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arxiv: 2604.18171 · v1 · submitted 2026-04-20 · 💻 cs.HC

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 This is my paper

Pith reviewed 2026-05-10 04:02 UTC · model grok-4.3

classification 💻 cs.HC
keywords non-native speakersinteractional anxietyAI translationmultilingual communicationspeaking self-efficacymutual understanding channel
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The pith

An AI tool with real-time translation and a mutual-understanding channel raises non-native speakers' confidence and lowers their anxiety in conversations with natives.

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

The paper tests a direct speaking-support tool for non-native speakers who face linguistic gaps and unclear social cues when talking with native speakers. The tool translates spoken input on the fly and creates a shared channel that signals assistance needs to the other side. A within-subjects study with 25 pairs performing collaborative tasks found that non-native speakers reported higher speaking self-efficacy, lower interactional anxiety, and less workload after using the tool, with stronger effects among those with below-average proficiency. Native speakers also noticed the support requests and felt greater responsibility to help. The work shows that combining technical translation with an explicit mutual-understanding link can ease both language and social barriers at once.

Core claim

Providing non-native speakers an AI system that delivers real-time translation for speaking plus a channel for signaling needs to native speakers produces measurable gains in speaking self-efficacy, reductions in interactional anxiety and workload, and a stronger sense of mutual support, with larger benefits for lower-proficiency users.

What carries the argument

The AI tool that supplies real-time translation support while opening a mutual-understanding channel between non-native and native speakers.

Load-bearing premise

Self-report scales for self-efficacy, anxiety, and workload accurately reflect the intended experiences, and results from these 25 pairs will hold for other people and tasks.

What would settle it

A larger, more varied group of participants using the tool during unstructured everyday conversations shows no drop in anxiety or rise in self-reported confidence compared with no-tool conditions.

Figures

Figures reproduced from arXiv: 2604.18171 by Jiting Cheng, Justin Peng, Naomi Yamashita, Peinuan Qin, Yi-Chieh Lee, Zhengtao Xu, Zicheng Zhu.

Figure 1
Figure 1. Figure 1: The experiment interface displaying the tool that integrates with the Objects Game component. The [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The user journeys of the (a) speaking assistant and (b) the mutual understanding channel, detailing [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The comparison on perceptions of speaking self-efficacy, workload, and anxiety between [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The comparison on perceptions of speaking self-efficacy, workload, and anxiety between [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The comparison on perceptions of speaking self-efficacy, workload, and anxiety between [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The comparison on perceptions of speaking self-efficacy, workload, and anxiety between [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Speaking assistant usage count among NNSs, categorized by L2-speaking proficiency level. NNSs with lower proficiency levels exhibited a higher frequency of speaking assistant usage, with a notable decrease in usage as proficiency improved. The significant differences in usage frequency between proficiency levels were confirmed through a one-way ANOVA and subsequent Tukey post-hoc analysis. Error bars repre… view at source ↗
Figure 8
Figure 8. Figure 8: Descriptive statistics of NNS participants’ input behavior when using the speaking assistant for [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The (a) NNS speaking performance was evaluated by their NS partner, including three aspects: (b) [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
read the original 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.

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

3 major / 2 minor

Summary. The paper introduces an AI tool for real-time speaking support via translation and a mutual understanding channel between non-native speakers (NNS) and native speakers (NS) to reduce linguistic and interactional anxiety. It reports a within-subjects experiment with 25 NNS-NS pairs (N=50) on collaborative tasks, claiming the tool improved NNS speaking self-efficacy, reduced interactional anxiety and workload (particularly for below-average proficiency NNSs), with both parties reporting mutual support and communicative responsibility.

Significance. If the results hold after addressing measurement and analysis gaps, the work contributes to HCI and multilingual communication research by providing direct speaking aids rather than comprehension-only tools and by emphasizing mutual understanding to mitigate anxiety. It supplies empirical data and design insights that could inform real-time AI communication systems.

