Tag: information-architecture
14 items with this tag.
A personal design system for scholarly output
Almost everything I produce starts as a markdown file and ends in a different format — a slide deck, a handout, a Word document, this website — and until September 2026 I designed each one from scratch. I've replaced that with a personal design system with one visual language, one identity, and a template for each format, all reading the same token file. This post explains what the system is, how the pieces fit together technically, and what's still unfinished.
What the Quartz graph view reveals about this site
The graph view is one of the reasons I built this site with Quartz, and now that there's enough writing on it the links between pages have started to form a shape. The graph opens from the top bar, shows the site's writing as a network coloured by type, and lets you choose what's on it. This post shows how to read it, and what it reveals about the site: where the ideas cluster, which concepts hold the rest together, and which ones nothing connects to.
An open scholarship workflow: What academic publishing can learn from open source
Academic publishing treats scholarship as a finished, individually owned artefact. This post describes a writing and publishing workflow built on a different premise: that a scholarly corpus could work like an open source project — readable, contributable, forkable, and never permanently owned by anyone.
Building an AI-ready knowledge base
A detailed account of a week-long project to restructure 5,819 Obsidian notes using AI as a working partner. The project involved building a 23-category taxonomy, migrating thousands of legacy notes to a consistent metadata structure, and generating AI-written descriptions for every note in the collection. The piece describes not just what was done, but how extended planning conversations, external project documentation, and careful human review at each phase made the work tractable. The most unexpected outcome was that building infrastructure for a note collection required articulating, for the first time, precisely how I think about my academic field.
A systematic literature review of knowledge graph construction and application in education
Source details Abu-Salih, B., & Alotaibi, S. (2024). A systematic literature review of knowledge graph construction and application in education.
Documentation debt
The accumulated cost of outdated, ambiguous, or poorly structured institutional knowledge — long paid invisibly by readers who work around the gaps, and exposed when AI agents read the same documents literally.
Higher Education Reference Model (HERM)
A shared vocabulary for universities, set out in business, data, application, and technology reference models, and a practical starting point for making institutional knowledge readable by software and AI agents.
Building an AI workflow for academics with structured documentation
Most advice on building an AI workflow for academics concentrates on writing better prompts. After several weeks of restructuring years of scholarly output with Claude Code, I've found the leverage lies in the structured context around the model — documentation that works as external memory, a cycle of testing rules and locking in the ones that hold, and the discipline of making implicit connections explicit. The same applies to institutions, where AI tools are only as reliable as the information architecture they're given.
Graph database
A graph database stores entities and the typed relationships between them, and is the storage layer behind a knowledge graph. For a programme team it can hold the curriculum map itself, so questions about how standards, outcomes, and assessments connect are answered by following the links instead of reading the handbooks.
Beyond document management: Graph infrastructure for professional curricula
Professional education curricula face a fundamental infrastructure problem: while comprehensively documented, they lack systematic queryability. This presentation introduces a three-layer architecture using graph databases as the source of truth for curriculum structure, supported by vector databases for content retrieval and the Model Context Protocol for stakeholder interfaces.
Vertex
A curriculum knowledge graph that turns programme documentation into queryable infrastructure. Curriculum structure, learning outcomes, assessment alignment, and regulatory standards are modelled as a graph staff can interrogate in plain language — turning quality-assurance and compliance checks that take weeks into ones that take minutes. A Snapplify product I work on as a partner and consultant.
Documentation becomes infrastructure when AI agents are the readers
When AI agents consume documentation as operational input, it undergoes a category shift from reference material to operational architecture; inaccuracies no longer merely inconvenience readers, they cause system failures. This essay argues that the primary bottleneck for institutional AI integration is not AI capability but information architecture: how institutional knowledge is structured, maintained, and made available to AI systems. Documentation written for human readers cannot function as reliable AI input without deliberate restructuring around explicit relationships and rigorous maintenance workflows. Treating this transition as a governance imperative, rather than a technical afterthought, determines whether AI integration delivers on its institutional promise.
Knowledge graph
How a knowledge graph differs from a vector database: it stores entities and the typed relationships between them, so AI can reason across connections.
Context engineering
How context engineering differs from prompt engineering: building the knowledge and structure a language model needs to reason well about your work.