This study focuses on how Asian college students shape their identities and cultures through the use of Artificial Intelligence tools. The research addresses the following questions: 1. What are the attitudes, beliefs, and expectations of Asian college students towards AI? 2. What opportunities and risks, particularly from cultural, language, and gender perspectives, does AI present for Asian college students? Interviews were employed to explore these questions, framed by the Pyramid Model of Intercultural Competence (Deardorff, 2009). The interview questions were revised and designed based on the Technology Acceptance Model (TAM) by Davis (1989), which examines how users' acceptance is affected by technological tools. The data were collected publicly at the University of Rochester, with 21 participants interviewed voluntarily. Moreover, the collected data were analyzed through two rounds of coding using MAXQDA 24.4. The first round of analysis involved in vivo coding, followed by value coding to deeply explore and analyze Asian international students' experiences with AI. The findings revealed that while AI tools are generally viewed positively, with students appreciating the enhanced learning and communication opportunities they provide, there are also significant concerns regarding cultural and linguistic biases. Overall, the study concluded that AI has the potential to both facilitate and confuse cultural identities, depending on how it is integrated and perceived within educational contexts. These insights emphasize the need for culturally sensitive AI design and implementation to better support the diverse needs of international students.

Authors:
Jialin Yan, University of Rochester, United States
Tianxiang Zhu, University of Rochester, United States

About the Presenter(s)
Jialin Yan is a Ph.D. student in Education at the University of Rochester, specializing in AI policies in higher education and K-12 education. Currently, Jialin is working on projects that explore the integration of AI in educational settings and its

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