9 items with this tag.
Closing the infrastructure series: what fourteen tools across three modules enable in practice, the Claude commands that invoke them, and what's still missing.
AI assessment scales and similar policies are taxonomies of containment that ask how to protect existing assessment practices from AI, not whether those practices remain fit for purpose. This post argues that they're asking the wrong question, and examines what higher education might be asking instead, with particular implications for health professions education.
Most universities have responded to AI by rewriting assessment policies and running prompt-writing workshops. Context engineering demands something different: infrastructure decisions that commit institutions to a direction. This post explains what context engineering involves, why it matters for health professions education, and why the gap between changing words and changing structures is where most institutions are stuck.
Most advice on organising your notes for AI treats it as a retrieval problem. The harder problem is translation; making your thinking machine-readable without losing what makes it yours. Contextual interoperability is the infrastructure that enables genuine AI collaboration in scholarly work.
The capacity to make human knowledge machine-readable while preserving its meaning, enabling AI to reason within a specific intellectual framework.
A model for accessing AI capabilities while personal context remains private and under individual control, separating computational intelligence from data ownership.
A framework positioning personal context—knowledge, values, goals, thinking patterns—as central to human-AI collaboration, with individuals maintaining control over their cognitive environment while accessing AI capabilities.
Professional curricula are extensively documented but not systematically queryable, creating artificial information scarcity that makes compliance reporting and quality assurance labour-intensive. This essay proposes a three-layer architecture — graph databases as the source of truth for curriculum structure, vector databases for semantic content retrieval, and a Model Context Protocol layer for stakeholder access — that transforms documentation into operational infrastructure. The architecture incorporates temporal versioning for longitudinal evidence, role-based access controls for multi-stakeholder environments, and internal quality audit against institutional policy alongside external regulatory compliance, enabling verification in hours rather than weeks.
This essay proposes 'context sovereignty' as a framework for maintaining human agency in AI-supported learning, arguing that context engineering — not just prompting — is the key to meaningful human-AI collaboration.