Abstract:

Policy frameworks, ethical guidelines, and governance models have been developed on how to integrate artificial intelligence (AI) into higher education quality assurance (QA). However, there has been insufficient interest in how AI is enacted in QA practice and in how those responsible for quality work interpret its use. This gap has led to uncertainty regarding how AI reshapes professional judgement, accountability, and decision-making authority. This paper proposes a qualitative study exploring how academic and administrative staff make sense of AI within routine quality assurance processes. Using semi-structured interviews and an organizational sensemaking perspective, the study examines how staff interpret AI-generated outputs and respond to automation in contexts of responsibility and risk. Rather than testing predefined assumptions, the research seeks to generate practice-based insights grounded in participants’ experiences. The study extends sensemaking theory to AI-enabled QA and aligns thematic analysis with the organizational perspective on meaning construction.

Please choose your submission type: Developmental Paper

Primary Track: Organisational Studies

Keywords: Organisational research and practice: interdisciplinary approaches to understanding and managing the relationships that exist within and across sectors

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Ms. Lina Bahafi