arXiv cs.CL· Taghrid Housari, Massa Rankoussi, Jafar Abdul Majeed, Wael Tarabishi, Khloud Al Jallad, Nada Ghneim·· 11 小时前AI 评分21
ArSciKids：面向阿拉伯语小学科学教育的课程对齐数据集，用于实验与选择题生成
ArSciKids: Arabic Curriculum-Aligned Datasets for Scientific Experiment and Multiple-Choice Question Generation in Primary Education
AI 导读
ArSciKids 发布两个阿拉伯语课程对齐数据集：999 条来自沙特、叙利亚、约旦官方小学科学教材的人工整理实验记录，以及 2,167 道来自沙特 Manhaji 平台的选择题，均覆盖 1-6 年级且为真实教育内容而非机器翻译或合成数据。
正文 · 原文
Abstract:Despite growing interest in Arabic natural language processing, curriculum-aligned educational resources remain scarce compared with English, and few public datasets for Arabic curriculum content with structured, generation-ready annotations are available, particularly for primary-school science. We introduce ArSciKids, a collection of two Arabic datasets designed for curriculum-aligned educational content generation: (1) a scientific-experiments dataset containing 999 manually curated records from official primary-school science textbooks in Saudi Arabia, Syria, and Jordan, and (2) an assessment-question dataset containing 2,167 multiple-choice questions collected from the Saudi Manhaji educational platform. The experiment records are structured with titles, objectives, tools, procedures, observations, conclusions, keywords, and grade levels, while the assessment records contain lesson references, questions, four answer options, correct answers, and explanations. Both datasets cover grades 1-6 and consist of real-life Arabic educational content rather than machine-translated or synthetically generated material. We assess the usefulness of these resources for generation by fine-tuning ten open models spanning sub-billion to several-billion parameter scales using LoRA. Models are evaluated using automatic metrics, including ROUGE and BLEU, and answer accuracy for multiple-choice generation. Moreover, we evaluate the results using a five-criterion human evaluation protocol. The results show substantial differences across model scales and tasks. ArSciKids will be publicly available, and we hope that it provides a manually curated, structured foundation for research on Arabic educational NLP, curriculum-aware generation, and low-resource language modeling.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.01191 [cs.CL] |
| (or arXiv:2611.01191v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.01191 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Khloud Al Jallad [view email]
[v1]
Sat, 31 Oct 2026 18:44:39 UTC (1,224 KB)
来源：arXiv cs.CL · arxiv.org