mlsanityData to 14 Oct 2026

From Recall to Reasoning: Automated Question Generation for Deeper Math Learning through Large Language Models

arXiv 2505.11899 ↗First posted 17 May 2025cs.AIcited by 3

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Abstract (arXiv)

Educators have started to turn to Generative AI (GenAI) to help create new course content, but little is known about how they should do so. In this project, we investigated the first steps for optimizing content creation for advanced math. In particular, we looked at the ability of GenAI to produce high-quality practice problems that are relevant to the course content. We conducted two studies to: (1) explore the capabilities of current versions of publicly available GenAI and (2) develop an improved framework to address the limitations we found. Our results showed that GenAI can create math problems at various levels of quality with minimal support, but that providing examples and relevant content results in better quality outputs. This research can help educators decide the ideal way to adopt GenAI in their workflows, to create more effective educational experiences for students.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 15 of its 32 references that are in the map. First posted on arXiv 17 May 2025.

Builds on

15 of 32 references.

References · 15

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
  3. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  5. 2023Mathematical Capabilities of ChatGPTcited by 268
  6. 2024Searching for Best Practices in Retrieval-Augmented Generationcited by 83
  7. 2023Computing Education in the Era of Generative AIcited by 52
  8. 2023Hot or Cold? Adaptive Temperature Sampling for Code Generation with Large Language Modelscited by 38
  9. 2024Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluationcited by 29
  10. 2024Bringing Generative AI to Adaptive Learning in Educationcited by 24
  11. 2022Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needscited by 19
  12. 2024How Teachers Can Use Large Language Models and Bloom's Taxonomy to Create Educational Quizzescited by 17
3 more
  1. 2023Conic10K: A Challenging Math Problem Understanding and Reasoning Datasetcited by 5
  2. 2024A Survey on Neural Question Generation: Methods, Applications, and Prospectscited by 5
  3. 2024A Review of Data Mining in Personalized Education: Current Trends and Future Prospectscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Enhancing Diversity of LLM-Generated Educational Taskscited by 2
  2. 2026A self-evolving multi-role collaborative framework with fine-grained difficulty guidance for innovative mathematical problem generationcited by 0
  3. 2026From Execution to Education: A Bloom-Aligned Framework for Measuring Educational Control in LLMscited by 0