Toward AI-EDBOK in Industry 4.0: Quantifying AI Transition Readiness at Ontario’s Community Colleges

Abstract

This research addresses a critical gap in the standardized assessment of Artificial Intelligence (AI) readiness across Ontario’s 24 publicly funded community colleges. In response to the structural challenges of Industry 4.0, it introduces a structured, reproducible framework culminating in the AI Transition Readiness Index (TRI). Unlike conventional compliance-focused tools, this model emphasizes methodological rigor, cross-institutional comparability, and policy alignment.
The methodology is organized into three tiers. The first establishes a conceptual base derived from Constructivism, Connectivism, and the author’s ConnectivAI theory, which frames institutional learning as a networked, algorithmically shaped process. The second tier distinguishes between governance intent (Will) and implementation capacity (Way), operationalized through the G-PLAC framework—a calibrated realignment of the original G-PLANET-X model. The third tier integrates statistical due diligence, drawing on Lean Six Sigma practices, IMF benchmarking logic, and established principles of data validation. Leading indicators—such as AI governance structures—support predictive insight, while lagging indicators—such as program offerings and employment alignment—serve to confirm institutional outcomes.
Beyond institutional diagnostics, the study aspires to lay the foundation for an Artificial Intelligence in Education Body of Knowledge (AI-EdBOK), modeled after the Project Management Body of Knowledge (PMBOK) developed by the Project Management Institute to consolidate domain-specific expertise. AI-EdBOK is envisioned as a scalable, evolving reference to support evidence-informed governance, curriculum modernization, and sector-wide alignment in the era of intelligent systems.