Proceedings of the 4th International Conference on Internet, Education and Information Technology (IEIT 2024)

<Previous Article In Volume
Next Article In Volume>

Interdisciplinary Curriculum Design and Quasi-Experimental AI-Study of Junior High School History from the Perspective of Core Competencies

Authors
Fangfang Liu1, 2, Yiyun Wang3, 4, Zeyu Zhang5, Linkai Zhu6, *, Guang Li7
1Capital Normal University High School, Beijing, China
2School of Education, University of Macau, Macao, China
3Student Affairs Office, Hebei University of Economics and Business, Shijiazhuang, China
4College of Education for the Future, Beijing Normal University, Zhuhai, People’s Republic of China
5Faculty of Data Science, City University of Macau, Macao, China
6School of Information Technology, Hebei University of Economics and Business, Shijiazhuang, China
7School of History, Capital Normal University, Beijing, China
*Corresponding author. Email: Linkai@hueb.edu.cn
Corresponding Author
Linkai Zhu
Available Online 21 November 2024.
DOI
10.2991/978-94-6463-574-4_71How to use a DOI?
Keywords
quasi-experimental; AI; Core Competencies; History Education
Abstract

This study investigates the development and quasi-experimental testing of an interdisciplinary curriculum in junior high school history education, viewed through the lens of core competencies and enhanced with deep learning technologies, specifically large language models. The primary objective is to evaluate the effectiveness of embedding core competencies—critical thinking, collaboration, communication, and creativity—within the history curriculum, and to extend these competencies using AI-driven analytical tools to boost students’ interdisciplinary insights and skills. Utilizing a quasi-experimental design, the study compared an experimental group, which engaged in the AI-enhanced interdisciplinary curriculum, to a control group that followed the traditional history curriculum. Data were gathered via pre- and post-intervention assessments, which included measures of academic performance, student surveys on engagement and perception, and qualitative interviews. The introduction of deep learning tools facilitated more sophisticated analysis and application of historical data, encouraging a more dynamic interaction with the material. Key findings demonstrate that students participating in the interdisciplinary curriculum significantly improved their ability to integrate and apply historical knowledge across various disciplines, showing superior core competencies relative to the control group. These results underline the potential of integrating sophisticated XLNet Model in curriculum design to foster enhanced educational outcomes in junior high school history education.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 4th International Conference on Internet, Education and Information Technology (IEIT 2024)
Series
Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date
21 November 2024
ISBN
978-94-6463-574-4
ISSN
2667-128X
DOI
10.2991/978-94-6463-574-4_71How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Fangfang Liu
AU  - Yiyun Wang
AU  - Zeyu Zhang
AU  - Linkai Zhu
AU  - Guang Li
PY  - 2024
DA  - 2024/11/21
TI  - Interdisciplinary Curriculum Design and Quasi-Experimental AI-Study of Junior High School History from the Perspective of Core Competencies
BT  - Proceedings of the 4th International Conference on Internet, Education and Information Technology (IEIT 2024)
PB  - Atlantis Press
SP  - 622
EP  - 628
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-574-4_71
DO  - 10.2991/978-94-6463-574-4_71
ID  - Liu2024
ER  -