Transformative Experiences with AI: Exploring the Opportunities and Challenges for International Chinese Students in Higher Education (Poster 10)

• In Event: Artificial Intelligence, Gender, Race, Culture, and Environmental Justice in Education: An Intersectional Analysis of Bias, Equity, and Sustainability Globally

Abstract

This study examines the experiences of international Chinese students in higher education concerning Artificial Intelligence (AI), utilizing quantitative research methods and Stata for data analysis. The research is structured around three key dimensions: cultural background, gender, and educational background, analyzing how these factors influence students' interactions with AI technologies. Specifically, the study investigates the extent to which AI facilitates academic success, the risks it poses regarding bias and inequality, and the challenges students face in adapting to AI-driven learning environments.
Framed by Mezirow's Transformative Learning Theory (1991), which emphasizes critical reflection, perspective transformation, and prior experiences in shaping how individuals engage with new learning paradigms. Additionally, the Technology Acceptance Model (TAM) (Davis, 1989) serves as a theoretical foundation for designing survey instruments, measuring students' perceptions of AI's usefulness, ease of use, and overall acceptance. Therefore, research questions are:1. How do international Chinese students' cultural backgrounds influence their perceptions and utilization of AI technologies in higher education? 2. What are the key barriers to AI adoption among international students, and what institutional or peer support mechanisms mitigate these challenges?
By integrating Transformative Learning Theory, this study explores how critical engagement with AI fosters cognitive and behavioral adaptation, while TAM-based survey instruments assess students’ acceptance and engagement with AI tools. The study aims to provide empirical insights into the role of AI in shaping academic identity, intercultural competence, and digital literacy among international Chinese students. Furthermore, findings from this research will inform the culturally responsive design of AI-driven educational technologies, contributing to more inclusive and equitable AI integration in higher education.

Authors

• Shiyue Zhang, FlyHigh Talent

• Jialin Yan, University of Rochester