# How Teachers Can Use Large Language Models and Bloom's Taxonomy to Create Educational Quizzes

arXiv: https://arxiv.org/abs/2401.05914 · HTML: https://mlsanity.com/paper/2401.05914/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 3 of 3)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs](https://mlsanity.com/paper/2205.00355/) (2022)
- [Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation](https://mlsanity.com/paper/2205.01730/) (2022)

## Built on by (most cited first, 17 of 17)

- [A Novel Approach to Scalable and Automatic Topic-Controlled Question Generation in Education](https://mlsanity.com/paper/2501.05220/) (2025)
- [An Automatic Question Usability Evaluation Toolkit](https://mlsanity.com/paper/2405.20529/) (2024)
- [Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research](https://mlsanity.com/paper/2506.01839/) (2025)
- [LLMs in Education: Novel Perspectives, Challenges, and Opportunities](https://mlsanity.com/paper/2409.11917/) (2024)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Standardizing Intelligence: Aligning Generative AI for Regulatory and Operational Compliance](https://mlsanity.com/paper/2503.04736/) (2025)
- [SlideBot: A Multi-Agent Framework for Generating Informative, Reliable, Multi-Modal Presentations](https://mlsanity.com/paper/2511.09804/) (2025)
- [FLAME: Self-Supervised Low-Resource Taxonomy Expansion using Large Language Models](https://mlsanity.com/paper/2402.13623/) (2024)
- [From Recall to Reasoning: Automated Question Generation for Deeper Math Learning through Large Language Models](https://mlsanity.com/paper/2505.11899/) (2025)
- [TrueReason: An Exemplar Personalised Learning System Integrating Reasoning with Foundational Models](https://mlsanity.com/paper/2502.10411/) (2025)
- [Enhancing Diversity of LLM-Generated Educational Tasks](https://mlsanity.com/paper/2512.23601/) (2025)
- [Mechanistic Interpretability of Cognitive Complexity in LLMs via Linear Probing using Bloom's Taxonomy](https://mlsanity.com/paper/2602.17229/) (2026)
- [Easy Come, Easy Go? Examining the Perceptions and Learning Effects of LLM-based Chatbot in the Context of Search-as-Learning](https://mlsanity.com/paper/2410.01396/) (2024)
- [Catching The Correct Answer Trap: Characterising AI Tutor Blind Spots When Analysing Student Reasoning](https://mlsanity.com/paper/2605.23925/) (2026)
- [Cognitively Layered Data Synthesis for Domain Adaptation of LLMs to Space Situational Awareness](https://mlsanity.com/paper/2603.09231/) (2026)
- [From Memorization to Creation: Evaluating the Cognitive Depth of LLM-Generated Educational Questions](https://mlsanity.com/paper/2606.18257/) (2026)
- [High-Order Question Generation in a Multilingual Educational Context](https://mlsanity.com/paper/2607.13901/) (2026)

## Abstract (arXiv)

Question generation (QG) is a natural language processing task with an abundance of potential benefits and use cases in the educational domain. In order for this potential to be realized, QG systems must be designed and validated with pedagogical needs in mind. However, little research has assessed or designed QG approaches with the input from real teachers or students. This paper applies a large language model-based QG approach where questions are generated with learning goals derived from Bloom's taxonomy. The automatically generated questions are used in multiple experiments designed to assess how teachers use them in practice. The results demonstrate that teachers prefer to write quizzes with automatically generated questions, and that such quizzes have no loss in quality compared to handwritten versions. Further, several metrics indicate that automatically generated questions can even improve the quality of the quizzes created, showing the promise for large scale use of QG in the classroom setting.