# A Classification of Artificial Intelligence Systems for Mathematics Education

arXiv: https://arxiv.org/abs/2107.06015 · HTML: https://mlsanity.com/paper/2107.06015/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 63 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Analysing Mathematical Reasoning Abilities of Neural Models](https://mlsanity.com/paper/1904.01557/) (2019)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [Deep Learning for Symbolic Mathematics](https://mlsanity.com/paper/1912.01412/) (2019)
- [Measuring abstract reasoning in neural networks](https://mlsanity.com/paper/1807.04225/) (2018)
- [Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics](https://mlsanity.com/paper/1706.04317/) (2017)
- [Deep Network Guided Proof Search](https://mlsanity.com/paper/1701.06972/) (2017)
- [Mathematical Reasoning in Latent Space](https://mlsanity.com/paper/1909.11851/) (2019)
- [Premise Selection and External Provers for HOL4](https://mlsanity.com/paper/1509.03534/) (2015)
- [Improving QED-Tutrix by Automating the Generation of Proofs](https://mlsanity.com/paper/1803.01468/) (2018)
- [Using ConceptNet to Teach Common Sense to an Automated Theorem Prover](https://mlsanity.com/paper/1912.12957/) (2019)

## Built on by (most cited first, 1 of 1)

- [Can the current trends of AI handle a full course of mathematics?](https://mlsanity.com/paper/2507.21664/) (2025)

## Abstract (arXiv)

This chapter provides an overview of the different Artificial Intelligence (AI) systems that are being used in contemporary digital tools for Mathematics Education (ME). It is aimed at researchers in AI and Machine Learning (ML), for whom we shed some light on the specific technologies that are being used in educational applications; and at researchers in ME, for whom we clarify: i) what the possibilities of the current AI technologies are, ii) what is still out of reach and iii) what is to be expected in the near future. We start our analysis by establishing a high-level taxonomy of AI tools that are found as components in digital ME applications. Then, we describe in detail how these AI tools, and in particular ML, are being used in two key applications, specifically AI-based calculators and intelligent tutoring systems. We finish the chapter with a discussion about student modeling systems and their relationship to artificial general intelligence.