Navigating Global and Local Generative AI Tools in Higher Education: Perspectives from Chinese Students

• In Event: Highlighted Session: Perils and Promises of Generative AI in Higher Education: Comparative Insights from Mainland China, Hong Kong, Japan, Mongolia, and the U.S.

Proposal

This study explores the impact of generative AI tools on Chinese undergraduate students’ learning, particularly within a context where access to certain global technologies is limited. By examining both global tools like ChatGPT, which face access restrictions in China and locally Generative AI tools, it highlights the complexities of digital learning in China. The research provides valuable insights into how students navigate technological evolution, cultural norms, and regulatory challenges, contributing to the broader conversation on the future of education in a globalized yet technologically diverse landscape.

Utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT), this study explores the adoption and use of generative AI tools among Chinese undergraduate students. UTAUT provides a robust framework to understand the factors that drive or hinder technology acceptance, such as performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh et al., 2016). Through this lens, the study investigates how generative AI tools, including those with restricted access like ChatGPT, as well as local AI alternatives influence students’ learning processes across different academic disciplines.

A qualitative research approach was adopted for this study, with in-depth interviews conducted with 15 undergraduate students from one of China’s top universities. The participants came from a variety of disciplinary backgrounds, providing a rich, cross-sectional view of AI usage in academic learning. The interviews focused on understanding how students integrate generative AI into their studies, their experiences and perspectives on the differences between global AI tools and local alternatives. Data from the interviews were analyzed using multiple rounds of coding and thematic analysis, ensuring that key themes and patterns were identified and explored in depth.

The findings reveal that generative AI tools are deeply embedded in the learning routines of Chinese students, with many expressing strong reliance on these technologies for tasks such as literature searching, writing, and brainstorming. Interestingly, the limited access of ChatGPT has created a discipline-specific disparity in AI usage. Students in certain fields, such as engineering and computer science, often find ways to access restricted tools, while students in humanities and social sciences face greater barriers to access. Additionally, the study found that cultural and linguistic factors significantly influence how students choose between global and local AI tools. Many students prefer local AI tools for language-specific tasks, while others find ChatGPT more useful for its advanced capabilities, despite its limited availability.

This study contributes to the growing literature on generative AI in higher education by demonstrating how technological restrictions and local alternatives shape students’ learning experiences. It highlights the nuanced impacts of AI tools on education in environments where access to global technologies is limited, yet local innovations are advancing. Furthermore, the study offers a unique perspective on how cultural and linguistic factors mediate the adoption of global versus local AI technologies, providing essential insights for future educational technology policy and practice in diverse global contexts.

Authors

• Qin Xie, University of Minnesota - Twin Cities

• Ming Li, Osaka University

• Fei Cheng, Kyoto University