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Paper short abstract

This paper examines how AI mediated organizational learning shapes university governance in the MENA region and how governance structures can prevent pathological autopoiesis by supporting socially responsive rather than self-referential decision making.

Paper long abstract

Universities in the MENA region are increasingly adopting AI driven tools to support

governance and institutional decision making. While these technologies can enhance

organizational learning and efficiency, they also risk reinforcing pathological autopoiesis,

understood as self-referential governance loops in which institutions prioritize internal

performance, metrics, and prestige over societal needs. This paper examines how AI mediated

organizational learning is represented within university governance frameworks and whether

these processes enable adaptive, inclusive, and socially responsive decision making. Using a

qualitative document-based approach, the study analyses publicly available strategic plans,

governance policies, digital transformation strategies, and institutional reports from selected

MENA universities. The analysis focuses on how AI is framed in governance discourse, how

organizational learning is structured, and whether governance mechanisms connect institutional

decision making to broader social challenges. The findings suggest that while AI is frequently

presented as a tool for improving institutional performance and strategic alignment, its

integration into governance often reproduces existing hierarchies and internalized routines.

Explicit mechanisms that link organizational learning to societal accountability and inclusion

remain limited, although emerging governance practices such as transparency frameworks and

references to social impact indicate potential pathways for countering self-reinforcing decision

loops. By situating university governance within broader development debates, this paper

contributes to discussions on power, agency, and digital futures in non-Western and resource

constrained contexts and highlights how AI supported governance can be reoriented to prevent

pathological autopoiesis and support more inclusive and socially grounded institutional futures.