Students' Current Perceptions of the Use of Generative Artificial Intelligence in a Transnational Education Program

Aim This study investigates Chinese undergraduate students’ perceptions of Generative Artificial Intelligence (GenAI) within an English-medium transnational education program. It aims to understand how students use GenAI, their skill levels, encountered challenges, and attitudes toward its academic and future career implications. Background/Context The research was conducted at the AIEN Institute, a dual-degree partnership between Shanghai Ocean University and the University of Tasmania. The Centre for English Language Teaching (CELT) supports Mandarin-speaking students in English for Academic Purposes and Business English. Over three phases, CELT shifted from prohibiting GenAI to guided integration, adopting a “Student as Partners” model to promote ethical and educational use. Description The study reflects a proactive, curriculum-embedded approach to GenAI. Students used GenAI for brainstorming, translation, editing, and research within structured assessments. Emphasis was placed on prompt-crafting, critical evaluation of outputs, and academic integrity through restorative dialogue rather than punitive measures. Method A bilingual survey was administered to 169 second-year students following a multi-week GenAI-integrated assessment. The survey included 15 questions (quantitative and qualitative) on usage patterns, tool familiarity, perceived accuracy, challenges, and attitudes toward faculty support and future workshops. Evidence Key findings show students primarily use GenAI for academic tasks (69.5%), especially brainstorming (28.4%) and translation (21.4%). While 53% rated their prompt skills as competent, 94.6% reported frequent inaccuracies. Most (77.4%) perceived faculty as supportive, and 76% believed GenAI would positively impact their careers. Nearly half expressed interest in skill-building workshops. Contribution The study highlights the need for structured support—such as workshops on prompt engineering, critical evaluation, and ethical use—to maximize GenAI’s educational benefits. It offers a transferable model for integrating GenAI in transnational and multilingual contexts, emphasizing partnership-based approaches to uphold academic integrity while fostering digital literacy. Author Contributions: Michael Wilder: Corresponding Author, Project Lead, Researcher Sernan Donacao: Co-Author, Project Implementation, Researcher Zhai Yiting: Survey Translation Zhang Yiru: Survey Translation Ding Shihan: Student Literature Review Hui Zhijun: Student Literature Review Guo Jiayun: Student Literature Review Xu Yangrui: Student Literature Review Lu Mijia: Student Literature Review Zhang Huibo: Student Literature Review Zhang Jiaming: Student Literature Review