Designing Extended Intelligence: A Taxonomy of Psychobiological Effects of XR–AI Systems for Human Capability Augmentation

Extended Reality (XR) and Artificial Intelligence (AI) are increasingly converging within cyber–physical infrastructures, including digital twins, the Spatial Web, and smart-city systems. These environments require new frameworks for understanding how human performance emerges through sustained interaction with immersive interfaces and adaptive computational agents. This paper introduces the TAXI–XI-CAP framework, a two-layer model that links psychobiological mechanisms of XR–AI interaction to higher-level, experimentally testable capability constructs. The TAXI layer defines 42 mechanisms spanning perception, cognition, physiology, sensorimotor control, and social coordination, while XI-CAP organizes these into capability patterns such as remote dexterity, distributed cognition, and adaptive workload regulation. Derived through a theory-guided synthesis across XR, neuroscience, and human–automation interaction, the framework models performance as emerging from interacting mechanisms under real-world constraints. A validation-oriented research agenda is proposed, emphasizing mechanism-level measurement, capability-level evaluation, and longitudinal testing. The TAXI–XI-CAP framework provides a structured basis for hypothesis generation, comparative analysis, and empirical validation of XR–AI systems, supporting the development of reliable, scalable, and human-centered Extended Intelligence infrastructures.