AI agents aim to reduce foreign language anxiety with level-appropriate dialogue

Researchers have developed a novel multi-agent embodied conversational system designed to reduce foreign language anxiety (FLA) in second language acquisition (SLA). The system generates dialogue tailored to a learner's proficiency level, based on the Common European Framework of Reference for Languages (CEFR). A preliminary study with Japanese students learning English indicated that the system produced dialogue at an appropriate level for learners more frequently than an unsimplified agent, though it did not yield statistically significant FLA reduction due to a small sample size. AI

IMPACT Could offer new tools for language learners struggling with anxiety and improve educational AI applications.

RANK_REASON The cluster contains an academic paper detailing a novel system and preliminary study results. [lever_c_demoted from research: ic=1 ai=1.0]

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• Common European Framework of Reference for Languages
• English
• foreign language anxiety
• Japanese
• Krishan Rajaratnam
• large language models
• second-language acquisition

• paper
• product
• other

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