple agents, each responsible for distinct tasks—such as numeric calculation, geometry validation, and visualization—our system delivers mathematically accurate and contextually relevant problems with visual aids. Evaluation across Geometry and Function problem types shows that our method significantly outperforms a baseline system in terms of text coherence, consistency, and relevance, while maintaining the essential geometrical and functional integrity of the original problems. Although some challenges remain in ensuring consistent visual outputs, our framework demonstrates the immense potential of LLMs in transforming the way educators generate and utilize visual aids in math education.

Cite this Paper

BibTeX
@InProceedings{pmlr-v264-lee25b, title = {VISTA: Visual Integrated System for Tailored Automation in Math Problem Generation Using LLM}, author = {Lee, Jeongwoo and Park, Kwangsuk and Park, Jihyeon}, booktitle = {Proceedings of Large Foundation Models for Educational Assessment}, pages = {136--156}, year = {2025}, editor = {Li, Sheng and Cui, Zhongmin and Lu, Jiasen and Harris, Deborah and Jing, Shumin}, volume = {264}, series = {Proceedings of Machine Learning Research}, month = {15--16 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v264/main/assets/lee25b/lee25b.pdf}, url = {https://proceedings.mlr.press/v264/lee25b.html}, abstract = {Generating accurate and consistent visual aids is a critical challenge in mathematics education, where visual representations like geometric shapes and functions play a pivotal role in enhancing student comprehension. This paper introduces a novel multi-agent framework that leverages Large Language Models (LLMs) to automate the creation of complex mathematical visualizations alongside coherent problem text. Our approach not only simplifies the generation of precise visual aids but also aligns these aids with the problem’s core mathematical concepts, improving both problem creation and assessment. By integrating multiple agents, each responsible for distinct tasks—such as numeric calculation, geometry validation, and visualization—our system delivers mathematically accurate and contextually relevant problems with visual aids. Evaluation across Geometry and Function problem types shows that our method significantly outperforms a baseline system in terms of text coherence, consistency, and relevance, while maintaining the essential geometrical and functional integrity of the original problems. Although some challenges remain in ensuring consistent visual outputs, our framework demonstrates the immense potential of LLMs in transforming the way educators generate and utilize visual aids in math education.} }
Endnote
%0 Conference Paper %T VISTA: Visual Integrated System for Tailored Automation in Math Problem Generation Using LLM %A Jeongwoo Lee %A Kwangsuk Park %A Jihyeon Park %B Proceedings of Large Foundation Models for Educational Assessment %C Proceedings of Machine Learning Research %D 2025 %E Sheng Li %E Zhongmin Cui %E Jiasen Lu %E Deborah Harris %E Shumin Jing %F pmlr-v264-lee25b %I PMLR %P 136--156 %U https://proceedings.mlr.press/v264/lee25b.html %V 264 %X Generating accurate and consistent visual aids is a critical challenge in mathematics education, where visual representations like geometric shapes and functions play a pivotal role in enhancing student comprehension. This paper introduces a novel multi-agent framework that leverages Large Language Models (LLMs) to automate the creation of complex mathematical visualizations alongside coherent problem text. Our approach not only simplifies the generation of precise visual aids but also aligns these aids with the problem’s core mathematical concepts, improving both problem creation and assessment. By integrating multiple agents, each responsible for distinct tasks—such as numeric calculation, geometry validation, and visualization—our system delivers mathematically accurate and contextually relevant problems with visual aids. Evaluation across Geometry and Function problem types shows that our method significantly outperforms a baseline system in terms of text coherence, consistency, and relevance, while maintaining the essential geometrical and functional integrity of the original problems. Although some challenges remain in ensuring consistent visual outputs, our framework demonstrates the immense potential of LLMs in transforming the way educators generate and utilize visual aids in math education.
APA
Lee, J., Park, K. & Park, J.. (2025). VISTA: Visual Integrated System for Tailored Automation in Math Problem Generation Using LLM. Proceedings of Large Foundation Models for Educational Assessment, in Proceedings of Machine Learning Research 264:136-156 Available from https://proceedings.mlr.press/v264/lee25b.html.

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