Exploration of Factors Affecting the Continuous Usage Intention of College Students in AIGC

DOI: https://doi.org/10.62517/jhet.202615339

Author(s)

Yunfei Xu, Liyan Tian*, Guoxia Dong, Jing Liu

Affiliation(s)

Beijing Union University, Beijing, China *Corresponding author

Abstract

Currently, the application scope and depth of AIGC technology continue to expand in fields such as education, media, and entertainment. As primary adopters of emerging technologies, university students are demonstrating increasing reliance and engagement with AIGC tools. Based on the continuous use model of information systems and the theory of information ecosystem, a theoretical model of influencing factors for the continuous use willingness of AIGC technology among college students was established. With the help of the network platform, 394 valid questionnaires from college students were collected, and descriptive statistical analysis and path analysis were conducted on the recovered data using SPSS. Empirical research indicates that there is a positive influence among multiple factors. Among them, satisfaction has the most prominent direct impact on the willingness to continue using. Based on the research results, this study provides suggestions from multiple aspects, covering the optimization of AIGC technology, the improvement of educational platforms, and the enhancement of user awareness, in order to enhance college students' dialectical understanding and continuous usage intention of AIGC, and promote the development and efficiency improvement of generative artificial intelligence in the field of education.

Keywords

College Students; AIGC; Willingness to Continue Using; Information Ecosystem

References

[1]The 14th Five-Year Plan for National Economic and Social Development of the People's Republic of China and the Long-Range Objectives Through the Year 2035 [N]. People's Daily, 2021-03-13 (001). [2]National Development and Reform Commission interprets "Opinions on Deeply Implementing the 'Artificial Intelligence+' Initiative" [N]. People's Telecommunications, 2025-08-29 (004). [3]Bhattacherjee A. An Empirical Analysis of the Antecedents of Electronic Commerce Service Continuance[J]. MIS Quarterly,2001,25(3):351-370. [4]Huang Lu, Jiang Yan, Xu Tong, et al. How Cloud Computers Can Facilitate Personalized Learning: Influencing Factors of Primary School Students' Continued Use Intentions of Cloud Computers [J]. Education and Equipment Research, 2025, 41(5): 3-10 + 18. [5]Yang Genfu. Research on the Influencing Factors of Mobile Reading User Satisfaction and Continued Usage Intention - Taking Content Aggregation Apps as an Example [J]. Modern Information, 2015, 35(03): 50-56. [6]Nardi B, O＇Day V．Information Ecologies: Using Technology with Heart［M］．The MIT Press, 1999: 27． [7] Wang Jian. Research on the Influencing Factors of Mobile Library Readers' Usage Intention Based on PLS-SEM Method [J]. Information Exploration, 2023(10): 108-119. [8]Zhang Yue, Li Qingyu, Liu Yuqi, et al. Research on the Influencing Factors of User Dropout Behavior in the AIGC Application Platform from the Perspective of Configuration [J]. Information Theory and Practice, 2024, 47(3): 130-137. [9]Qiu Junping, Long Kang, Xu Zhongyang. An Empirical Study on the Continuous Usage Intention of Generative AI Users Based on ECT-U&G - Taking College Students as an Example [J]. Modern Information, 2025, 45(6): 46-59. [10]Wang Di, Chen Yong. The Disintegration and Reconstruction of Traditional News Discourse Authority in the Context of AIGC [J]. Audio-Visual, 2026, (6): 101-104. [11]Chu Ruijie, Zhuang Rui. Analysis of the Construction of User Trust Mechanism by Generative AI Anthropomorphic Communication from the Perspective of Human-Computer Interaction: A Case Study of DeepSeek [J]. Journal of Hubei University of Economics (Humanities and Social Sciences Edition), 2025, 22(7): 111-115. [12]Zhang Jinao, Lu Xinyuan, Wang Durong, et al. Research on the Adoption of AIGC Health Information Driven by Dual Factors of AI Literacy and Health Information Literacy [J]. Library and Information Science, 2025, (4): 83-95.