Spanning approximately 120,000 words, this interdisciplinary work bridges cognitive science, labor economics, and human-computer interaction to explore the erosion of tacit knowledge (know-how that cannot be explicitly codified) in four craft-intensive domains: surgery, violin lutherie, architectural hand-drafting, and commercial fishing navigation. The document synthesizes a 30-month ethnographic study of 45 master practitioners and their apprentices, alongside a controlled experiment comparing AI-augmented training vs. traditional mentoring. The central thesis challenges the prevailing assumption that all expert performance can be reduced to algorithms. Instead, the author identifies “residual tacit domains” — skills requiring embodied feedback loops, haptic perception, and context-sensitivity that current AI systems fail to replicate. The document is divided into four sections: (1) a taxonomy of tacit knowledge, (2) case studies of failure when AI replaces rather than augments, (3) design principles for “preserving AI” that documents rather than automates, and (4) policy recommendations for vocational education. Major findings show that apprentices trained solely via simulation-based AI lack adaptive repair skills when real-world conditions deviate from training data. The work concludes with a philosophical argument for preserving inefficiency in skill transmission as a form of cultural and cognitive diversity.
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