Effects of AI-Powered Embodied Avatars on Communication Quality and Social Connection in Asynchronous Virtual Meetings
Bibliographic record
Abstract
Immersive technologies such as virtual and augmented reality (VR/AR) allow remote users to meet and interact in a shared virtual space using embodied virtual avatars, creating a sense of co-presence. However, asynchronous communication-essential in many real-world contexts-remains underexplored in these environments. Traditional playback-based systems lack interactivity and often fail to preserve critical contextual cues necessary for effective asynchronous communication. In this paper, we introduce AVAGENTs, AI-powered virtual avatars that replicate users' verbal and nonverbal cues from recordings of past meetings. Avagents can interpret meeting context and generate appropriate responses to questions posed by asynchronous viewers. Through a user study (N = 30), we evaluated Avagents against a traditional playback method and a voice-based AI assistant across two asynchronous meeting scenarios: analytic reasoning and affective resonance. Results showed that Avagents enhance the asynchronous communication experience by increasing social presence, sense of belonging, emotional intimacy, and other user perceptions. We discuss the findings and their implications for designing effective AI-driven asynchronous communication tools in VR/AR environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".