2 items with this tag.
Higher education institutions face persistent pressure to demonstrate AI engagement, often resulting in 'innovation theatre' — the performance of transformation without corresponding structural change. This essay presents a diagnostic framework distinguishing between performative and structural AI integration across four domains: governance and accountability, resource architecture, learning systems, and boundary setting. Unlike linear maturity models, it reveals gaps between institutional rhetoric and operational reality. Three legitimate strategic positions — incremental, selective, and transformative — help institutions move from accidental drift toward conscious choice. Treating AI integration as ongoing strategic practice rather than fixed deployment ensures institutions preserve agency over technology decisions aligned with institutional values.
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.