--- title: "Automated Question Generation for Science — Ashqar; 2024" description: "The paper addresses automatic creation of assessment questions for Arabic science material." authors: ["Mohammad Tami; Huthaifa I. Ashqar;", "Mohammed Elhenawy"] year: 2024 venue: "arXiv:2406.08520" source: "https://arxiv.org/abs/2406.08520" doi: "10.48550/arxiv.2406.08520" arxiv: "2406.08520" licence: "CC BY 4.0 (arXiv)" licence_url: "http://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/tami-2024-automated-question-generation-science-tests/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/tami-2024-automated-question-generation-science-tests/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/) / Automated Question Generation for Science Tests in Arabic Language Using NLP Techniques # Automated Question Generation for Science Tests in Arabic Language Using NLP Techniques **Authors:** Mohammad Tami; Huthaifa I. Ashqar;, [Mohammed Elhenawy](https://md.research.elamur.ai/free/en/authors/mohammed-elhenawy/) · **Year:** 2024 · **Venue:** arXiv:2406.08520 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://arxiv.org/abs/2406.08520) · [PDF](https://arxiv.org/pdf/2406.08520) · [DOI 10.48550/arxiv.2406.08520](https://doi.org/10.48550/arxiv.2406.08520) · [arXiv 2406.08520](https://arxiv.org/abs/2406.08520) · [OpenAlex](https://openalex.org/W4399695053) **Citations:** 0 citations (openalex-snapshot · 2026-10-14) · [1](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary The paper addresses automatic creation of assessment questions for Arabic science material. Earlier Arabic systems relied on rule-based transformation of declarative sentences and suffered from parsing errors, NER mistakes, and long, complex sentences. Most neural question generators also need a gold answer span alongside the context. The authors avoid that requirement with a three-stage pipeline: keyword and key-phrase extraction, question generation, and ranking. KeyBert picks candidate phrases by comparing n-gram embeddings with the whole passage, and part-of-speech tagging filters out off-topic or malformed candidates. Each surviving phrase, together with its passage, is fed to the pretrained AraT5 model, which writes a question targeting it. The data are 111 passages taken manually from Palestinian grade 11–12 biology textbooks. GPT-4 Turbo produced reference questions for the same passages, and the system's output scored 83.50% precision, 78.68% recall and 80.95% F1 against them. Expert raters gave an average of 4.2/5 (84%). The work shows that a small, answer-free pipeline can produce usable Arabic test questions with modest resources. ## Key points - The pipeline has three stages: key-phrase extraction (KeyBert), question generation (AraT5), and ranking of the generated questions. - KeyBert's candidates come from plain n-grams, so the authors add PoS tagging to keep phrases on topic. - The corpus contains 111 entries drawn from the biology textbooks that Palestinian students use in the eleventh and twelfth grades. Its most frequent words concern blood, cells, DNA and neurons. - Questions from OpenAI GPT-4 Turbo served as the benchmark. Against it the system scored P 83.50%, R 78.68% and F1 80.95%. - Field experts rated the questions 4.2 out of 5 on average (84%). ## Interesting ideas - The extracted key phrase serves as the answer the question must target, so no human-annotated answers are needed. — For Mova (back): Mova could generate questions from its own text graph, taking an entity or state from the world as the answer and producing the question that asks for it. - Statistical phrase candidates are cleaned with part-of-speech patterns, which removes off-topic or ill-formed n-grams. — For Mova (world): instead of n-grams, Mova can take noun-phrase subtrees from the UD tree as candidates and rank them by how central their entity is in the world of the text. - A large LLM's output is used as a cheap reference set when no gold questions exist, with human ratings added as a check. — For Mova (learn): LLM teacher questions could become a silver benchmark for Mova's question generator, provided negative controls are added, since agreeing with one teacher model does not prove the questions are correct. - Content selection is treated as its own step, in which the most representative pieces of a passage are chosen before any question is written. — For Mova (answer): a school-course test builder could first select the key facts of a textbook passage, then quiz Mova's students only on those facts, making the tests focused and explainable. - Generation is split into separate steps (select, generate, rank), so each step can be inspected and blamed when output is bad. — For Mova (back): the same split would let each generated question be traced to the selected fact and the ranking reason, which fits Mova's explainability requirement. