title: Quantifying AI Readiness: Bridging Ontario's Community Colleges and Industry 4.0 Demands
title: A dissertation proposal summary
titleType: Subtitle
Creators: name: Tse, Carmel
Publisher: name: Zenodo
Year: 2025
Subjects: subject: Artificial intelligence
subject: Educational sciences
subject: FOS: Educational sciences
subject: Canada
subject: Ontario
subject: Community Colleges

description: Abstract

This dissertation examines the readiness of Ontario’s 24 public community colleges to equip the workforce with AI-related competencies necessary for Industry 4.0. From a business administration perspective, this research assesses how effectively these institutions align their artificial intelligence (AI) curricula with labor market demands and broader economic objectives. The study introduces ConnectivAI, an evolution of the Connectivism theory, to conceptualize how AI is transforming organizational learning, workforce training, and strategic talent development. AI, particularly the advent of Generative AI (e.g., OpenAI’s ChatGPT), presents both challenges and opportunities for workforce readiness, necessitating a data-driven evaluation of institutional adaptability.

To quantify AI integration, this dissertation develops the AI-Readiness Index (ARI), a business intelligence metric that assesses colleges based on AI course offerings, policy clarity, and student enrollment trends. The ARI is derived from AI-driven data analytics, web scraping, and survey validation, providing an evidence-based framework for measuring institutional preparedness.

A comparative benchmarking analysis will position Ontario’s colleges against other Canadian provinces and leading AI-education hubs globally, identifying strategic gaps and best practices. Additionally, the study evaluates the return on investment (ROI) of AI education, linking AI curriculum adoption with labor market outcomes and employer demand. The research also examines how policy frameworks and quality assurance measures influence the adoption of AI within Ontario’s higher education sector, drawing comparisons with AI policy approaches in the United States.

Findings from this study will provide actionable insights for college administrators, policymakers, and business leaders, offering a strategic roadmap for AI-driven workforce development. By bridging the gap between higher education and labor market needs, this research aims to contribute to Ontario’s competitive positioning in the global AI economy, ensuring that postsecondary institutions function as strategic enablers of economic growth and innovation in Industry 4.0.

Acknowledgment

I would like to express my sincere gratitude to Dr. Isaac Ahinsah-Wobil of SSBM for his invaluable guidance and support as my dissertation supervisor, Dr. Ace Vo of Loyola Marymount University in Los Angeles, CA for his coaching on research methodologies and Hugo Lau, who assisted with research administration. I am also deeply indebted to Dr. Bill Ip, Adjunct Professor of Robotics at Lone Star College, Houston, TX, whose initial insights and discussions on Industry 4.0 were instrumental in shaping the focus of this research. I extend my best wishes to Dr. Ip for a full and speedy recovery.
description: Table of Contents

1.      Introduction

o    1.1 Background and rationale

o    1.2 Scope and definitions

o    1.3 Study period (2020-2024)

o    1.4 Research objectives

o    1.5 Significance of the study

2.      Literature review

o    2.1 Quality assurance in higher education

o    2.2 AI in academic QA

o    2.3 AI policies and Integration in Canadian and global contexts

o    2.4 Global comparisons of QA standards

o    2.5 Existing data analysis and research gaps

o    2.6 Economic impacts of AI education

o    2.7 ConnectivAI: An AI-driven evolution of Connectivism

3.      Research methodology

o    3.1 Data collection

o    3.2 Sample and scope

o    3.3 Limitations

o    3.4 Analysis tools: Lean Six Sigma and DMAIC

o    3.5 Precision level: Six Sigma vs. Three Sigma

4.      AI-Readiness Index (ARI) 

o    4.1 ARI and the G-PLANET-X framework

o    4.2 G-PANELT-X variable Definitions

o    4.3 Weighting (W1-W8)

o    4.4 Sensitivity report

o    4.5 Normalization

o    4.6 ARI formula

o    4.7 Baseline benchmarking

o    4.8 Interplay of the ARI attributes

o    4.9 Using tiered indices for strategic improvement

o    4.10 Comparisons with Canadian and international standards

o    4.11 Rationale for aggregated data approach

5.      Current QA practices and future directions: A benchmarking perspective

6.      Expected outcomes and relevance

7.      Conclusion and next steps