University students use AI differently across languages and cultures
From Access to Realized Affordances: University Students' Generative AI Engagement across Linguistic and Sociotechnical Contexts
Computers and Society
Summary
The study found that university students in China, Japan, and Mongolia use generative AI tools in different ways depending on their language and cultural context. In China, students switch between local and global AI tools; Japanese students mainly use global AI tools in their own language, while Mongolian students often switch to English for better results. The researchers show that simply having access to AI tools isn’t enough for students to make full educational use of them. Instead, how students use AI depends on language, technology access, school practices, and their own judgments about the tools.
What this means in practice
- •For educational technology developers: Design AI tools that adapt to different languages and cultural settings to better support diverse student needs in universities.
- •For university it administrators: Create access policies and support systems for generative AI that consider local linguistic preferences and privacy concerns.
Authors
Ming Li, Qin Xie, Ariunaa Enkhtur, Lilan Chen, Fei Cheng
Abstract
Generative artificial intelligence (GenAI) is increasingly embedded in university students' academic work, yet student engagement is often examined through adoption, frequency of use, or general perceptions, with less attention to how it is shaped by linguistic and sociotechnical conditions. This comparative qualitative study examines how university students access, incorporate, and evaluate GenAI across three contrasting higher education settings in China, Japan, and Mongolia. Semi-structured interviews were conducted with 42 undergraduates from one university in each setting, and the data were analyzed through qualitative interpretation supported by co-occurrence network and correspondence analyses using KH Coder. Across the three samples, GenAI was incorporated into academic practices spanning exploration, production, and refinement. However, access pathways differed across the three contexts. Participants in China navigated between domestic and global GenAIs, those in Japan predominantly used global GenAIs through Japanese, and those in Mongolia more frequently shifted to English when Mongolian outputs were perceived as less satisfactory. During their use of GenAI, students evaluated its accuracy, privacy implications, potential for dependence, and effects on critical thinking. The findings suggest that technological access alone does not ensure equivalent educational usability across linguistic and sociotechnical contexts. Building on the empirical findings and the analytical perspectives, an empirically informed integrative framework is developed in which student GenAI engagement is understood as situated, mediated, enacted, and evaluated. The framework conceptualizes realized affordances as educational possibilities that become practically usable through the interplay of sociotechnical conditions, linguistic resources, academic practices, and evaluative judgments.