2 items with this tag.
Professional curricula are comprehensively documented but not systematically queryable, creating artificial information scarcity. This creates significant problems for institutions: regulatory compliance reporting consumes weeks of staff time, quality assurance requires exhaustive manual verification, and curriculum office teams cannot efficiently answer structural questions. Current approaches—manual document review, VLE keyword search, curriculum mapping spreadsheets, and purpose-built curriculum management systems—fail to expose curriculum structure in queryable form. We propose an architecture where graph databases become the source of truth for curriculum structure, with vector databases for content retrieval and the Model Context Protocol providing accessible interfaces. This makes documented curriculum structure explicitly queryable—prerequisite chains, competency mappings, and assessment coverage—enabling compliance verification in hours rather than weeks. The architecture suits AI-forward institutions—those treating AI integration as ongoing strategic practice requiring active engagement with evolving technologies. Technology handles structural verification; educators retain essential authority over educational meaning-making. The proposal argues for removing technical barriers to interrogating curriculum complexity rather than eliminating that complexity through technological solution.