The AI-Native University: A Conceptual Framework and Maturity Model for AI Transformation in Higher Education

Abstract

Artificial intelligence (AI) is transforming higher education rapidly through advances in generative AI, learning analytics, intelligent tutoring systems, and AI-assisted decision support. Though universities increasingly adopt AI for teaching, learning, assessment, research, and administrative activities, the implementation often remains fragmented and focused on specific applications. Eхisting research has made significant contributions to areas such as Artificial Intelligence in Education (AIEd), learning analytics, educational data mining, smart universities, and digital transformation. Despite these advances, a very limited attention has been given to conceptualizing artificial intelligence as a strategic institutional capability that connects teaching, learning, assessment, research, administration, and governance within a coherent university-wide framework.
To address this gap, the present study introduces the concept of the AI-Native University (AINU). The AI-Native University refers to a higher education institution in which artificial intelligence is not treated as a standalone technology or a collection of isolated applications, but as a core institutional capability embedded across academic, administrative, and governance functions. At the same time, AI implementation remains guided by human oversight, ethical principles, institutional governance, and the broader educational mission of the university. Drawing on insights from Artificial Intelligence in Education (AIEd), digital transformation, socio-technical systems theory, and human–AI collaboration research, this study develops a conceptual framework that eхplains how universities can move from fragmented AI adoption toward a more integrated and institution-wide approach to AI-enabled transformation.
The proposed framework includes five key areas where AI can influence university operations: teaching, learning, assessment, research, and institutional management. These areas are supported by several enabling factors, including appropriate infrastructure, governance mechanisms, ethical guidelines, quality assurance processes, cybersecurity measures, and faculty development initiatives. The paper also outlines a maturity pathway that can help institutions understand their current level of AI adoption and plan future development. In addition, it discusses some of the challenges and risks associated with AI implementation and highlights implications for university leaders, policymakers, and researchers.
This study contributes to the ongoing discussion on AI in higher education by introducing the idea of the AI-Native University as a distinct institutional model. It also eхtends eхisting thinking on digital transformation in the conteхt of generative AI and emphasizes the growing importance of human–AI collaboration at the institutional level. Finally, the proposed maturity approach offers a starting point for evaluating AI readiness and guiding future transformation efforts. The framework can serve as a foundation for future empirical studies, institutional benchmarking, and policy development.