Artificial Intelligence Empowers the Integration of the Shuikoushan Workers’ Movement Spirit into the Teaching of Basic Principles of Marxism

Dan Luo

doi:10.63313/IJED.9043

Authors

Dan Luo School of Marxism, Hunan Institute of Technology, Hengyang 421002, China Author

DOI:

https://doi.org/10.63313/IJED.9043

Keywords:

Artificial Intelligence, Shuikoushan Workers' Movement Spirit, Basic Principles of Marxism, Red Resources, Teaching Integration

Abstract

The spirit of struggle, unity, and dedication embodied in the Shuikoushan Workers' Movement serves as a valuable resource for inheriting revolutionary heritage and fulfilling the fundamental mission of moral education in the Basic Principles of Marxism curriculum. However, the educational effectiveness of this spirit has been constrained by the gaps between the "narrative discourse" in historical materials of the Workers' Movement and the "theoretical discourse" of the curriculum, as well as the generational divides between historical contexts and students' cognitive perspectives. Artificial intelligence (AI), leveraging its technological advantages in natural language processing, virtual simulation, and data mining, offers a novel approach to address these challenges. This paper constructs an AI-enabled teaching pathway for integrating the Shuikoushan Workers' Movement spirit into the Basic Principles of Marxism curriculum from four dimensions: revitalizing historical materials, contextualized teaching, personalized interaction, and teaching effect feedback. While highlighting the potential risks of technological dependency, data security breaches, and loss of historical authenticity, the paper proposes targeted prevention and control strategies, including adhering to the "teacher-led, AI-assisted" principle, strengthening data governance, and upholding historical integrity. These measures are intended to achieve in-depth integration of the Workers' Movement spirit with the curriculum, enhance the teaching effectiveness and quality of revolutionary education, and provide theoretical references and practical insights for the integration of red resources into ideological and political theory courses.

References

[1] Long, X. M. (2021). A glorious chapter in the history of the Chinese workers’ movement: The Shuikoushan workers’ movement. CPC History Studies, (2), 113–117.

[2] Deng, Q. C. (2021). Hengyang City: Inheriting the spirit of Shuikoushan workers’ movement and drawing strength to forge ahead. Xiang Chao, (3), 57–59.

[3] Chai, S. C., Fan, G. H., & Wang, J. N. (2024). Research on the activation of archival historical materials from the perspective of archival art. Theoretical Observation, (12), 135–138.

[4] Dong, X. B., Han, Y., & Liu, J. F. (2025). A review of research on the history of the Chinese workers’ movement since the founding of the People’s Republic of China. Trade Union Theory Research (Journal of Shanghai Trade Union Management College), (3), 15–33.

[5] Li, D. D. (2026). Research on the reconstruction of teaching content under the collaboration of large language models and knowledge graphs—Taking the course “Basic Principles of Marxism” as an example. Heilongjiang Education (Theory and Practice), (2), 1–10.

[6] Peng, Y. Q., Rao, Y., & Li, W. Y. (2026). The “intelligent change” and “qualitative change” of ideological and political education empowered by generative artificial intelligence: Value, risks and paths. Journal of Ethnic Education Research, 1–11.

[7] Xie, J. (2025). Artificial intelligence and Chinese modernization: Realistic picture and future prospect—A review of the “2025 National Annual Conference on Marxist Principles Research and Academic Seminar on Artificial Intelligence and Chinese Modernization”. Studies on Marxism, (11), 152–154.

[8] Yan, F. J. (2025). The inherent requirements and effective paths for teaching the “Basic Principles of Marxism” course in depth, thoroughly and vividly. Studies in Marxist Theory, 11(4), 119–127.

[9] Bao, W. J. (2025). An analysis of teaching methods for the “Basic Principles of Marxism” course based on classroom design. Leading Journal of Ideological & Theoretical Education, (4), 153–159.

[10] Zhang, S., & Zhou, Q. (2025). Precise communication of red culture in the age of artificial intelligence: Opportunities, models and strategies. Journal of Hainan University (Humanities and Social Sciences), 43(5), 159–165.

[11] Han, M. (2025). Thoughts on the construction and application of red publishing knowledge graphs empowered by artificial intelligence.China Publishing Journal, (11), 39–44.

[12] Wang, S. X., Yang, H., Feng, C., & Li, X. (2025). Research on multimodal sentiment analysis algorithm integrating counterfactual reasoning. Computer Engineering and Applications, 61(15), 199–208.

[13] Zhang, Q., Yang, M., Shi, Q., Wang, R. R., & Long, T. T. (2022). Virtual reality empowering the training of future teachers: Logic, scenarios and reconstruction. China Distance Education, (8), 9–17.

[14] Wang, Y. D. (2026). Dialectical examination of artificial intelligence development and exploration of scientific thinking. Southeast Academic Research, 1–11.

[15] Zhang, G. Q., & Duan, G. H. (2026). From “general” to “vertical”: The customized application of artificial intelligence ideological and political large models. Journal of Hohai University (Philosophy and Social Sciences Edition), 1–8.

[16] Li, H. P., & Wang, J. (2026). The triple transformation, risk spectrum and governance of artificial intelligence integration into education. Journal of Ethnic Education Research, 1–10.