# Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs

arXiv: https://arxiv.org/abs/2205.00355 · HTML: https://mlsanity.com/paper/2205.00355/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 84 references that are in the map.
- First posted on arXiv 30 Apr 2022.

## Builds on (most cited first, 18 of 18)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Controllable Sentence Simplification](https://mlsanity.com/paper/1910.02677/) (2019)
- [Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering](https://mlsanity.com/paper/1909.06356/) (2019)
- [Semantic Graphs for Generating Deep Questions](https://mlsanity.com/paper/2004.12704/) (2020)
- [Controllable Open-ended Question Generation with A New Question Type Ontology](https://mlsanity.com/paper/2107.00152/) (2021)
- [Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting](https://mlsanity.com/paper/2105.11698/) (2021)
- [ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation](https://mlsanity.com/paper/2101.08382/) (2021)
- [Improving Unsupervised Question Answering via Summarization-Informed Question Generation](https://mlsanity.com/paper/2109.07954/) (2021)
- [Multi-hop Question Generation with Graph Convolutional Network](https://mlsanity.com/paper/2010.09240/) (2020)
- [Designing for human-AI complementarity in K-12 education](https://mlsanity.com/paper/2104.01266/) (2021)
- [Question-type Driven Question Generation](https://mlsanity.com/paper/1909.00140/) (2019)
- [Data-Driven Feedback Generation for Introductory Programming Exercises](https://mlsanity.com/paper/1711.07148/) (2017)
- [Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model](https://mlsanity.com/paper/2110.06560/) (2021)
- [Question Generation from a Knowledge Base with Web Exploration](https://mlsanity.com/paper/1610.03807/) (2016)

## Built on by (most cited first, 19 of 19)

- [ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions](https://mlsanity.com/paper/2303.06594/) (2023)
- [Training Turn-by-Turn Verifiers for Dialogue Tutoring Agents: The Curious Case of LLMs as Your Coding Tutors](https://mlsanity.com/paper/2502.13311/) (2025)
- [How Teachers Can Use Large Language Models and Bloom's Taxonomy to Create Educational Quizzes](https://mlsanity.com/paper/2401.05914/) (2024)
- [Generative AI for Education (GAIED): Advances, Opportunities, and Challenges](https://mlsanity.com/paper/2402.01580/) (2024)
- [Evaluating the Knowledge Dependency of Questions](https://mlsanity.com/paper/2211.11902/) (2022)
- [Towards Diverse and Effective Question-Answer Pair Generation from Children Storybooks](https://mlsanity.com/paper/2306.06605/) (2023)
- [Towards Enriched Controllability for Educational Question Generation](https://mlsanity.com/paper/2306.14917/) (2023)
- [Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels](https://mlsanity.com/paper/2409.12425/) (2024)
- [QG-SMS: Enhancing Test Item Analysis via Student Modeling and Simulation](https://mlsanity.com/paper/2503.05888/) (2025)
- [From Recall to Reasoning: Automated Question Generation for Deeper Math Learning through Large Language Models](https://mlsanity.com/paper/2505.11899/) (2025)
- [From Objectives to Questions: A Planning-based Framework for Educational Mathematical Question Generation](https://mlsanity.com/paper/2506.00963/) (2025)
- [Ask To The Point: Open-Domain Entity-Centric Question Generation](https://mlsanity.com/paper/2310.14126/) (2023)
- [Power-up! What Can Generative Models Do for Human Computation Workflows?](https://mlsanity.com/paper/2307.02243/) (2023)
- [Surgment: Segmentation-enabled Semantic Search and Creation of Visual Question and Feedback to Support Video-Based Surgery Learning](https://mlsanity.com/paper/2402.17903/) (2024)
- [EduNLP: Towards a Unified and Modularized Library for Educational Resources](https://mlsanity.com/paper/2406.01276/) (2024)
- [Free Lunch for User Experience: Crowdsourcing Agents for Scalable User Studies](https://mlsanity.com/paper/2505.22981/) (2025)
- [Advancing Question Generation with Joint Narrative and Difficulty Control](https://mlsanity.com/paper/2506.06812/) (2025)
- [Generating News-Centric Crossword Puzzles As A Constraint Satisfaction and Optimization Problem](https://mlsanity.com/paper/2308.04688/) (2023)
- [From Model to Classroom: Evaluating Generated MCQs for Portuguese with Narrative and Difficulty Concerns](https://mlsanity.com/paper/2506.15598/) (2025)

## Abstract (arXiv)

NLP-powered automatic question generation (QG) techniques carry great pedagogical potential of saving educators' time and benefiting student learning. Yet, QG systems have not been widely adopted in classrooms to date. In this work, we aim to pinpoint key impediments and investigate how to improve the usability of automatic QG techniques for educational purposes by understanding how instructors construct questions and identifying touch points to enhance the underlying NLP models. We perform an in-depth need finding study with 11 instructors across 7 different universities, and summarize their thought processes and needs when creating questions. While instructors show great interests in using NLP systems to support question design, none of them has used such tools in practice. They resort to multiple sources of information, ranging from domain knowledge to students' misconceptions, all of which missing from today's QG systems. We argue that building effective human-NLP collaborative QG systems that emphasize instructor control and explainability is imperative for real-world adoption. We call for QG systems to provide process-oriented support, use modular design, and handle diverse sources of input.