Toward AI-EdBOK in Industry 4.0: Quantifying AI Transition Readiness at Ontario's Community Colleges
Bibliographic record
Abstract
This doctorate thesis examines the AI readiness of Ontario's publicly funded community colleges within the framework of Industry 4.0, focusing on governance structures and operational capacity for integrating artificial intelligence (AI) into education. To address the lack of standardized assessment tools, the study introduces the AI Transition Readiness Index (TRI), a benchmarking framework based on the G-PLAC model, which evaluates institutional governance ("Will") and operational capacity ("Way"). Using deterministic chatbot evaluations, statistical normalization, and Monte Carlo simulations, the research assesses governance maturity and operational indicators such as AI program offerings, learner engagement, policy alignment, and curriculum breadth. Findings reveal variability in AI readiness, with institutions like Seneca and Conestoga leading, while others show gaps in transparency, strategic alignment, and program diversity. A rubric-based assessment further categorizes colleges into tiers of alignment with AI-related objectives in Strategic Mandate Agreements (SMAs), highlighting strengths in Workforce Alignment and Community/Industry Partnerships, and areas for improvement in AI programming and strategic commitment. Recommendations include enhancing AI governance frameworks, expanding interdisciplinary AI curricula, and institutionalizing the TRI for longitudinal tracking. This research contributes to AI governance in education and supports Ontario's strategic goals in AI readiness and innovation.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".