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.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Academic and Postgraduate
Illustrationen
9 s/w Tabellen
9 Tables, black and white
Maße
Höhe: 235 mm
Breite: 157 mm
Dicke: 20 mm
Gewicht
ISBN-13
978-1-041-10597-8 (9781041105978)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
James Hutson is Professor and Department Head of Art History, AI, and Visual Culture at Lindenwood University, St. Charles, Missouri, the United States.
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
