AI Expert Twin: Capturing Expert Cognition for Human-Centred, Practice-Based Learning

TL;DR

This paper presents the AI Expert Twin, a framework that models expert cognition to enhance human-centered, practice-based learning through AI systems.

Contribution

It introduces a formalized, transferable model of expert cognition capturing tacit knowledge for integration into AI-powered educational tools.

Findings

Demonstrated the framework's feasibility in a cultural heritage workshop

Formalized expert cognition as a three-layer representation

Supports scalable, practice-based learning across domains

Abstract

Tacit knowledge embedded in expert practice remains difficult to capture, formalise, and scale. While AI-driven educational systems have advanced personalisation, learner modelling, affective support, and self-regulated learning, they less often model the tacit reasoning and context-sensitive judgement that underpin expert practice in practice-based domains. This paper introduces the AI Expert Twin, a cognition-centric framework that models expert knowledge as structured, computable representations of procedural actions, semantic concepts, and decision processes. The framework also considers how value-laden preferences, trade-offs, and uncertainty shape expert judgement in practice. We formalise expert cognition as a three-layer representation and capture knowledge from experts under this model, laying the groundwork for integration into AI-powered educational system. A case study in a cultural heritage workshop demonstrates the feasibility of the approach in a real-world setting. The framework is designed to be transferable across domains such as vocational education and creative industries. By embedding expert heuristics into AI while maintaining transparency and learner agency, the AI Expert Twin offers a novel path towards scalable, practice-based learning and invites further research on ethical, human-centred applications of AI in education.