Users feel frustrated when they do not know when to speak with LLM-based agents. Technical delays disrupt the natural rhythm of conversation (turn-taking), yet there is little understanding of how these specific delays impact the back-and-forth flow of interaction. To address this, we analyzed human-agent conversations in social VR to measure timing differences. We used conversation analysis techniques to track specific timing metrics, such as how long it takes to respond (response latencies) and how agents handle interruptions (repair attempts). We found that agents are significantly slower to respond with a median of 4.1 seconds compared to a human’s 1.2 seconds. We identified a “conversational timing drift”, noting that agents struggle with start-up latency, i.e., taking too long to start speaking, and wind-down latency, i.e., failing to stop speaking quickly when a user interrupts them. This is the first study to empirically quantify human-agent conversational latencies within VR. We offer design suggestions to help future agents manage conversational timing better, ultimately improving natural conversation and user experience.

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.