# An Automated Multiple-Choice Question Generation Using Natural Language Processing Techniques

arXiv: https://arxiv.org/abs/2103.14757 · HTML: https://mlsanity.com/paper/2103.14757/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Built on by (most cited first, 6 of 6)

- [ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models](https://mlsanity.com/paper/2410.16701/) (2024)
- [Interactive Storytelling for Children: A Case-study of Design and Development Considerations for Ethical Conversational AI](https://mlsanity.com/paper/2107.13076/) (2021)
- [Automatic Question & Answer Generation Using Generative Large Language Model (LLM)](https://mlsanity.com/paper/2508.19475/) (2025)
- [Adversarial and Safely Scaled Question Generation](https://mlsanity.com/paper/2210.09467/) (2022)
- [A Comparative and Experimental Study on Automatic Question Answering Systems and its Robustness against Word Jumbling](https://mlsanity.com/paper/2311.15513/) (2023)
- [BengaliMCQ: Automatic Generation and Answer Prediction of Academic Multiple-Choice Questions in a Low-Resource Language](https://mlsanity.com/paper/2608.15547/) (2026)

## Abstract (arXiv)

Automatic multiple-choice question generation (MCQG) is a useful yet challenging task in Natural Language Processing (NLP). It is the task of automatic generation of correct and relevant questions from textual data. Despite its usefulness, manually creating sizeable, meaningful and relevant questions is a time-consuming and challenging task for teachers. In this paper, we present an NLP-based system for automatic MCQG for Computer-Based Testing Examination (CBTE).We used NLP technique to extract keywords that are important words in a given lesson material. To validate that the system is not perverse, five lesson materials were used to check the effectiveness and efficiency of the system. The manually extracted keywords by the teacher were compared to the auto-generated keywords and the result shows that the system was capable of extracting keywords from lesson materials in setting examinable questions. This outcome is presented in a user-friendly interface for easy accessibility.