Frontiers in Virtual Reality
7
Doi:
https://doi.org/10.3389/frvir.2026.1860842
Research in virtual reality and related immersive environments increasingly relies on avatars as perceptual and embodied carriers of social identity, yet existing avatar systems offer limited control over identity cues, restricted demographic diversity, and weak reproducibility. These constraints narrow the questions that can be asked about embodiment, social categorization, and intergroup processes. We introduce synthetic embodiment as a theory and method framework for studying AI-generated identity cues in virtual reality and related immersive environments, including persistent metaverse contexts. The framework reconceptualizes identity not as a fixed categorical label, but as a multidimensional and controllable design space varying across phenotype, gender presentation, age, cultural markers, ambiguity, and intersectional cue structure. We further propose the Embodiment Categorization Bias (ECB) model, which specifies a testable process through which identity cues operate as perceptual inputs, shape embodiment strength, guide social categorization, and influence intergroup outcomes such as bias, empathy, perceived threat, cooperation, and social distance. To support empirical work, we outline a methodological pipeline for avatar generation, standardization, immersive integration, perceptual validation, and fairness auditing. An illustrative feasibility demonstration shows how theoretically specified identity variables can be translated into structured generative prompts and preliminary avatar stimuli across multiple generative AI systems. This demonstration is not presented as perceptual validation or as empirical evidence for the ECB model, but as an illustration of the early stimulus-construction stage of the synthetic embodiment pipeline. Together, synthetic embodiment positions AI-generated avatars as research infrastructure for more precise, scalable, and ethically accountable studies of identity in immersive environments.