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arxiv: 2605.15127 · v1 · submitted 2026-05-14 · 💻 cs.HC · cs.AI

Recognition: no theorem link

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

Laleh Nourian , Anisa Callis , Stephanie Patterson , Jadeline Miao , Jamison Heard , Garreth W. Tigwell
Authors on Pith no claims yet

Pith reviewed 2026-05-15 03:09 UTC · model grok-4.3

classification 💻 cs.HC cs.AI
keywords international studentsconversational AIcross-cultural adaptationAI support toolsChatGPTcultural challengeslong-term supportUS universities
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The pith

International students view conversational AI as a quick first-aid tool for cultural challenges but want it to become a long-term support companion.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper studies how international students in the US adopt conversational AI tools such as ChatGPT to handle the stresses of moving to a new culture. Through a survey of 60 students and interviews with 14, the work maps specific challenges to patterns of AI use and uncovers motivations along with limits on that use. Students currently turn to AI mainly for fast answers to immediate problems like daily logistics or cultural misunderstandings. At the same time, participants show interest in AI growing into an ongoing companion that offers sustained help during the longer process of adaptation. This matters because university support systems and informal networks are fragmented, so clearer insight into AI's role could guide better-designed tools for this population.

Core claim

Our findings show that AI is perceived as a first-aid tool for immediate challenges, however, there is an interest in transforming AI from a tool for short-term help into a long-term support companion. By identifying where and how AI can provide long-term support, and where it is insufficient, we contribute recommendations for creating AI-powered support tailored to the unique needs of international students.

What carries the argument

Mixed-methods study of survey responses from 60 international students and interviews with 14 that links reported challenges to AI adoption patterns, motivations, and boundaries of use.

If this is right

  • AI systems can be designed to handle short-term queries while also supporting persistent, multi-session interactions over time.
  • Recommendations point toward AI features that address the specific overlapping challenges faced by international students.
  • Hybrid support models combining AI with university services become feasible where AI alone is insufficient.
  • Identification of usage boundaries helps define safe and effective scopes for AI in cross-cultural contexts.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Specialized AI companions trained on common international-student scenarios could reduce daily stress during the first year in the US.
  • Such tools might complement rather than replace human networks when universities have limited counseling resources.
  • Testing persistent AI support in actual campus settings would reveal whether it improves measurable adaptation outcomes like academic performance or well-being.
  • The pattern could extend to other groups undergoing cultural transitions, such as new immigrants or expatriates.

Load-bearing premise

The sample of 60 survey respondents and 14 interviewees sufficiently represents the diversity of international students' experiences and AI usage patterns across US institutions.

What would settle it

A follow-up study with a larger sample drawn from multiple US universities that finds no widespread interest in AI as a long-term companion would undermine the claim.

Figures

Figures reproduced from arXiv: 2605.15127 by Anisa Callis, Garreth W. Tigwell, Jadeline Miao, Jamison Heard, Laleh Nourian, Stephanie Patterson.

Figure 1
Figure 1. Figure 1: Support types that international students prefer [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Comparative analysis of international students’ challenges and usage of conversational AI for each challenge domains [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: AI usage distribution among the four challenge domains [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: AI usage distribution for detailed tasks among the four challenge domains [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
read the original abstract

Moving to a new culture and adapting to a new life, as an international student, can be a stressful experience. In the US, international students face unique overlapping challenges, yet the current support ecosystem, including university support systems and informal social networks, remains largely fragmented. While conversational AI has emerged as a tool used by many (e.g., generative AI chatbots like ChatGPT and Google Gemini), we do not have a clear understanding of how international students adopt and perceive these technologies as support tools. We conducted a survey study (n=60) to map the relationship between international students' challenges and AI adoption patterns, followed by an interview study with 14 participants to identify the underlying motivations and boundaries of use. Our findings show that AI is perceived as a first-aid tool for immediate challenges, however, there is an interest in transforming AI from a tool for short-term help into a long-term support companion. By identifying where and how AI can provide long-term support, and where it is insufficient, we contribute recommendations for creating AI-powered support tailored to the unique needs of international students.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper reports results from a mixed-methods study consisting of a survey (n=60) and semi-structured interviews (n=14) with international students in the U.S. It maps relationships between cross-cultural adaptation challenges and patterns of conversational AI adoption (e.g., ChatGPT, Gemini), concluding that students view AI primarily as a short-term 'first-aid' tool for immediate issues while expressing interest in its evolution into a long-term support companion; the work ends with design recommendations for AI-powered support tailored to this population.

