
The Presence Engine™ Living Thesis presents Human-Centric AIX™ (AI Experience), a framework for building AI systems with contextual continuity architecture instead of stateless task execution. This work addresses the fundamental gap in current AI design: systems that optimize for productivity while failing at presence, memory, and sustained human development. Grounded in Bandura’s social learning theory, Hogan’s critical thinking dispositions research, and the OCEAN personality framework, this thesis argues that AI systems train humans through repeated interaction—and current architectures are training poorly. The proposed solution: privacy-first infrastructure modeling how humans think across time rather than what they say in discrete moments. "The impact of AI on humanity may have less to do with the way we use it than how it is built. Presence Engine offers a way of favoring continuity and coherence over present shock and calibration. It's an approach worth our attention." — Douglas Rushkoff, Author of Team Human and Present Shock This is active research. The framework includes technical architecture, theoretical grounding, and early prototype evidence, but requires longitudinal validation. Version 4 (forthcoming) will expand real-world testing and results and continued technical methodology, evidence, and honest assessment of limitations. Current Status: Phase 1 Validation (5-10 users) is underway. Version 4 (forthcoming) will integrate longitudinal data and expanded failure mode analysis. Follow the Technical Architecture Specification for real-time methodology updates.
Machine Learning, Artificial intelligence, Presence Engine, Thesis, Large Language Models, Human-centric AI, Generative AI, Human-Centric AIX
Machine Learning, Artificial intelligence, Presence Engine, Thesis, Large Language Models, Human-centric AI, Generative AI, Human-Centric AIX
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