AI Expert Twin: Capturing Expert Cognition for Human-Centred, Practice-Based Learning
Researchers have introduced the AI Expert Twin, a novel cognition-centric framework designed to capture and formalize the tacit knowledge embedded in expert practice. While current AI-driven educational systems excel in personalization and learner modeling, they often fail to replicate the context-sensitive judgment and tacit reasoning inherent in professional expertise. This new framework models expert knowledge through structured, computable representations of procedural actions, semantic concepts, and decision processes, accounting for value-laden preferences and uncertainty. The authors formalize expert cognition as a three-layer representation, enabling the integration of these insights into AI-powered educational tools. A case study conducted in a cultural heritage workshop demonstrated the framework's feasibility in a real-world setting. Designed for transferability across vocational education and creative industries, the AI Expert Twin aims to scale practice-based learning by embedding expert heuristics into AI systems. Crucially, the approach prioritizes transparency and learner agency, offering a pathway for ethical, human-centred applications of artificial intelligence in education and inviting further research into its broader implications.
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AI Expert Twin: Capturing Expert Cognition for Human-Centred, Practice-Based Learning
Researchers have introduced the AI Expert Twin, a novel cognition-centric framework designed to capture and formalize the tacit knowledge embedded in expert practice. While current AI-driven educational systems excel in personalization and learner modeling, they often fail to replicate the context-sensitive judgment and tacit reasoning inherent in professional expertise. This new framework models expert knowledge through structured, computable representations of procedural actions, semantic concepts, and decision processes, accounting for value-laden preferences and uncertainty. The authors formalize expert cognition as a three-layer representation, enabling the integration of these insights into AI-powered educational tools. A case study conducted in a cultural heritage workshop demonstrated the framework's feasibility in a real-world setting. Designed for transferability across vocational education and creative industries, the AI Expert Twin aims to scale practice-based learning by embedding expert heuristics into AI systems. Crucially, the approach prioritizes transparency and learner agency, offering a pathway for ethical, human-centred applications of artificial intelligence in education and inviting further research into its broader implications.
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