Significance. If the empirical patterns hold, the study offers timely HCI contributions by documenting motivations, boundaries, and perceived gaps in AI use for a specific vulnerable group whose support needs are often fragmented. The primary-data approach and explicit focus on transforming short-term use into sustained companionship provide concrete starting points for future system design and evaluation.

major comments (2)
  1. [Methods] Methods section: The recruitment description provides no evidence of stratification by institution type, nationality, academic level, or English proficiency, nor any quantitative assessment of theme saturation or inter-rater reliability for the thematic analysis. Because the headline claim about AI being perceived as a 'first-aid tool' with interest in long-term companion use rests entirely on these 74 participants, the absence of such details directly limits the generalizability asserted in the abstract and Discussion.
  2. [Findings] Findings / Discussion: The paper does not report any quantitative checks (e.g., frequency counts of themes across demographic subgroups or comparison of survey vs. interview responses) that would substantiate the transition from 'short-term help' to 'long-term support companion' as a robust pattern rather than an artifact of the convenience sample.
minor comments (2)
  1. [Abstract] Abstract: The sentence describing the survey-interview sequence would be clearer if it explicitly stated the total number of unique participants and whether any overlap existed between the two samples.
  2. [Related Work] Related Work: A brief citation to recent HCI literature on AI for marginalized or migrant populations would help situate the contribution more precisely.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the opportunity to revise our manuscript. We appreciate the referee's detailed feedback on the methods and findings sections. We have addressed the concerns by providing additional details on our recruitment and analysis procedures and by including quantitative summaries of the themes. Below we respond point by point.

read point-by-point responses

  1. Referee: [Methods] Methods section: The recruitment description provides no evidence of stratification by institution type, nationality, academic level, or English proficiency, nor any quantitative assessment of theme saturation or inter-rater reliability for the thematic analysis. Because the headline claim about AI being perceived as a 'first-aid tool' with interest in long-term companion use rests entirely on these 74 participants, the absence of such details directly limits the generalizability asserted in the abstract and Discussion.

    Authors: We thank the referee for highlighting these important methodological details. Our recruitment was conducted via convenience sampling through international student associations, university mailing lists, and online communities, which did not include formal stratification. We have updated the Methods section to report the demographic distribution of our sample (e.g., participants from 12 countries, various academic levels) and to describe our thematic analysis process in more detail, including how saturation was determined through iterative coding until no new themes emerged. Regarding inter-rater reliability, the first author coded all transcripts, with a second researcher independently coding 20% of the data; discrepancies were discussed until consensus was reached. We have also revised the abstract and Discussion to frame our findings as exploratory rather than broadly generalizable. revision: partial

  2. Referee: [Findings] Findings / Discussion: The paper does not report any quantitative checks (e.g., frequency counts of themes across demographic subgroups or comparison of survey vs. interview responses) that would substantiate the transition from 'short-term help' to 'long-term support companion' as a robust pattern rather than an artifact of the convenience sample.

    Authors: We agree that incorporating quantitative checks would enhance the robustness of our claims. In the revised version, we have added frequency counts from the interview data in the Findings section (e.g., 11 out of 14 interviewees explicitly described using AI for short-term 'first-aid' support, and 9 expressed a desire for long-term companion features). We also include a brief comparison noting that survey results showed 65% using AI for immediate adaptation challenges, aligning with interview themes. These additions help substantiate the observed patterns. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical primary-data study

full rationale

The paper reports results from a survey (n=60) and interviews (n=14) on international students' use of conversational AI. No equations, fitted parameters, derivations, or self-referential definitions appear in the provided text or abstract. Findings are presented as direct outputs from thematic analysis of collected responses rather than any internal construction or self-citation chain that reduces to the inputs. The central claims rest on external participant data and are therefore self-contained against the listed circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical qualitative HCI study; no mathematical derivations, free parameters, axioms, or invented entities are present.

pith-pipeline@v0.9.0 · 5513 in / 935 out tokens · 50278 ms · 2026-05-15T03:09:21.399753+00:00 · methodology

discussion (0)

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Reference graph

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