From Sensemaking to Sensebecoming: How Institutional Contexts Shape Generative AI Use in Higher Education

Abstract

The rapid diffusion of generative artificial intelligence (AI) is transforming higher education, yet little is known about how students and staff make sense of these technologies and incorporate them into learning practices. Drawing on sensemaking and sensegiving theory, this study examines how actors construct meaning around generative AI and how these interpretations evolve into emerging practices. Using a comparative qualitative design across traditional universities and private tertiary institutions, we explore how institutional contexts shape the cues that actors draw upon to interpret AI. Our findings reveal distinct sensemaking trajectories. In traditional universities, generative AI is framed through epistemic concerns, academic integrity, and disciplinary norms, producing cautious experimentation and contested interpretations. In contrast, actors in private tertiary institutions interpret AI through employability, efficiency, and skill development, leading to more instrumental and pragmatic adoption. These differences shape divergent sensegiving dynamics, with traditional universities emphasizing boundary-setting while private institutions actively promote AI-enabled learning. We theorize these processes as sensebecoming, whereby actors move from interpreting generative AI to developing evolving norms and practices for its use. This study extends sensemaking research by showing how institutional contexts shape the transition from interpretation to practice during technological disruption.