Annie Yuan

Annie Yuan

Intangible Cultural Heritage (ICH) education has traditionally relied on apprenticeship, embodied participation, and long-term engagement with masters, materials, and cultural environments. While these modes of transmission remain essential, they are difficult to scale. Existing digital heritage initiatives have expanded documentation and access, but often preserve artefacts, procedures, and representations of practice rather than the aesthetic and cognitive structures through which expertise operates. This paper argues that the future challenge of ICH education is not only the transmission of craft techniques, but the scalable transmission of aesthetic cognition: the perception, judgement, interpretation, and culturally situated meaning-making through which aesthetic expertise develops. Drawing on aesthetic education, tacit knowledge, cognitive apprenticeship, and expert cognition, we propose a shift from craft transmission to Aesthetic Cognition Transmission. To support this shift, we introduce Workflow Cognition as a model of how experts coordinate perception, judgement, decision-making, and action within evolving workflows. We then propose Workflow Cognition Translation as a methodological framework for transforming expert workflow cognition into computable educational representations for AI-native learning systems. The paper makes three contributions: it reframes ICH education around aesthetic cognition transmission; introduces Workflow Cognition Translation as a method for representing expert aesthetic cognition; and outlines an AI-native cognitive apprenticeship infrastructure involving AI Expert Twins, workflow-based tutoring, and progressive learner participation. Rather than replacing masters, workshops, or embodied practice, the framework positions AI as a cognition mediation infrastructure for expanding access to heritage expertise.

Annie Yuan, Xiaohua Chen, Kalina Yacef et al.

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.

Annie Yuan

Current generative AI systems are increasingly effective at processing explicit knowledge, including retrieving information, summarising documents, generating explanations, and supporting codified workflows. However, high-level expertise also depends on tacit sensing: perceiving weak signals, recognising emerging tensions, detecting coherence degradation, and anticipating instability before formal indicators appear. Existing AI education, AI literacy, and human-AI collaboration frameworks remain centred on prompting, task execution, and productivity support and are poorly equipped to address this tacit layer of expert cognition. This vision paper argues that next-generation AI systems should move beyond explicit knowledge processing toward the longitudinal modelling of expert tacit sensing. It introduces Tacit Signal Infrastructure as a layer for capturing, structuring, modelling, interpreting, and validating expert tacit signals over time. It further defines Long-term Cognitive Operations as the practices required to maintain and govern such systems, including memory curation, semantic organisation, tacit signal modelling, reasoning calibration, and cognitive governance. Building on this framing, the paper proposes the Cognitive Operations Manager as a prototype AI-native professional role for coordinating tacit signal modelling, semantic modelling, AI system calibration, expert validation, and ethical governance. It also introduces the Cognitive Operations Research and Training Framework (CORTF) to support research, education, and workforce development. The paper contributes a conceptual foundation for designing AI systems that model expert sensing over time, positioning cognition as an infrastructural, operational, and professional domain in persistent human-AI systems.

Annie Yuan

Current AI-driven educational systems primarily rely on behavioural analytics, performance metrics, and content-level interactions to model learning. While these approaches provide useful indicators of learner activity, they are insufficient for representing the expert cognition used to interpret learner development, identify misconceptions, and make adaptive pedagogical decisions. Existing learning analytics dashboards largely visualise learner behaviour for human instructors, rather than embody expert cognition as a reasoning infrastructure for AI-native education. This paper introduces the Expert Cognition Dashboard (ECD), a cognition-centred reporting infrastructure for AI Twin-driven education systems. ECD models expert cognition within dashboard systems, enabling learner behaviours to be interpreted through expert-like cognitive structures rather than treated as raw behavioural signals. The proposed framework transforms student interactions into interpretable cognition structures through AI Tutor analysis and multi-level dashboard aggregation. Its architecture organises cognition across three layers: individual cognition dashboards, class cognition dashboards, and AI Twin expert dashboards for cross-group reasoning and adaptive intervention. Building on the AI Expert Feedback Ecology framework, ECD redefines dashboards as cognitive middleware that connects learner behaviours with AI-driven expert reasoning. By modelling interpretation, identity cognition, value recognition, misconception patterns, and learning tension, ECD enables AI Twins to identify recurring learner difficulties, generate adaptive tasks, and support personalised intervention. The paper argues for a shift from learning analytics toward Cognition Intelligence, positioning dashboards as foundational cognition infrastructures that embed expert reasoning into future AI-native education systems.

Annie Yuan

Existing computational models of expertise primarily focus on observable behaviour or decision outcomes, failing to capture the internal cognitive structures that generate expert reasoning. In this work, we introduce the Expert Identity Cognition Model (EICM), a three-layer framework for modelling expert cognition beyond behaviour. EICM conceptualises expert cognition as an identity-structured process operating within situational constraints, where constraints are interpreted through internal tensions arising from competing identity commitments and stabilised into value structures that guide action. Unlike behaviour-centric or constraint-driven approaches, EICM positions tension as the central cognitive mechanism connecting world structure and decision formation. We argue that expert cognition is not merely behavioural adaptation under constraints but an identity-structured negotiation process that produces stable judgement patterns across contexts. The framework provides a new perspective for modelling tacit knowledge, expert judgement, and cognitive consistency in domains including professional practice, cultural expertise, and design reasoning.