# Math Multiple Choice Question Generation via Human-Large Language Model Collaboration

arXiv: https://arxiv.org/abs/2405.00864 · HTML: https://mlsanity.com/paper/2405.00864/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 1 May 2024.

## Builds on (most cited first, 6 of 6)

- [RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning](https://mlsanity.com/paper/2305.14502/) (2023)
- [How Useful are Educational Questions Generated by Large Language Models?](https://mlsanity.com/paper/2304.06638/) (2023)
- [Improving the Validity of Automatically Generated Feedback via Reinforcement Learning](https://mlsanity.com/paper/2403.01304/) (2024)
- [Synthesizing Tasks for Block-based Programming](https://mlsanity.com/paper/2006.16913/) (2020)
- [Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models](https://mlsanity.com/paper/2404.02124/) (2024)
- [Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank](https://mlsanity.com/paper/2306.08847/) (2023)

## Built on by (most cited first, 9 of 9)

- [Tool-Planner: Task Planning with Clusters across Multiple Tools](https://mlsanity.com/paper/2406.03807/) (2024)
- [Leveraging LLM-Respondents for Item Evaluation: a Psychometric Analysis](https://mlsanity.com/paper/2407.10899/) (2024)
- [DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions](https://mlsanity.com/paper/2406.19356/) (2024)
- [From Text to Visuals: Using LLMs to Generate Math Diagrams with Vector Graphics](https://mlsanity.com/paper/2503.07429/) (2025)
- [Enhancing Diversity of LLM-Generated Educational Tasks](https://mlsanity.com/paper/2512.23601/) (2025)
- [Cognitively Diverse Multiple-Choice Question Generation: A Hybrid Multi-Agent Framework with Large Language Models](https://mlsanity.com/paper/2602.03704/) (2026)
- [EvalYaks: Instruction Tuning Datasets and LoRA Fine-tuned Models for Automated Scoring of CEFR B2 Speaking Assessment Transcripts](https://mlsanity.com/paper/2408.12226/) (2024)
- [FEAT: A Preference Feedback Dataset through a Cost-Effective Auto-Generation and Labeling Framework for English AI Tutoring](https://mlsanity.com/paper/2506.19325/) (2025)
- [EduAgentQG: Multi-Agent Personalized Mathematics Question Generation with Explicit Diversity and Objective-Aware Evaluation](https://mlsanity.com/paper/2511.11635/) (2025)

## Abstract (arXiv)

Multiple choice questions (MCQs) are a popular method for evaluating students' knowledge due to their efficiency in administration and grading. Crafting high-quality math MCQs is a labor-intensive process that requires educators to formulate precise stems and plausible distractors. Recent advances in large language models (LLMs) have sparked interest in automating MCQ creation, but challenges persist in ensuring mathematical accuracy and addressing student errors. This paper introduces a prototype tool designed to facilitate collaboration between LLMs and educators for streamlining the math MCQ generation process. We conduct a pilot study involving math educators to investigate how the tool can help them simplify the process of crafting high-quality math MCQs. We found that while LLMs can generate well-formulated question stems, their ability to generate distractors that capture common student errors and misconceptions is limited. Nevertheless, a human-AI collaboration has the potential to enhance the efficiency and effectiveness of MCQ generation.