title: How AI-ready are Ontario's community colleges for Industry 4.0?
title: A dissertation proposal summary
titleType: Subtitle
Creators: name: Tse, Carmel
Publisher: name: Zenodo
Year: 2024
Subjects: subject: Artificial intelligence
subject: Educational sciences
subject: FOS: Educational sciences
subject: Canada
subject: Ontario
subject: Community Colleges

description: Abstract

This dissertation proposal examines the preparedness of Ontario's 24 public community colleges to deliver effective AI-related education and meet the evolving demands of Industry 4.0. The study introduces a novel metric, the AI-Readiness Index (ARI), to quantify the integration of key AI areas—Artificial Intelligence, Machine Learning, Analytics and Big Data, Robotics, and Natural Language Processing—into college curricula. The ARI will be calculated using normalized values of core and non-core AI course offerings, AI policy clarity, and student enrollment in AI-related programs. Data will be collected through AI-driven web scraping, validated by surveys of college registrars and public stakeholders. A comparative analysis will benchmark Ontario's progress against other Canadian provinces and leading international AI education providers. The study also examines current quality assurance practices in Ontario, comparing them to emerging trends in the United States, to identify potential areas for growth and innovation. This research aims to provide actionable insights for policymakers, college administrators, and curriculum developers, ultimately contributing to a more robust and responsive AI education ecosystem in Ontario and ensuring the province's workforce is prepared for the challenges and opportunities of 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, and Dr. Ace Vo of Loyola Marymount University in Los Angeles, CA for his coaching on research methodologies. 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

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