LLM-based Embodied Conversational Agent for Reducing Foreign Language Speaking Anxiety in Social VR
Abstract
Foreign language speaking anxiety (FLSA) poses a major challenge for English-language learners, suppressing confidence and triggering a cycle of avoidance that hinders language acquisition. To address this, we explored the use of LLM-based embodied conversational agents (ECA) in social virtual reality (VR), which provide personalized support and multimodal interaction in a contextualized environment. We developed three English-language learning scenarios in social VR and conducted a five-day mixed-methods study where participants (N=20) engaged in daily 30-minute role-play practice with an LLM-based ECA to evaluate the efficacy of the system. Quantitative results showed a significant reduction in self-reported FLAS after 3 days, along with subtle gains in speaking proficiency measures. Qualitatively, learners perceived increased confidence, attributing it to the LLM-based ECA’s non-judgmental stance, linguistic scaffolding, affective encouragement, and adaptive feedback. Our findings suggest the potential of LLM-based ECAs in social VR for language learning and offer considerations for future agent design.
Fields of Study & Subjects
Virtual Reality Applications and Impacts
Social Robot Interaction and HRI
AI in Service Interactions
Publication details
- Journal / source
- Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Pages
- 1–24
- Publication date
- 13 April 2026