一个中国的小学科学问题数据集在解决问题的过程中生成
Dong Li1,2, Zhi Liu3,4, Chaodong Wen1,2
1Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China.
Scientific data
|January 22, 2026
概括
研究人员开发了一个中国科学问题 (CSQ) 数据集,以改进大型语言模型 (LLM) 以实现个性化科学教育. 该数据集有助于LLMs为小学学生生成年级级级别的适当的科学问题解决过程.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 科学教育科学教育教育.
背景情况:
- 大型语言模型 (LLM) 显示了个性化科学教育的前景.
- 一个关键的挑战是将LLM产生的解决问题的过程适应到特定的学生年级水平.
- 现有的数据集可能无法充分支持学年级特定的科学问题解决技能的发展.
研究的目的:
- 开发和引入中国科学问题 (CSQ) 数据集.
- 评估和增强法学士的科学解决问题的能力.
- 为了使LLMs能够生成等级级的适当的科学问题解决过程.
主要方法:
- 创建了一个包含12,000个高质量样本的中国科学问题 (CSQ) 数据集.
- 包括四个小学科目中的各种问题类型和学科属性.
- 设计了一个语言模型,对CSQ数据集进行微调,以模拟学生的思维过程.
主要成果:
- 该CSQ数据集,与其广泛的注释,有效地微调LLMs.
- 无论是自动的还是人类的评估都证实了LLM绩效的改善.
- 生成的解决问题的过程成功地与小学学生的年级水平保持一致.
结论:
- CSQ数据集是推动人工智能在科学教育中的宝贵资源.
- 士可以有效地接受培训,提供个性化的,适合学年级的科学问题解决支持.
- 这项工作有助于开发更适应性和有效的科学学习教育工具.
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