mlsanityData to 7 Oct 2026

Three Questions Concerning the Use of Large Language Models to Facilitate Mathematics Learning

arXiv 2310.13615 ↗First posted 20 Oct 2023cs.CLcited by 9

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Abstract (arXiv)

Due to the remarkable language understanding and generation abilities of large language models (LLMs), their use in educational applications has been explored. However, little work has been done on investigating the pedagogical ability of LLMs in helping students to learn mathematics. In this position paper, we discuss the challenges associated with employing LLMs to enhance students' mathematical problem-solving skills by providing adaptive feedback. Apart from generating the wrong reasoning processes, LLMs can misinterpret the meaning of the question, and also exhibit difficulty in understanding the given questions' rationales when attempting to correct students' answers. Three research questions are formulated.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 16 of its 39 references that are in the map. First posted on arXiv 20 Oct 2023.

Builds on

16 of 39 references.

References · 16

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4 more
  1. 2021Generate & Rank: A Multi-task Framework for Math Word Problemscited by 82
  2. 2022The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialoguescited by 78
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Cited by year

Citing papers per year: 2023: 1; 2024: 5; 2025: 3.

9 papers.

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  1. 2025Large Language Models Don't Make Sense of Word Problems. A Scoping Review from a Mathematics Education Perspective
  2. 2023Novice Learner and Expert Tutor: Evaluating Math Reasoning Abilities of Large Language Models with Misconceptions
  3. 2025Assessment of Evolving Large Language Models in Upper Secondary Mathematics
  4. 2025Simulating LLM-to-LLM Tutoring for Multilingual Math Feedback
  5. 2024Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process
  6. 2024LLM Reasoning Engine: Specialized Training for Enhanced Mathematical Reasoning
  7. 2025Large Language Models and Mathematical Reasoning Failures
  8. 2025A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematics