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Adoption of Artificial Intelligence and Inertia in Higher Education: Exploring Complex Resistance to Technological Change [Kietas viršelis]

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Pastovi nuoroda: https://www.kriso.lt/db/9781041105978.html
This research monograph explores the complex resistance to integrating Artificial Intelligence (AI) within higher education institutions. Despite the significant potential of AI to enhance education, faculty adoption remains inconsistent and is often met with skepticism.

This book investigates key factors contributing to this resistance, such as leadership deficits, funding barriers, cultural inertia, and faculty attitudes toward technological change. Drawing on qualitative and quantitative empirical data, case studies from U.S. and international institutions, and theoretical analysis, the book uncovers underlying concerns about job security and professional identity.

It points to actionable strategies for overcoming these barriers, and will be relevant for scholars, researchers, advanced students and educators grappling with issues navigating technological integration in academia, and with interests in the sociology of education, educational technology, and higher education administration.
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
James Hutson is Professor and Department Head of Art History, AI, and Visual Culture at Lindenwood University, USA.