Understanding How International Students in the U.S. Are Using Conversational AI to Support Cross-Cultural Adaptation

Synthesis: This mixed-methods study (survey n=60, interviews n=14) investigates how international students in the U.S. adopt and perceive The Path to Conversational AI Tutors: Integrating Tutoring Best Practices and Targeted Technologies to Produce Scalable AI Agents tools like ChatGPT and Google Gemini for cross-cultural adaptation. The current support ecosystem — university systems and informal social networks — remains fragmented, echoing broader integration gaps in higher education.

Key findings:

International students currently treat AI as a "first-aid tool" for immediate challenges: language barriers, navigating bureaucracy, and cultural adjustment. However, there is a clear desire to transform AI from short-term help into a long-term support companion — a finding that connects to Building AI Companions that Prioritise Learning over Performance and the broader Student Experience literature.

The study distinguishes between domains where AI can provide sustained support (language practice, administrative navigation, cultural information) and where it falls short (emotional connection, deep cultural understanding, replacing human community). This maps onto the Human-in-the-Loop paradigm, suggesting AI should augment rather than replace human support systems.

The findings have implications for designing Equity interventions for international student populations and align with Culturally Relevant Pedagogy principles. The study contributes to Higher Education understanding of how diverse student populations interact with Generative AI tools beyond academic contexts.

What this means for practice

• Instructors. Steer international students toward the tasks they rate AI most reliable for — grammar, structure, and summarizing (academic usefulness Mean=4.27) — rather than cultural navigation, the domain with their lowest satisfaction (Mean=3.49).
• Instructors. Ask about AI-mediated emotional support directly, because only 20% of students used AI for psychological challenges yet they still reported high intent to continue (Mean=4.0), so the need is likely being met quietly or not at all.
• Administrators. Reclaim the administrative guidance students are outsourcing: 29% reported logistical difficulty but 86% of AI users turned to AI for visa, tax, and paperwork tasks, which leaves consequential equity decisions resting on an unverified chatbot.
• Designers. Treat transnational data privacy as a functional requirement, not a policy page — students feared that disclosed data could affect visa or immigration status, and that fear is what blocks the long-term Human-in-the-Loop use they say they want.

Limitations

• The survey (n=60) and interviews (n=14) drew only international students at U.S. institutions recruited through LinkedIn, Twitter, and Reddit, so the adoption patterns are not a representative sample.
• Interviews asked participants to recall prior AI use instead of completing set tasks during the session, so some usage may have been forgotten and underreported.
• Only student perspectives were collected; staff at International Student Offices and other support providers were not studied.
• The domain comparisons rest on self-reported 5-point Likert ratings, and no inter-rater reliability is reported, following Reflexive Thematic Analysis guidelines.

Citation

Nourian, L., Callis, A., Patterson, S., Miao, J., Heard, J., & Tigwell, G. W. (2026). Understanding how international students in the U.S. are using conversational AI to support cross-cultural adaptation.