AI in International Education—Beyond Efficiency, Toward Equity

Teaching in international education has shown me one persistent contradiction: we expect global mobility, multilingual competence, and academic excellence, yet the system relies heavily on English as a gatekeeper.

AI suddenly disrupts this structure.

When I design AI-enhanced language learning courses at UBC, I constantly see how tools like generative models, machine translation, and conversational AI allow students from diverse linguistic backgrounds to access knowledge more fairly. For many of my learners, AI is not merely a shortcut—it is a form of linguistic empowerment.

But AI also introduces new challenges: dependency, loss of authorship, misinformation, unequal digital literacy, and ethical concerns. That is why my courses intentionally integrate:

• micro & macro AI ethics
• repair-strategy prompting
• AI anxiety reflection
• cross-platform comparison tasks
• AI-for-learning, not AI-instead-of-learning

The purpose is not to teach students to use AI, but to teach them to think with AI responsibly, to understand its boundaries, and to maintain independent academic identity in an age of assistance.

In the international education context, I see AI as a historical turning point:

It does not simply make learning faster; it redefines what counts as ability, who gets to participate, and how multilingual learners navigate global systems.

For me, AI is ultimately a tool for equity, critical literacy, and self-empowered learning—if we design pedagogy that keeps students, not algorithms, at the center.