ORIGINAL RESEARCH article
Front. Educ.
Sec. Higher Education
Volume 10 - 2025 | doi: 10.3389/feduc.2025.1622620
The Moderating Role of Ethnic Culture on Adoption Intention of Generative Artificial Intelligence among University Students
Provisionally accepted
Kai Cao1*
Ping Wang2- 1Library of Qinghai university, Qinghai University, Xining, China
- 2Xiamen University, Xiamen, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
This study is based on an extended model of the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to explore the influencing factors of the adoption intention of generative artificial intelligence (GAI) in the higher-education environment of multi-ethnic regions. It also focuses on analyzing the moderating effects of ethnic culture. Data from 432 university students were collected through a questionnaire survey and analyzed using a structural equation model. The research findings are as follows: (1) The factors influencing university students' adoption intention of generative artificial intelligence include perceived ease of use, perceived usefulness, social influence, facilitating conditions, perceived learning performance, perceived learning efficiency, and user satisfaction. (2) Tool designs that are not well-adapted to ethnic cultures and the conflicts between practicality and cultural values weaken users' perceived usefulness of generative artificial intelligence, negatively moderating the impact of perceived usefulness on user satisfaction.These findings provide unique insights for university administrators in multi-ethnic regions to formulate GAI adoption policies. In addition, this study also has certain reference significance for other research in the cross-cultural field.
Keywords: Generative AI, higher education, ethnic culture, Technological readiness, Moderating analysis, Structural Equation Modeling
Received: 04 May 2025; Accepted: 25 Aug 2025.
Copyright: © 2025 Cao and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Kai Cao, Library of Qinghai university, Qinghai University, Xining, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.