Alleviating Linguistic and Interactional Anxiety of Non-Native Speakers in Multilingual Communication

2026-04-20

AI Summary

This work addresses the language and interactional anxiety experienced by non-native speakers in multilingual communication, which stems from limited linguistic proficiency and uncertainty in real-time interaction, often without adequate support. The study proposes an AI-powered spoken-language assistance tool that integrates real-time translation with a bidirectional mutual understanding mechanism, uniquely combining immediate expressive support for non-native speakers with features designed to elicit empathetic responses from native interlocutors. Experimental results demonstrate that the tool significantly enhances non-native speakers’ speaking self-efficacy while reducing their interactional anxiety and cognitive load—particularly among those with lower language proficiency—and simultaneously fosters greater empathy and willingness to collaborate between both parties. This approach establishes a novel paradigm for human-AI collaborative communication across language barriers.

Technology Category

• Humans and AI: AI for Accessibility
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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. Furthermore, NNSs reported a significant sense of support from their NS partners via the mutual understanding channel, and NSs also clearly perceived the NNSs' need for assistance and displayed a strong sense of communicative responsibility. This research underscores the potential of AI support in real-time NNS communication and the importance of promoting mutual understanding, culminating in actionable design insights for future work.

Problem

• linguistic anxiety
• interactional anxiety
• non-native speakers
• multilingual communication
• speaking support

Innovation

• real-time speaking support
• mutual understanding channel
• interactional anxiety
• AI-assisted communication
• non-native speakers