AI-Mediated Expert Wisdom Transmission: A Dual-Dimensional Framework of Process Alignment and Independent Retention

Background: Expert wisdom transmission faces an "internalization gap"—learners can reproduceanswers but struggle to reconstruct reasoning paths when confronting unfamiliar tasks. Whileartificial intelligence advances offer new opportunities for technologizing wisdom transmission,the field lacks unified evaluation frameworks and falsifiable implementation standards.Objective: This study proposes Expert Wisdom AI-fication Theory (EWAT), with the core being theestablishment of a dual-indicator evaluation system of Structural Correspondence Index (SCI) andCognitive Independence Index (CII), providing a measurable and reproducible technicalframework for AI-assisted wisdom transmission.Methods: We construct mapping models between Expert Process Profiles (EPP) and LearnerCognitive States (LCS), developing the Structural Correspondence Index (SCI) and CognitiveIndependence Index (CII) as evaluation tools. Three AI-assisted mechanisms are designed:Socratic questioning, minimalist process tracking, and desirable difficulty regulation. Fivefalsifiable propositions (P1-P5) are proposed for verification.Claims: Compared to traditional teaching methods, AI-assisted wisdom transmission withequivalent instructional inputs should yield: (1) higher process alignment (SCI); (2) strongerindependent retention capabilities (CII); (3) process features that outperform final grades inpredicting long-term performance.Boundaries: This study proposes a conceptual theoretical framework. All quantitative thresholdsrequire calibration and verification in practical applications; cross-cultural applicability requiresfurther research. When failures occur or dependence on external assistance rebounds,applications should be downgraded or suspended.Conclusion: EWAT provides an independence-first design language, supporting educationaltransformation from traditional knowledge transmission to deep capability activation, providing aframework foundation for open science replication and improvement.