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

Problem
This preprint addresses the gap in understanding how international students in the U.S. utilize conversational AI for cross-cultural adaptation. Despite the proliferation of generative AI tools, there is limited empirical research on their adoption and effectiveness in supporting the unique challenges faced by this demographic. The authors aim to elucidate the relationship between the challenges encountered by international students and their patterns of AI usage, thereby informing the design of AI systems that better cater to their needs.

Method
The study employs a mixed-methods approach, beginning with a survey of 60 international students to quantitatively assess their challenges and AI adoption patterns. This is complemented by qualitative interviews with 14 participants to gain deeper insights into their motivations and limitations regarding AI use. The survey data was analyzed to identify correlations between specific challenges (e.g., language barriers, social integration) and the perceived utility of conversational AI. The interviews provided context to these findings, revealing nuanced perspectives on the role of AI as a support tool. The authors do not disclose specific computational resources or detailed statistical methodologies used in the analysis.

Results
The findings indicate that international students primarily view conversational AI as a “first-aid” tool for immediate challenges, such as language translation and information retrieval. However, there is a significant interest in evolving these tools into long-term companions that can provide ongoing support. The study highlights that while AI is effective for short-term assistance, its current capabilities are perceived as insufficient for deeper, sustained engagement. The authors suggest that tailored AI solutions could enhance the adaptation process, but specific metrics or comparisons to existing support systems are not provided.

Limitations
The authors acknowledge several limitations, including the small sample size of both the survey and interview participants, which may not be representative of the broader international student population. Additionally, the study’s reliance on self-reported data may introduce biases. The authors do not address potential ethical considerations related to AI use in sensitive contexts or the implications of data privacy for users. Furthermore, the lack of longitudinal data limits the understanding of how AI adoption evolves over time.

Why it matters
This research has significant implications for the design and deployment of AI systems aimed at supporting international students. By identifying specific challenges and preferences, the study provides a foundation for developing AI tools that are not only reactive but also proactive in fostering long-term adaptation. The insights gained could inform future research on AI in educational contexts and contribute to the broader discourse on the role of technology in enhancing cross-cultural experiences. Ultimately, this work encourages the integration of user-centered design principles in the development of AI solutions tailored to the unique needs of diverse populations.

Authors: Laleh Nourian, Anisa Callis, Stephanie Patterson, Jadeline Miao, Jamison Heard, Garreth W. Tigwell
Source: arXiv:2605.15127
URL: https://arxiv.org/abs/2605.15127v1