1 citations · 1 across the 10 of their papers we have counts for
10 papers
A Dynamic Coupling Theory of Expertise Through Thinking Flow and Workflow Evolution
Annie Yuan
Expertise has long been explained through tacit knowledge, deliberate practice, skill acquisition, and expert performance. While these perspectives have advanced understanding of e…
From Craft Practice to Aesthetic Cognition Transmission: Workflow Cognition Translation for AI-native Intangible Cultural Heritage Education
Annie Yuan
Intangible Cultural Heritage (ICH) education has traditionally relied on apprenticeship, embodied participation, and long-term engagement with masters, materials, and cultural envi…
Tacit Signal Infrastructure: Towards AI Systems that Model Expert Sensing Over Time
Annie Yuan
Current generative AI systems are increasingly effective at processing explicit knowledge, including retrieving information, summarising documents, generating explanations, and sup…
Expert Cognition Dashboard: From Learning Analytics to Cognition Intelligence in AI-Driven Education
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…
Modelling Expert Cognition Beyond Behaviour: Towards Interpretation, Tension, and Value Structures
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 r…
AI Expert Twin: Capturing Expert Cognition for Human-Centred, Practice-Based Learning
Annie Yuan, Xiaohua Chen, Kalina Yacef +1
Tacit knowledge embedded in expert practice remains difficult to capture, formalise, and scale. While AI-driven educational systems have advanced personalisation, learner modelling…
Think Before You Lie: How Reasoning Leads to Honesty
Ann Yuan, Asma Ghandeharioun, Carter Blum +6
While existing evaluations of large language models (LLMs) measure deception rates, the underlying conditions that give rise to deceptive behavior are poorly understood. We investi…
Language Models Struggle to Use Representations Learned In-Context
Michael A. Lepori, Tal Linzen, Ann Yuan +1
Though large language models (LLMs) have enabled great success across a wide variety of tasks, they still appear to fall short of one of the loftier goals of artificial intelligenc…
Beyond the Rosetta Stone: Unification Forces in Generalization Dynamics
Carter Blum, Katja Filippova, Ann Yuan +8
Large language models (LLMs) struggle with cross-lingual knowledge transfer: they sometimes hallucinate when asked in one language about facts expressed in a different language dur…
Who's asking? User personas and the mechanics of latent misalignment
Asma Ghandeharioun, Ann Yuan, Marius Guerard +3
Despite investments in improving model safety, studies show that misaligned capabilities remain latent in safety-tuned models. In this work, we shed light on the mechanics of this…