Towards Reducing Foreign Language Anxiety Using Level-Appropriate Embodied Conversational Agents

2026-07-23

AI Summary

This study addresses foreign language anxiety (FLA), a significant barrier to second language acquisition—particularly in conversational contexts—exacerbated by dialogue agents that often produce overly complex utterances. To mitigate this, the authors propose a multi-agent embodied dialogue system that dynamically adapts linguistic complexity through an innovative “generate–evaluate–regenerate” loop, integrating CEFR proficiency levels with a level classifier to simplify output in real time. This approach uniquely combines multi-agent collaboration with CEFR-aligned simplification, enhancing both comprehensibility and psychological comfort. In a small-scale experiment with Japanese university students, 87.4% of generated utterances fell within ±1 CEFR level of learners’ self-assessed proficiency, substantially outperforming an unsimplified baseline (54.1%), thereby demonstrating the system’s efficacy in tailoring dialogue difficulty to individual learners.

Abstract

Foreign language anxiety (FLA) can be a major barrier to second language acquisition (SLA), especially in conversational contexts. With the proliferation of large language models (LLMs) throughout all areas of life, recent work suggests that interacting with LLM agents can be instrumental within the field of SLA and foreign language education, especially for reducing FLA. Related work also suggests that linguistic demands and task complexity can be predictors of FLA, implying that the use of demanding, complex language could lead to learners experiencing higher FLA. In this paper, we propose a novel multi-agent embodied conversational system that generates level-appropriate dialogue for English language learners. These levels are based on those defined by the Common European Framework of Reference for Languages (CEFR) to describe non-native listener and speaker proficiency. Using a "generate-evaluate-regenerate" loop with multiple LLM agents and a level classifier, it achieves a desired simplicity that is adaptive to the user's proficiency level. We also share the results of a preliminary small-sample pilot study that tested this system with Japanese university students, to see whether it would yield lower FLA levels than an unsimplified embodied conversational agent. Analysis of conversational output showed that 87.4% of dialogue sentences generated by the proposed multi-agent system fell within one predicted CEFR level of the learner's self-assessed proficiency, compared to 54.1% for the unsimplified agent. This suggests that the novel system is better able to produce output at an appropriate level for the learner. Though this study did not yield statistically significant evidence that the system reduces FLA levels in Japanese learners of English, likely due to a small sample size, it provides usability findings and culturally-informed design insights that will inform future study.

Problem

• Foreign Language Anxiety
• Second Language Acquisition
• Embodied Conversational Agents
• CEFR
• Language Proficiency

Innovation

• level-appropriate dialogue
• embodied conversational agents
• multi-agent LLM system
• CEFR alignment
• foreign language anxiety reduction