Meta-Books: Restructuring Organisational Knowledge for Agentic AI Reasoning
TL;DR
The paper’s most distinctive argument is that meta-books are the first organisational KM framework explicitly aligned with the cognitive architecture of human learning — integrating schema theory, cognitive load theory, dual coding, retrieval practice, the spacing effect, meaningful learning, and connectionist neuroscience — with implications for universities, professional certification, and all structured knowledge delivery systems.
Abstract
Organisations hold significant knowledge capital that their AI systems cannot access as knowledge. The knowledge bases most organisations maintain — manuals, onboarding documents, process documentation, and accumulated expertise — are structurally incompatible with AI reasoning: there are no dependency structures to traverse, no role classifications to honour, no relational context to surface. This paper introduces meta-books as a knowledge management design response to this structural problem. A meta-book transforms a conventional organisational document into a typed, dependency-linked knowledge graph. Source content is decomposed into typed knowledge objects — concepts, procedures, examples, controversies, and reflective insights — connected by semantically meaningful edges such as prerequisite-of, illustrates, contradicts, and applies-to. Every node carries pedagogical metadata: depth layer, role classification, engagement time, and prerequisite links. Unlike retrieval-augmented generation, meta-books restructure source content itself, making dependency, context, and role visible to AI at the point of reasoning, and support cross-source integration allowing knowledge nodes to migrate across individual, team, and organisational graphs without losing relational context. Meta-books reposition organisational knowledge as a compound, AI-traversable asset with implications across the full employee lifecycle — from recruitment and adaptive onboarding to knowledge retention beyond individual tenure. The paper positions meta-books against learning object metadata, adaptive hypermedia, semantic web standards, and enterprise knowledge graphs, demonstrating that no existing standard simultaneously occupies the intersection of typed knowledge structure, pedagogical metadata, and AI queryability. Five design principles are derived using design science research methodology and evaluated through illustrative decomposition of a representative organisational document. The paper’s most distinctive argument is that meta-books are the first organisational KM framework explicitly aligned with the cognitive architecture of human learning — integrating schema theory, cognitive load theory, dual coding, retrieval practice, the spacing effect, meaningful learning, and connectionist neuroscience — with implications for universities, professional certification, and all structured knowledge delivery systems.