How Should Business Schools Adapt to the AI Era? A Framework for Institutional AI Transition Readiness
Bibliographic record
Abstract
This presentation introduces a reproducible, evidence-based framework for assessing AI transition readiness in higher education, with a particular emphasis on business school strategy and institutional governance. Delivered as the keynote address for Global Entrepreneurship Week 2025 at the Postgraduate Hackathon co-hosted by Humber College (Canada), CBS International Business School (Germany), the University of Salford (U.K.), and GEA College (Slovenia), the session synthesizes and operationalizes the core findings of my doctoral dissertation, Toward AI-EdBOK in Industry 4.0 (DOI: 10.5281/zenodo.16990734). Building on the dissertation’s conceptual and methodological architecture, the presentation highlights the AI Transition Readiness Index (TRI) and the G-PLAC Framework (Governance–Programs–Learners–Agreements–Classification). Together, these models evaluate both an institution’s Will (AI governance, policy maturity, and intent) and its Way (operational capacity to deliver AI-enabled teaching and learning). The research pipeline combines deterministic Python chatbots, NLP-driven snippet extraction, R-based statistical validation, Six Sigma Gage R&R and Monte Carlo methods, and risk-based principles adapted from the IMF AML/CFT supervisory framework. The deck positions TRI and G-PLAC as rigorous, reproducible tools that help business schools and colleges respond to the accelerating demands of AI adoption. By benchmarking Ontario’s 24 public colleges against the QS World Top 10 AI universities and mapping these results to labour market indicators, the work demonstrates how AI readiness in education can be connected to workforce demands in the Fourth Industrial Revolution. This resource provides academic leaders, policymakers, and researchers with a structured, methodology-backed approach to institutional AI transition—bridging conceptual theory, practical governance needs, and empirical evidence.
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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 | 0.058 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.007 | 0.050 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| 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".