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Prof. Paul Sudnik

Munich University of Applied Sciences, Germany

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Home > Archive > 2025 > Volume 15, Number 5, 2025
IJSSH 2025 Vol.15(5): 184-188
doi: 10.18178/ijssh.2025.15.5.1262

Interdisciplinary Integrated Curriculum Design, Evaluation and Improvement Based on Causal Inference

Jing Zhou and Bin Duan*
School of Automation and Electronic Information, Xiangtan University, Xiangtan City, Hunan Province, China
Email: 1353478786@qq.com (J.Z.); db61850@163.com (B.D.)
*Corresponding author

Manuscript received April 29, 2025; accepted July 28, 2025; published October 28, 2025.

Abstract—To meet the demands of the information and intelligent era and cultivate students’ ability to integrate diverse knowledge and skills to solve practical problems, interdisciplinary integrated curriculum design has become an important paradigm in education and teaching. However, due to challenges such as the interference of confounding factors, static evaluation systems, and the lack of dynamic optimization mechanisms, the design, assessment, and improvement of interdisciplinary integrated courses are difficult to be effectively supported. For this reason, based on the theory of causal science, this paper proposes the method of “introducing mediators to eliminate confounding”, adopts the structural equation model and combines counterfactual inference to calculate the causal effect, in order to analyze the intervention path. Taking the cognitive computing course as a case, a causal model including AI-enhanced research, the application of metacognitive strategies and higher-order abilities is constructed, and an empirical analysis is conducted through teaching data. The results show that the overall intervention effect is positive, and teaching intervention mainly acts on the improvement of students’ abilities through mediating variables. The research provides a scientific path for the quantitative assessment of educational intervention effects and offers theoretical and methodological support for the dynamic optimization and precise improvement of the curriculum.

Keywords—interdisciplinary integration, causal inference, eliminate confounding, curriculum improvement

Cite: Jing Zhou and Bin Duan, "Interdisciplinary Integrated Curriculum Design, Evaluation and Improvement Based on Causal Inference," International Journal of Social Science and Humanity, vol. 15, no. 5, pp. 184-188, 2025.

Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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