AI Expert Twin Framework Models Tacit Knowledge for Cultural Heritage Education
A new framework called the AI Expert Twin aims to capture and formalize tacit expert knowledge for practice-based learning, demonstrated through a cultural heritage workshop. The system models expert cognition as three layers: procedural actions, semantic concepts, and decision processes, including value-laden preferences and trade-offs. This addresses the challenge of scaling expert judgment in domains like conservation, where tacit knowledge is rarely codified. The paper, published on arXiv (2605.01401v1), lays groundwork for integrating such models into AI-powered educational systems.
Key facts
- The AI Expert Twin framework models expert knowledge as structured, computable representations.
- It covers procedural actions, semantic concepts, and decision processes.
- The framework considers value-laden preferences, trade-offs, and uncertainty.
- Expert cognition is formalized as a three-layer representation.
- A case study in a cultural heritage workshop demonstrates feasibility.
- The paper is published on arXiv with ID 2605.01401v1.
- The approach targets practice-based domains where tacit knowledge is key.
- The framework aims to integrate into AI-powered educational systems.
Entities
Institutions
- arXiv