# Automated Educational Question Generation at Different Bloom's Skill Levels using Large Language Models: Strategies and Evaluation

arXiv: https://arxiv.org/abs/2408.04394 · HTML: https://mlsanity.com/paper/2408.04394/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 8 Aug 2024.

## Builds on (most cited first, 11 of 11)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [Reframing Instructional Prompts to GPTk's Language](https://mlsanity.com/paper/2109.07830/) (2021)
- [A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models](https://mlsanity.com/paper/2201.05337/) (2022)
- [Generative Language Models for Paragraph-Level Question Generation](https://mlsanity.com/paper/2210.03992/) (2022)
- [Scalable Educational Question Generation with Pre-trained Language Models](https://mlsanity.com/paper/2305.07871/) (2023)
- [Harnessing LLMs in Curricular Design: Using GPT-4 to Support Authoring of Learning Objectives](https://mlsanity.com/paper/2306.17459/) (2023)

## Built on by (most cited first, 29 of 29)

- [Can Vision-Language Models Evaluate Handwritten Math?](https://mlsanity.com/paper/2501.07244/) (2025)
- [Generating AI Literacy MCQs: A Multi-Agent LLM Approach](https://mlsanity.com/paper/2412.00970/) (2024)
- [Multi-Agent Collaborative Framework For Math Problem Generation](https://mlsanity.com/paper/2511.03958/) (2025)
- [Evaluating LLM-Generated Q&A Test: a Student-Centered Study](https://mlsanity.com/paper/2505.06591/) (2025)
- [From Recall to Reasoning: Automated Question Generation for Deeper Math Learning through Large Language Models](https://mlsanity.com/paper/2505.11899/) (2025)
- [Cognitively Diverse Multiple-Choice Question Generation: A Hybrid Multi-Agent Framework with Large Language Models](https://mlsanity.com/paper/2602.03704/) (2026)
- [Small, Private Language Models as Teammates for Educational Assessment Design](https://mlsanity.com/paper/2605.15015/) (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)
- [Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational Videos](https://mlsanity.com/paper/2505.01790/) (2025)
- [Rate-Distortion Guided Knowledge Graph Construction from Lecture Notes Using Gromov-Wasserstein Optimal Transport](https://mlsanity.com/paper/2511.14595/) (2025)
- [Generate-Then-Validate: A Novel Question Generation Approach Using Small Language Models](https://mlsanity.com/paper/2512.10110/) (2025)
- [Automatic Question Generation for Intuitive Learning Utilizing Causal Graph Guided Chain of Thought Reasoning](https://mlsanity.com/paper/2601.06098/) (2026)
- [Reflecting in the Reflection: Integrating a Socratic Questioning Framework into Automated AI-Based Question Generation](https://mlsanity.com/paper/2601.14798/) (2026)
- [CODE-GEN: A Human-in-the-Loop RAG-Based Agentic AI System for Multiple-Choice Question Generation](https://mlsanity.com/paper/2604.03926/) (2026)
- [LLM-as-Judge in Education: A Curriculum-Grounded Marking Pipeline](https://mlsanity.com/paper/2606.17507/) (2026)
- [The Impact of Item-Writing Flaws on Difficulty and Discrimination in Item Response Theory](https://mlsanity.com/paper/2503.10533/) (2025)
- [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)
- [ClickSight: Interpreting Student Clickstreams to Reveal Insights on Learning Strategies via LLMs](https://mlsanity.com/paper/2505.15410/) (2025)
- [From Model to Classroom: Evaluating Generated MCQs for Portuguese with Narrative and Difficulty Concerns](https://mlsanity.com/paper/2506.15598/) (2025)
- [Automated Domain Question Mapping (DQM) with Educational Learning Materials](https://mlsanity.com/paper/2601.07062/) (2026)
- [Hallucination-Free Automatic Question & Answer Generation for Intuitive Learning](https://mlsanity.com/paper/2601.14280/) (2026)
- [Cognitively Layered Data Synthesis for Domain Adaptation of LLMs to Space Situational Awareness](https://mlsanity.com/paper/2603.09231/) (2026)
- [Optimizing Coverage and Difficulty in Reinforcement Learning for Quiz Composition](https://mlsanity.com/paper/2603.27695/) (2026)
- [KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing](https://mlsanity.com/paper/2605.23933/) (2026)
- [Cross-Dataset Bloom Question Classification: Supervised Models and Prompted LLMs](https://mlsanity.com/paper/2606.13684/) (2026)
- [High-Order Question Generation in a Multilingual Educational Context](https://mlsanity.com/paper/2607.13901/) (2026)
- [ConnectED: A Curriculum-Aligned AI System for Vietnamese Instructional Lesson Planning and Student Learning](https://mlsanity.com/paper/2607.28647/) (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)
- [Evaluation of pre-trained models for pedagogical assessment of novel AI-assisted educational questions](https://mlsanity.com/paper/2609.27749/) (2026)

## Abstract (arXiv)

Developing questions that are pedagogically sound, relevant, and promote learning is a challenging and time-consuming task for educators. Modern-day large language models (LLMs) generate high-quality content across multiple domains, potentially helping educators to develop high-quality questions. Automated educational question generation (AEQG) is important in scaling online education catering to a diverse student population. Past attempts at AEQG have shown limited abilities to generate questions at higher cognitive levels. In this study, we examine the ability of five state-of-the-art LLMs of different sizes to generate diverse and high-quality questions of different cognitive levels, as defined by Bloom's taxonomy. We use advanced prompting techniques with varying complexity for AEQG. We conducted expert and LLM-based evaluations to assess the linguistic and pedagogical relevance and quality of the questions. Our findings suggest that LLms can generate relevant and high-quality educational questions of different cognitive levels when prompted with adequate information, although there is a significant variance in the performance of the five LLms considered. We also show that automated evaluation is not on par with human evaluation.