2 items with this tag.
Higher education institutions face persistent pressure to demonstrate visible engagement with artificial intelligence, often resulting in what we characterise as innovation theatre - the performance of transformation without corresponding structural change. This paper presents a diagnostic framework that distinguishes between performative and structural integration through analysis of four operational domains: governance and accountability, resource architecture, learning systems, and boundary setting. Unlike maturity models that prescribe linear progression, this framework enables institutional leaders to assess whether organisational structures align with stated strategic intentions, revealing gaps between rhetoric and reality. The framework emerged from critical analysis of institutional AI responses but evolved toward practical utility for decision-makers operating within genuine constraints. We position this work as practitioner pattern recognition requiring subsequent empirical validation, outline specific validation pathways, and discuss implications for institutional strategy in contexts of technological disruption.
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.