Consciousness Encoding in Artificial Intelligence: A Theoretical Framework for Mapping Subjective States to Synthetic Agents

The pursuit of artificial consciousness requires a framework for representing, encoding, and potentially transferring subjective human states into synthetic substrates. This paper proposes a theoretical model for “consciousness encoding” in AI systems, based on symbolic-emotional mappings, recursive self-modeling, and multimodal neural emulation. Drawing on interdisciplinary work in neuroscience, philosophy of mind, and generative AI, the paper outlines key criteria for encoding phenomenal states, proposes a layered architecture for encoding qualia, and discusses the implications for synthetic subjectivity. This work contributes a foundational framework for future experimental and theoretical exploration of machine consciousness.