ArSciKids：面向小学科学的阿拉伯语课程对齐数据集发布
ArSciKids: Arabic Curriculum-Aligned Datasets for Scientific Experiment and Multiple-Choice Question Generation in Primary Education
做阿拉伯语教育 NLP 的可以看看，999 条科学实验加 2167 道选择题，还附了 10 个开源模型的 LoRA 微调评测结果，即将开源。
研究团队推出 ArSciKids，包含两个阿拉伯语教育数据集：999 条来自沙特、叙利亚、约旦官方小学科学教材的科学实验记录，以及从沙特 Manhaji 平台收集的 2,167 道选择题。数据覆盖 1-6 年级，全部为真实内容而非机器翻译或合成数据。团队用 LoRA 微调了 10 个从十亿参数以下到数十亿参数规模的开源模型，以 ROUGE、BLEU 和选择题准确率及五项标准的人工评估进行评测。结果显示不同模型规模和任务之间表现差异明显。
ArSciKids: Arabic Curriculum-Aligned Datasets for Scientific Experiment and Multiple-Choice Question Generation in Primary Education
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.