AI Governance Deficiency: A Barrier to Industry 4.0 Readiness
Bibliographic record
Abstract
I am a doctoral candidate of the Swiss School of Business and Management (SSBM) and learning consultant researching the integration of Artificial Intelligence (AI) in teaching and learning. My work — documented in my concept note (Tse, 2024) and dissertation proposal (Tse, 2025) — focuses on Ontario’s community colleges’ readiness for the Fourth Industrial Revolution (Industry 4.0). My research reveals significant ethical and social challenges that directly impact both teaching practices and learning outcomes. This assignment is divided into two parts: · Part 1 outlines the ethical and social challenges stemming from AI governance deficiencies in education. · Part 2 introduces a comprehensive, AI-specific governance framework to measure and address these challenges, aiming to provide structured guidance for AI adoption in education. AI governance and leadership in educational institutions directly correlates with workforce readiness in Ontario communities. This study also introduces ConnectivAI, an extension of Connectivism (Siemens, 2005; Downes, 2010), to conceptualize AI's role in organizational learning, workforce training, and talent development. ConnectivAI expands Connectivism’s principles by leveraging generative and analytical AI to shape knowledge networks, enhance adaptability, and foster real-time human-AI interaction in education. Note The research methodology described in this document, including the AI-Readiness Index (ARI) and the G-PLANET-X framework, is currently in the proposal stage and awaits academic supervisor approval. As such, the framework and related metrics are preliminary constructs intended to guide future data collection and analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".