The Adoption of Artificial Intelligence and Inertia in Higher Education

0. Introduction: Understanding Inertia in Higher Education AI Adoption 0.1 Historical Context and Current Landscape 0.2 Key Themes and Conceptual Framework 0.3 Structure and Chapter Overview 1. Leadership and Funding Constraints 1.1 The Role of Leadership in Technological Change 1.2 Visionary versus Reactive Leadership: Case Studies 1.3 Leadership Strategies and Risk Management 2. Institutional Hierarchies and Cultural Dynamics 2.1 Institutional Structures, Groupthink, and Conservatism 2.2 Cultural Inertia and Resistance Dynamics 2.3 Explicit and Implicit Institutional Biases 3. Regional Disparities and Global Perspectives 3.1 Geographic and Economic Factors Influencing Adoption 3.2 Comparative Case Studies: Developed versus Emerging Economies 3.3 Policies and Institutional Strategies for Mitigating Global Inequality 4. Disciplinary Variations and Cultural Resistance 4.1 Disciplinary Attitudes: STEM and Humanities 4.2 Cultural Resistance within Academic Disciplines 4.3 Comparative Analysis and Empirical Evidence 5. Generational and Personality Influences 5.1 Generational Theory and Attitudinal Differences 5.2 Personality Traits and Technological Acceptance 5.3 Empirical Analysis of Generational and Personality Impacts 6. Professional Identity, Job Security, and Existential Concerns 6.1 Professional Identity and Resistance to Technological Change 6.2 Concerns Over Job Security and Role Displacement 6.3 Identity Loss and Self-Actualization 7. Institutional Politics, Succession Planning, and Collaborative Leadership 7.1 Institutional Politics and Stability of Technological Change 7.2 Succession Planning and Leadership Continuity 7.3 Evolving Leadership Styles: Hierarchical to Collaborative Models 8. Bridging the Generational Divide: Mentorship and Sustainable Cooperation 8.1 The Importance of Mentorship in Technological Integration 8.2 Intergenerational Cooperation and Knowledge Transfer 8.3 Case Studies of Effective Mentorship and Collaboration 9, Conclusion: Strategic Frameworks for Sustainable AI Adoption 9.1 Future Research Directions and Potential Developments 9.2 Implications for Policy and Educational Practices 9.3 Final Reflections on the Future of AI in Higher Education