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Hauptverfasser: Zou, Bin, Yuan, Yijia, Wang, Chenghao, Du, Yiran
Format: Preprint
Veröffentlicht: 2026
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Online-Zugang:https://arxiv.org/abs/2605.16653
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author Zou, Bin
Yuan, Yijia
Wang, Chenghao
Du, Yiran
author_facet Zou, Bin
Yuan, Yijia
Wang, Chenghao
Du, Yiran
contents This study examined whether AI-mediated speaking practice can reduce acculturative stress among Chinese international students in UK universities. Using a sequential explanatory mixed-methods design, 126 participants were randomly assigned to an experimental group, which completed a four-week intervention using EAP Talk, an AI-assisted English for Academic Purposes speaking platform offering role play, scenario-based practice, free talk, and automated feedback, or a control group, which continued usual academic and English-learning activities. Pre- and post-test questionnaires measured perceived language insufficiency, social isolation, and academic pressure, while semi-structured interviews with 20 experimental-group participants contextualised the quantitative findings. Linear mixed-effects models showed that the experimental group experienced significantly greater reductions than the control group across all three outcomes, with the strongest effect on perceived language insufficiency. Interview findings suggested that EAP Talk supported low-stakes rehearsal, communicative confidence, academic speaking preparation, and greater willingness to initiate social interaction. However, participants also noted that AI-mediated practice could not fully reproduce authentic human interaction, disciplinary feedback, or broader institutional support. The findings suggest that AI-mediated speaking practice can function as a supplementary scaffold for reducing communication-related dimensions of acculturative stress, but should be integrated with peer interaction, teacher feedback, and wider support services.
format Preprint
id arxiv_https___arxiv_org_abs_2605_16653
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Can AI Reduce Acculturative Stress? Exploring the Role of AI-Mediated Speaking Practice in Chinese International Students' Perceived Language Insufficiency, Social Isolation, and Academic Pressure
Zou, Bin
Yuan, Yijia
Wang, Chenghao
Du, Yiran
Human-Computer Interaction
This study examined whether AI-mediated speaking practice can reduce acculturative stress among Chinese international students in UK universities. Using a sequential explanatory mixed-methods design, 126 participants were randomly assigned to an experimental group, which completed a four-week intervention using EAP Talk, an AI-assisted English for Academic Purposes speaking platform offering role play, scenario-based practice, free talk, and automated feedback, or a control group, which continued usual academic and English-learning activities. Pre- and post-test questionnaires measured perceived language insufficiency, social isolation, and academic pressure, while semi-structured interviews with 20 experimental-group participants contextualised the quantitative findings. Linear mixed-effects models showed that the experimental group experienced significantly greater reductions than the control group across all three outcomes, with the strongest effect on perceived language insufficiency. Interview findings suggested that EAP Talk supported low-stakes rehearsal, communicative confidence, academic speaking preparation, and greater willingness to initiate social interaction. However, participants also noted that AI-mediated practice could not fully reproduce authentic human interaction, disciplinary feedback, or broader institutional support. The findings suggest that AI-mediated speaking practice can function as a supplementary scaffold for reducing communication-related dimensions of acculturative stress, but should be integrated with peer interaction, teacher feedback, and wider support services.
title Can AI Reduce Acculturative Stress? Exploring the Role of AI-Mediated Speaking Practice in Chinese International Students' Perceived Language Insufficiency, Social Isolation, and Academic Pressure
topic Human-Computer Interaction
url https://arxiv.org/abs/2605.16653