# Automated Question Generation for Science Tests in Arabic Language Using NLP Techniques

arXiv: https://arxiv.org/abs/2406.08520 · HTML: https://mlsanity.com/paper/2406.08520/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 11 Jun 2024.

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

- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Multi-Stage Document Ranking with BERT](https://mlsanity.com/paper/1910.14424/) (2019)
- [PatternRank: Leveraging Pretrained Language Models and Part of Speech for Unsupervised Keyphrase Extraction](https://mlsanity.com/paper/2210.05245/) (2022)

## Built on by (most cited first, 3 of 3)

- [Leveraging Explainable AI for LLM Text Attribution: Differentiating Human-Written and Multiple LLMs-Generated Text](https://mlsanity.com/paper/2501.03212/) (2025)
- [Advancing Object Detection in Transportation with Multimodal Large Language Models (MLLMs): A Comprehensive Review and Empirical Testing](https://mlsanity.com/paper/2409.18286/) (2024)
- [Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarios](https://mlsanity.com/paper/2408.00948/) (2024)

## Abstract (arXiv)

Question generation for education assessments is a growing field within artificial intelligence applied to education. These question-generation tools have significant importance in the educational technology domain, such as intelligent tutoring systems and dialogue-based platforms. The automatic generation of assessment questions, which entail clear-cut answers, usually relies on syntactical and semantic indications within declarative sentences, which are then transformed into questions. Recent research has explored the generation of assessment educational questions in Arabic. The reported performance has been adversely affected by inherent errors, including sentence parsing inaccuracies, name entity recognition issues, and errors stemming from rule-based question transformation. Furthermore, the complexity of lengthy Arabic sentences has contributed to these challenges. This research presents an innovative Arabic question-generation system built upon a three-stage process: keywords and key phrases extraction, question generation, and subsequent ranking. The aim is to tackle the difficulties associated with automatically generating assessment questions in the Arabic language. The proposed approach and results show a precision of 83.50%, a recall of 78.68%, and an Fl score of 80.95%, indicating the framework high efficiency. Human evaluation further confirmed the model efficiency, receiving an average rating of 84%.