Instrumentalization and Reconceptualization: Dual Pathways of Generative AI in Higher Education

Abstract

The rapid integration of generative artificial intelligence (AI) in higher education constitutes a major transformation affecting pedagogy, institutional structures, and academic work. Although existing studies emphasize its potential benefits, a coherent analysis of strategic adoption pathways remains limited. This article critically synthesizes recent scholarship to examine two dominant trajectories of generative AI integration: instrumentalization and reconceptualization. Using a systematic critical synthesis of 40 scholarly publications, the analysis explores the tension between employing AI to optimize existing educational processes and leveraging it to fundamentally reimagine educational purposes, institutional models, and academic identities. The findings identify competing narratives: one emphasizing efficiency, personalization, and automation, and another highlighting epistemological shifts, platform-based university models, and challenges to democratic and ethical principles. These trajectories generate tensions in areas such as academic integrity, digital literacy, and institutional strategy. The study concludes that sustainable AI integration requires balancing pragmatic implementation with critical, mission-oriented reflection on the future role of higher education in an AI-mediated context.