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) Topics: [Part-of-speech tagging](https://md.research.elamur.ai/free/en/topics/part-of-speech-tagging/) ## Cited by Cited by 1 paper of our library cites it (of the 140884 whose bibliographies we read); each has a page here. - [Jenin at AraGenEval Shared Task: Parameter-Efficient Fine-Tuning and Layer-Wise Analysis of Arabic LLMs for Authorship Style Transfer and Classification](https://md.research.elamur.ai/free/en/papers/malhis-2025-jenin-arageneval-shared-task-parameter/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @misc{ashqar2024automated, title = {{Automated Question Generation for Science Tests in Arabic Language Using NLP Techniques}}, author = {Ashqar;, Mohammad Tami; Huthaifa I. and Elhenawy, Mohammed}, year = {2024}, doi = {10.48550/arxiv.2406.08520}, eprint = {2406.08520}, archivePrefix = {arXiv}, url = {https://doi.org/10.48550/arxiv.2406.08520} } ``` - [← previous Automated essay scoring in Arabic: a dataset and analysis of a BERT-based system](https://md.research.elamur.ai/free/en/papers/ghazawi-2024-automated-essay-scoring-arabic-dataset/) - [next → Aya 23: Open Weight Releases to Further Multilingual Progress](https://md.research.elamur.ai/free/en/papers/aryabumi-2024-aya-23-open-weight-releases/) - [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/) ## References 5 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. ## 1. M. Al-Ayyoub, A. Nuseir, K. Alsmearat, Y. Jararweh, and B. Gupta, “Deep learning for arabic nip: A survey,” Journal of computational science, vol. 26, pp. 522-531,2018. [find](https://scholar.google.com/scholar?q=%23%23+1%2E+M%2E+Al%2DAyyoub%2C+A%2E+Nuseir%2C+K%2E+Alsmearat%2C+Y%2E+Jararweh%2C+and+B%2E+Gupta%2C+%E2%80%9CDeep+learning+for+arabic+nip%3A+A+survey%2C%E2%80%9D+Journal+of+computational+science%2C+vol%2E+26%2C+pp%2E+522%2D531%2C2018%2E) 2. IEEE, 2015, pp. 1-3. 10. G. Kumar, R. E. Banchs, and L. F. D’Haro, “Revup: Automatic gap- fill question generation from educational texts,” in Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, 2015, pp. 154-161. 11. M. Majumder and S. K. Saha, “A system for generating multiple choice questions: With a novel approach for sentence selection,” in Proceedings of the 2nd workshop on natural language processing techniques for educational applications, 2015, pp. 64-72. 12. J. Park, H. Cho, and S.-g. Lee, “Automatic generation of multiple-choice fill-in-the-blank question using document embedding,” in Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27-30,2018, Proceedings, Part II19. [find](https://scholar.google.com/scholar?q=IEEE%2C+2015%2C+pp%2E+1%2D3%2E+10%2E+G%2E+Kumar%2C+R%2E+E%2E+Banchs%2C+and+L%2E+F%2E+D%E2%80%99Haro%2C+%E2%80%9CRevup%3A+Automatic+gap%2D+fill+question+generation+from+educational+texts%2C%E2%80%9D+in+Proceedings+of+the+Tenth+Workshop+on+Innovative+Use+of+NL) 3. Springer, 2018, pp. 261- 265. 13. R. Shah, D. Shah, and L. Kurup, “Automatic question generation for intelligent tutoring systems,” in 2017 2Nd international conference on communication systems, computing and IT applications (CSCITA). [find](https://scholar.google.com/scholar?q=Springer%2C+2018%2C+pp%2E+261%2D+265%2E+13%2E+R%2E+Shah%2C+D%2E+Shah%2C+and+L%2E+Kurup%2C+%E2%80%9CAutomatic+question+generation+for+intelligent+tutoring+systems%2C%E2%80%9D+in+2017+2Nd+international+conference+on+communication+systems%2C+compu) 4. IEEE, 2017, pp. 127-132. 14. J. Zhang and J. Takuma, “A kanji learning system based on automatic question sentence generation,” in 2015 international conference on asian language processing (IALP). [find](https://scholar.google.com/scholar?q=IEEE%2C+2017%2C+pp%2E+127%2D132%2E+14%2E+J%2E+Zhang+and+J%2E+Takuma%2C+%E2%80%9CA+kanji+learning+system+based+on+automatic+question+sentence+generation%2C%E2%80%9D+in+2015+international+conference+on+asian+language+processing+%28IALP%29%2E) 5. IEEE, 2015, pp. 144-147. [↗](https://doi.org/10.5281/zenodo.4461265)