Call for abstract: The AI-University in Practice: Leadership, Policy, and Quality Assurance for the Future of Higher Education

2026-07-01

Artificial intelligence (AI) and generative AI is no longer a distant possibility in higher education. It is already reshaping how colleges and universities teach, assess, govern, support students, produce knowledge, make decisions, and demonstrate quality. Yet the AI university cannot be built through technology adoption alone. A university does not become intelligent simply because it purchases intelligent systems, integrates generative AI tools, or digitizes its administrative and academic functions.

This co-edited book positions the AI university as a new institutional condition rather than a technical upgrade. AI is reshaping teaching, learning, assessment, academic integrity, decision-making, faculty work, and institutional reputation. While it offers opportunities to expand access, personalize learning, and improve institutional effectiveness, it also raises concerns about inequality, surveillance, and superficial forms of innovation.

Bringing together scholars, university leaders, policymakers, quality assurance professionals, and educational technologists, this edited volume will examine the AI university around the world as a question of governance, responsibility, academic values, and institutional readiness for the future.

Contributors may address, but are not limited to, the following themes:

  1. The Idea of the AI University: From Digital Adoption to Institutional Transformation
  2. Leadership for the AI University: Vision, Judgment, and Strategic Responsibility
  3.  AI Policy in Higher Education: Rules, Flexibility, and the Governance of Uncertainty
  4. Quality Assurance in the Age of Artificial Intelligence: Evidence, Standards, and Trust
  5. Accreditation and the AI University: Rethinking External Review, Compliance, and Institutional Credibility
  6. Teaching and Learning in the AI University: Pedagogy, Personalization, and Human Development
  7. Assessment After Generative AI: Academic Integrity, Authentic Learning, and the Limits of Detection
  8. Faculty Roles in the AI University: Academic Labor, Professional Identity, and Human Expertise
  9. Student Success and AI: Advising, Analytics, Wellbeing, and the Ethics of Care
  10. Research and Knowledge Production in the AI University: Productivity, Integrity, and Scholarly Responsibility
  11. Data Governance and Algorithmic Accountability: Privacy, Bias, Transparency, and Institutional Risk
  12. AI, Equity, and Inclusion in Higher Education: Access, Bias, and the Uneven Benefits of Innovation

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