SSC-BanglaTutor:一个与课程一致的孟加拉语数据集,用于智能辅导系统

Eshraque Jabid Ifti¹, Fihab Ifty¹, Mehadi Hasan¹

¹Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka 1229, Bangladesh.

Data in brief

|March 11, 2026

概括

一个新的孟加拉语数据集包含11,286个基于提示的科学问题,有助于为孟加拉国中学证书 (SSC) 课程提供AI辅导. 本资源支持个性化学习和低资源自然语言处理 (NLP) 应用程序.

科学领域:

教育技术的教育技术
自然语言处理 (NLP) 是一种自然语言处理.
教育中的人工智能 (AI)

背景情况:

开发人工智能辅导系统需要专门的数据集来进行有效的微调.
现有的资源往往缺乏语言多样性和课程特异性,以满足区域教育需求.
孟加拉国中学证书 (SSC) 科学课程提供了一个独特的教育背景.

研究的目的:

引入一种新的孟加拉语数据集,用于基于人工智能的提示辅导系统.
支持对孟加拉国教育应用的大型语言模型 (LLM) 的微调.
在智能辅导系统中增强个性化的反和学习者建模.

主要方法:

从政府教科书和考试材料中手动创建了11286个基于提示的问答条目.
包括生物,化学和物理问题与SSC课程一致.
开发候选答案,其中有一个正确的选项和几个可信的错误选项,以及一个趋同得分.

主要成果:

数据集包括4859个生物学,3034个化学和3393个物理问题.
每个项目都包括一个趋同得分,以衡量线索的有效性和学生的进步.
使用精选的英语术语进行UTF-8编码可确保可访问性和NLP应用价值.

结论:

该数据集为开发语言包容性和教育有效的智能辅导系统提供了坚实的基础.
它促进了个性化的学习体验,并提供了对学生学习轨迹的见解.
该资源对本地学习者和低资源的NLP研究都是有价值的.

关键词:

一个辅导员,一个导师.孟加拉语数据集提示暗示暗示暗示它是它的ITS.在法学士 (LLM) 课程中.在RAG RAG的基础上.在SSC中,SSC是SSC.科学科学科学科学科学科学科学中等教育中等教育.

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