Generation of Visual Representations for Multi-Modal Mathematical Knowledge


  • Lianlong Wu University of Cambridge
  • Seewon Choi University of Cambridge
  • Daniel Raggi University of Cambridge
  • Aaron Stockdill University of Sussex
  • Grecia Garcia Garcia University of Sussex
  • Fiorenzo Colarusso University of Sussex
  • Peter C.H. Cheng University of Sussex
  • Mateja Jamnik University of Cambridge



Artificial Intelligence, Cognitive systems, Educational software and hardware tools for AI, Intelligent tutoring systems


In this paper we introduce MaRE, a tool designed to generate representations in multiple modalities for a given mathematical problem while ensuring the correctness and interpretability of the transformations between different representations. The theoretical foundation for this tool is Representational Systems Theory (RST), a mathematical framework for studying the structure and transformations of representations. In MaRE’s web front-end user interface, a set of probability equations in Bayesian Notation can be rigorously transformed into Area Diagrams, Contingency Tables, and Probability Trees with just one click, utilising a back-end engine based on RST. A table of cognitive costs, based on the cognitive Representational Interpretive Structure Theory (RIST), that a representation places on a particular profile of user is produced at the same time. MaRE is general and domain independent, applicable to other representations encoded in RST. It may enhance mathematical education and research, facilitating multi-modal knowledge representation and discovery.




How to Cite

Wu, L., Choi, S., Raggi, D., Stockdill, A., Garcia Garcia, G., Colarusso, F., Cheng, P. C., & Jamnik, M. (2024). Generation of Visual Representations for Multi-Modal Mathematical Knowledge. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23850-23852.