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Article
Affiliation(s)

University of Shanghai for Science and Technology, Shanghai, China

ABSTRACT

With the quick development in AIGC field, it has shown a great prospect in education, especially in Academic Competitions (AC). AC plays an important role in cultivating Innovation Thinking People (ITP). It can stimulate their innovation thinking and develop their comprehensive capabilities. In our current society, the traditional tutoring methods for AC have exposed some inherent shortcomings, which include that the personalized demands can’t be satisfied, the educational resources are hard to integrated, etc. The main point of the article is to investigate the innovation thinking cultivation mechanism, analyzing the deep reasons why AIGC can help enhance the abilities of the contestants, focusing on developing personalized dynamic question banks, emulating the real combat environments, and making customized learning plans. Through the combination of theory and practice, we have built an Input-Enhance-Output (IEO) model for cultivating the innovative thinking, where AIGC can help. Using Chi-square Test of Independence, we proved the strong correlation between the application of AIGC and cultivation of ITP. This research not only contributes the insights on rebuilding the AC tutoring model, but also offers perspectives for developing education technology in future.

KEYWORDS

AIGC educational technology, academic competitions, elite talent cultivation, innovative thinking, personalized learning, input-processing-output model

Cite this paper

ZHANG Yusen, LIU Cheng. (2025). Research on the Innovation Thinking Cultivation Mechanism Based on AIGC From the Perspective of Academic Competitions. US-China Education Review A, November 2025, Vol. 15, No. 11, 745-748.

References

Huang, A. H., & Zhang, Q. (2024). The current situation, problems, and countermeasures for the cultivation of innovative talents among college students in the new era. New Generation, 23(6), 43-48+55. 

Li, B. Y., Bai, Y., Zhan, X. N., & Li, G. (2023). The technological features and evolutionary forms of Artificial Intelligence Generated Content (AIGC). Documentation, Information & Knowledge, 40(1), 66-74.

Li, S. W., & Gao, A. (2024). Research and practice on the innovation talent cultivation model based on academic competitions. Innovation and Entrepreneurship Theory Research and Practice, 7(10), 132-136.

Shi, W. R., & Han, X. B. (2024). The impact of generative artificial intelligence on learning analytics research: Status and prospects—A review of the 2024 Learning Analytics and Knowledge International Conference (LAK24). E-Education Research, 45(12), 113-120.

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