Students’ Adoption of Generative AI: A Comparative Study of China, Japan, and Mongolia

• In Event: Artificial Intelligence, Digitalization, and Educational Equity

Proposal

Purpose of this study:
As generative AI (GAI) becomes increasingly integrated into higher education, it reveals both shared challenges and unique experiences among students across diverse global contexts, particularly in non-English-speaking regions. This study aims to critically examine the multifaceted impacts of GAI on students by analyzing empirical data from three countries—China, Japan, and Mongolia—each offering a distinct yet interconnected perspective.

Theoretical background:
This study is grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) proposed by Venkatesh (2016), which provides a comprehensive framework for examining the factors influencing students’ acceptance and use of GAI in higher education. UTAUT identifies key determinants, including performance expectancy, effort expectancy, social influence, and facilitating conditions. By applying this model, the study explores how these factors—alongside linguistic and cultural contexts—shape students’ experiences with GAI. The framework’s focus on both individual and contextual factors offers valuable insights into the unique challenges and opportunities GAI presents in non-English-speaking educational environments.

Research design, methodology:
A qualitative approach was chosen for its effectiveness in exploring students’ personal experiences and the context of GAI usage. We conducted a total of 42 interviews with undergraduate students from one top-tier university in each country, posing two primary research questions: 1) How do students utilize GAI in China, Japan and Mongolia, and what challenges do they encounter? 2) In what ways are their experiences similar or different? The sample was methodically balanced in terms of gender and academic discipline to enhance the robustness of the data. 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.

Findings:
The study reveals that GAI has become deeply integrated into Chinese students’ learning, with many relying on it for literature searches, writing, and brainstorming. However, limited access to ChatGPT has created disparities in AI usage across disciplines—engineering and computer science students often find ways to bypass restrictions, while humanities and social sciences students face greater barriers. Additionally, cultural and linguistic factors influence students’ choices between global and local GAI tools; many prefer local tools for language-specific tasks, while some still opt for ChatGPT due to its advanced capabilities despite restricted access.Students in Mongolia have also adopted GAI—especially ChatGPT and Gemini—as an academic aid for tasks like summarizing information, brainstorming, writing essays and in improving their writings, particularly in English. However, the limited accuracy of GAI in processing Mongolian text presents a major challenge, leading some students to rely on English or avoid AI for complex tasks. Students with limited English skills use AI Tools in combination with language translation tools such as Google Translate but they face challenges in formulating clear prompts. While some students found AI useful in improving their study habits generating new insights and suggestions, students are concerned about its reliability and the risk of over-reliance on AI-generated contents. Japanese students, despite not facing the technological restrictions of Chinese students or the language barriers of Mongolian students, generally exhibited a cautious attitude toward GAI, except for those in information science. This hesitation is largely attributed to instructors actively discouraging AI use and the lack of guidance on effectively integrating GAI into academic work.

Research limitations, implications:
This study focuses on students from top-tier universities in China, Japan, and Mongolia, which may limit generalizability to other institutions and regions. Additionally, rapid advancements in GAI and evolving policies could influence student adoption and experiences over time. Future research should explore broader student populations and consider faculty perspectives to gain a more comprehensive understanding of AI integration in higher education.
By applying UTAUT to GAI in non-English-speaking regions, this study highlights the significant role of linguistic and cultural factors in technology adoption. Institutional policies, access limitations, and societal attitudes shape students’ engagement with AI tools, underscoring the need to refine existing technology acceptance models to account for these variables.

Practical/social implications:
Findings suggest that policymakers should ensure equitable access to AI tools while addressing ethical and academic integrity concerns. Universities must implement clear guidelines and support structures to help students use AI responsibly and effectively. Educators should integrate AI literacy into curricula, equipping students with critical evaluation skills to navigate AI-generated content. Additionally, disparities in AI accessibility and use across disciplines highlight the need for targeted interventions to bridge the gap in technological resources and training.

Originality/value of paper:
This study offers a unique perspective on GAI adoption in higher education across non-English-speaking contexts, filling a critical gap in existing research. By comparing student experiences in China, Japan, and Mongolia, it provides valuable insights into how cultural, linguistic, and institutional factors shape AI usage in academic settings. The findings contribute to global discussions on AI in education, offering practical recommendations for educators, policymakers, and AI developers to optimize AI integration in diverse learning environments.

Authors

• Ming Li, The University of Osaka

• Qin Xie, University of Minnesota - Twin Cities

• Ariunaa Enkhtur, The University of Osaka

• Lilan Chen

• Fei Cheng