# How Useful are Educational Questions Generated by Large Language Models?

arXiv: https://arxiv.org/abs/2304.06638 · HTML: https://mlsanity.com/paper/2304.06638/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models](https://mlsanity.com/paper/2201.05337/) (2022)
- [Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds](https://mlsanity.com/paper/2205.01845/) (2022)

## Built on by (most cited first, 23 of 23)

- [Large Language Models in Cybersecurity: State-of-the-Art](https://mlsanity.com/paper/2402.00891/) (2024)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [ChIRAAG: ChatGPT Informed Rapid and Automated Assertion Generation](https://mlsanity.com/paper/2402.00093/) (2024)
- [ChatNVD: Advancing Cybersecurity Vulnerability Assessment with Large Language Models](https://mlsanity.com/paper/2412.04756/) (2024)
- [Math Multiple Choice Question Generation via Human-Large Language Model Collaboration](https://mlsanity.com/paper/2405.00864/) (2024)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [Contrastive Learning for Knowledge-Based Question Generation in Large Language Models](https://mlsanity.com/paper/2409.13994/) (2024)
- [Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights](https://mlsanity.com/paper/2506.04851/) (2025)
- [Building a Domain-specific Guardrail Model in Production](https://mlsanity.com/paper/2408.01452/) (2024)
- [FairMonitor: A Dual-framework for Detecting Stereotypes and Biases in Large Language Models](https://mlsanity.com/paper/2405.03098/) (2024)
- [MathViz-E: A Case-study in Domain-Specialized Tool-Using Agents](https://mlsanity.com/paper/2407.17544/) (2024)
- [Scaling Evidence-based Instructional Design Expertise through Large Language Models](https://mlsanity.com/paper/2306.01006/) (2023)
- [On Few-Shot Prompting for Controllable Question-Answer Generation in Narrative Comprehension](https://mlsanity.com/paper/2404.02800/) (2024)
- [Comparison of Large Language Models for Generating Contextually Relevant Questions](https://mlsanity.com/paper/2407.20578/) (2024)
- [Small, Private Language Models as Teammates for Educational Assessment Design](https://mlsanity.com/paper/2605.15015/) (2026)
- [FairytaleQA Translated: Enabling Educational Question and Answer Generation in Less-Resourced Languages](https://mlsanity.com/paper/2406.04233/) (2024)
- [Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational Videos](https://mlsanity.com/paper/2505.01790/) (2025)
- [Releasing the CRaQAn (Coreference Resolution in Question-Answering): An open-source dataset and dataset creation methodology using instruction-following models](https://mlsanity.com/paper/2311.16338/) (2023)
- [Tell Me Who Your Students Are: GPT Can Generate Valid Multiple-Choice Questions When Students' (Mis)Understanding Is Hinted](https://mlsanity.com/paper/2505.05815/) (2025)
- [From Model to Classroom: Evaluating Generated MCQs for Portuguese with Narrative and Difficulty Concerns](https://mlsanity.com/paper/2506.15598/) (2025)
- [ActuBench: A Multi-Agent LLM Pipeline for Generation and Evaluation of Actuarial Reasoning Tasks](https://mlsanity.com/paper/2604.20273/) (2026)
- [Ask-E: An Environment for Calibrated Question Generation](https://mlsanity.com/paper/2608.06933/) (2026)
- [The Architect, the Adversary, and the Judge: Closed-Loop Generation of Standards-Aligned Assessment Items at Scale](https://mlsanity.com/paper/2609.26087/) (2026)

## Abstract (arXiv)

Controllable text generation (CTG) by large language models has a huge potential to transform education for teachers and students alike. Specifically, high quality and diverse question generation can dramatically reduce the load on teachers and improve the quality of their educational content. Recent work in this domain has made progress with generation, but fails to show that real teachers judge the generated questions as sufficiently useful for the classroom setting; or if instead the questions have errors and/or pedagogically unhelpful content. We conduct a human evaluation with teachers to assess the quality and usefulness of outputs from combining CTG and question taxonomies (Bloom's and a difficulty taxonomy). The results demonstrate that the questions generated are high quality and sufficiently useful, showing their promise for widespread use in the classroom setting.