major comments (3)
  1. [Abstract and Experiment/Results sections] The central claims rest on self-reported Likert scales for self-efficacy, interactional anxiety, and workload with no reported objective corroboration such as coded speaking turns, latency measures, or behavioral logs. This is load-bearing for the headline conclusion in the abstract and results, as demand characteristics or social desirability cannot be ruled out without such data.
  2. [Results section] No statistical tests, effect sizes, p-values, confidence intervals, or multiple-comparison corrections are described for the reported improvements or the subgroup effect (below-average proficiency). The within-subjects design also omits any mention of order-effect checks or counterbalancing, undermining confidence in the findings.
  3. [Method section] The sample of 25 pairs is small for reliable subgroup analysis; the method provides no power analysis, exclusion criteria, or justification for generalizability beyond the specific collaborative tasks and convenience pool.
minor comments (2)
  1. [Abstract] Clarify whether 'significant' in the abstract refers to statistical significance or qualitative perception, and ensure consistency with the 'suggest' language used for the main findings.
  2. [Throughout] Define acronyms (NNS, NS) on first use and ensure figure captions fully describe the tool interface and task setup for readers unfamiliar with the domain.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for their constructive and detailed feedback. We have carefully reviewed each major comment and provide point-by-point responses below, indicating where revisions will strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract and Experiment/Results sections] The central claims rest on self-reported Likert scales for self-efficacy, interactional anxiety, and workload with no reported objective corroboration such as coded speaking turns, latency measures, or behavioral logs. This is load-bearing for the headline conclusion in the abstract and results, as demand characteristics or social desirability cannot be ruled out without such data.

    Authors: We agree that reliance on self-reported measures leaves open the possibility of demand characteristics, and we acknowledge this as a limitation of the current study. Validated Likert scales for anxiety and self-efficacy are standard in HCI and psychology research on language learning, and our qualitative data from open-ended responses aligned with the quantitative trends. However, we did not collect objective behavioral logs such as speaking turns or latency. In the revision, we will add an explicit limitations subsection discussing this gap and outlining how future work could incorporate video coding or system logs for corroboration. This improves transparency without altering the reported findings. revision: partial

  2. Referee: [Results section] No statistical tests, effect sizes, p-values, confidence intervals, or multiple-comparison corrections are described for the reported improvements or the subgroup effect (below-average proficiency). The within-subjects design also omits any mention of order-effect checks or counterbalancing, undermining confidence in the findings.

    Authors: We apologize for the insufficient detail in the submitted version. The within-subjects experiment used counterbalanced task orders, and analyses included paired t-tests with effect sizes (Cohen's d), p-values, confidence intervals, and Bonferroni corrections for the subgroup comparisons. No significant order effects were observed. We will revise the Results section to fully report these statistics, the counterbalancing procedure, and the order-effect verification to enhance clarity and confidence in the findings. revision: yes

  3. Referee: [Method section] The sample of 25 pairs is small for reliable subgroup analysis; the method provides no power analysis, exclusion criteria, or justification for generalizability beyond the specific collaborative tasks and convenience pool.

    Authors: We recognize the constraints of the sample size for subgroup analysis. A post-hoc power analysis will be added to the Method section. Exclusion criteria (e.g., no prior exposure to the prototype and basic proficiency thresholds) will be explicitly detailed. In the Discussion, we will expand the generalizability section to clarify that findings apply to the specific collaborative tasks and university convenience sample, while noting the strengths of the within-subjects design for controlling individual differences. revision: yes

standing simulated objections not resolved
  • We cannot add new objective behavioral data (such as coded speaking turns or latency measures) because it was not collected in the original experiment.

Circularity Check

0 steps flagged

No circularity: empirical experiment reports measured outcomes without derivation or self-referential reduction

full rationale

The paper describes an AI tool and evaluates it via a within-subjects experiment with 25 NNS-NS pairs, reporting effects on self-efficacy, anxiety, and workload from participant self-reports. No equations, fitted parameters, or mathematical predictions appear in the provided text. Claims rest on observed data rather than reducing to prior self-citations or tautological definitions. The derivation chain is self-contained as standard empirical reporting; no load-bearing step equates outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work rests on standard HCI assumptions about the validity of self-report scales for anxiety and self-efficacy; no free parameters, invented physical entities, or ad-hoc axioms are introduced beyond typical experimental design choices.

axioms (1)
  • domain assumption Self-report questionnaires validly measure speaking self-efficacy, interactional anxiety, and workload.
    The study uses these scales as primary outcomes without additional validation in the reported abstract.

pith-pipeline@v0.9.0 · 5528 in / 1284 out tokens · 27012 ms · 2026-05-10T04:02:22.829993+00:00 · methodology

discussion (0)

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Reference graph

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