Cyberism Perception-Interaction Layer: A Framework for AI-Enabled Sensing Between Humans and Cyberspace

Artificial intelligence (AI) and sensing technologies increasingly mediate how humans encounter physical, bodily, and digital environments. Yet intelligent-sensor frameworks still emphasize accuracy, latency, power, and robustness while giving less attention to the perceptual relation created between humans and cyberspace. This conceptual and integrative review uses directed conceptual synthesis, purposive literature selection, contrastive case analysis, and negative-case testing to develop the perception-interaction layer of Cyberism for AI-enabled sensing. The synthesis distinguishes exteroceptive cyber-sensing, which extends or reconstructs familiar external senses; interoceptive cyber-sensing, which externalizes and interprets bodily states; and machine-native cyber-sensing, which translates modalities such as radar and LiDAR into humanly actionable perception. These sub-domains are mapped, on a many-to-many basis, to three proposed evaluation constructs that differ in measurement maturity: perceptual coherence, interoceptive agency, and perceptual explainability. Three hardware-constant examples, an integrated worked crosswalk, and an engineering signal-chain comparison show how the same sensing hardware can require different interfaces, monitoring provisions, and human outcome measures when its perceptual role changes. The framework remains a proposal requiring empirical validation, but it contributes an auditable method for linking AI sensor pipelines to uncertainty communication, feedback-induced distribution change, appropriate reliance, human agency, oversight, and differential performance.