Alleviating Linguistic and Interactional Anxiety of Non-Native Speakers in Multilingual Communication
Peinuan Qin, Justin Peng, Zhengtao Xu, Jiting Cheng, Zicheng Zhu, Naomi Yamashita, Yi-Chieh Lee

TL;DR
This paper presents an AI-powered real-time translation tool designed to reduce anxiety and improve speaking confidence among non-native speakers during multilingual communication, fostering mutual understanding.
Contribution
It introduces a novel AI tool that provides real-time translation and mutual understanding channels, specifically addressing speaking anxiety and interactional challenges for NNSs.
Findings
The tool increased NNSs' speaking self-efficacy.
It reduced interactional anxiety and workload for NNSs.
NNSs felt more supported through mutual understanding channels.
Abstract
Non-native speakers (NNSs) frequently encounter speaking difficulties in multilingual communication, where existing approaches have shown promise in facilitating NNSs' comprehension and participation in real-time communication. However, they often overlook providing direct speaking support, where anxiety stemming from linguistic inadequacy and uncertain communication dynamics are core issues. To address this, we introduce an AI tool with translation for real-time speaking support. It also builds a channel for mutual understanding with native speakers (NSs) to mitigate interactional anxiety. Through a within-subjects experiment involving 25 NNS-NS pairs (N = 50) on collaborative tasks, our findings suggest that the tool improved NNSs' speaking self-efficacy, reduced their interactional anxiety, and decreased their workload, particularly for NNSs with below-average language proficiency.…
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