Intuitive Recognition of a Virtual Agent’s Learning State Through Facial Expressions in VR

As artificial intelligence agents become integral to immersive virtual reality environments, their inherent opacity presents a significant challenge to transparent human–agent communication. This study aims to determine if a virtual agent can effectively communicate its learning state to a user through facial expressions, and to empirically validate a set of designed expressions for this purpose. We designed three animated facial expression sequences for a stylized three-dimensional avatar, each corresponding to a distinct learning outcome: clear success (Case A), mixed performance (Case B), and moderate success (Case C). An initial online survey ($n=93$) first confirmed the general interpretability of these expressions, followed by a main experiment in virtual reality ($n=30$), where participants identified the agent’s state based solely on these visual cues. The results strongly supported our primary hypothesis (H1), with participants achieving a high overall recognition accuracy of approximately 91%. While user background factors did not yield statistically significant differences, observable trends suggest they may be worthy of future investigation. These findings demonstrate that designed facial expressions serve as an effective and intuitive channel for real-time, affective explainable artificial intelligence (affective XAI), contributing a practical, human-centric method for enhancing agent transparency in collaborative virtual environments.