Education Management in the Age of Artificial Intelligence:

Transforming Decision-Making and Institutional Performance

Ismailova Nilufar1,*

1 Department of World Economy and International Economic Relations, Tashkent State University of Economics, Tashkent,

Uzbekistan

Received: May 30, 2026 Revised: June 28, 2026 Accepted: August 14, 2026 ⋆ Corresponding author

ABSTRACT

Artificial intelligence (AI) is increasingly embedded in the administrative core of higher education institutions

(HEIs), reshaping how academic leaders plan, allocate resources, and evaluate performance. Yet empirical evidence

on whether AI-enabled decision-making actually improves institutional outcomes remains fragmented. This study

examines the relationship between AI adoption, data-driven decision-making, perceived usefulness, trust and

transparency, AI governance maturity, and institutional performance among administrators in Central Asian, South

Asian, Southeast Asian, and MENA higher-education institutions. Using a cross-sectional quantitative design

(N = 236), descriptive statistics, Pearson correlation, multiple linear regression, and one-way ANOVA were applied

to test the hypothesized relationships. Data-driven decision-making emerged as the strongest predictor of institutional

performance (β = 0.52, p < .001), followed by perceived usefulness (β = 0.29, p < .001) and AI governance

maturity (β = 0.21, p < .001); trust and transparency contributed a smaller but significant effect (β = 0.13,

p = .005). Notably, raw AI adoption lost statistical significance once these mediating constructs were controlled for

(β =−0.05, p=.481), suggesting that the mere presence of AI tools does not translate into institutional performance

gains unless it is channelled through disciplined, data-driven decision routines and sound governance. The full model

explained 59.7% of the variance in institutional performance (R2 = .597, adjusted R2 = .588). These findings extend

socio-technical models of educational technology adoption and offer HEI leaders an evidence base for prioritising

governance and decision-culture investments over tool acquisition alone.

Keywords: Artificial intelligence Education management Data-driven decision-making Institutional performance

Higher education governance Learning analytics

1. INTRODUCTION

Higher education institutions (HEIs) operate today under

conditions that earlier generations of administrators rarely

faced simultaneously: shrinking public subsidies, volatile

enrolment, demands for measurable accountability, and a

technological environment that changes faster than institutional

policy can be written. Artificial intelligence (AI) has

moved from a peripheral curiosity in university IT departments

to a core administrative capability, used to forecast

enrolment, flag at-risk students, automate advising, and inform

strategic planning at the level of the president’s office

and the board of trustees [1]. Recent industry survey data

illustrate the pace of this shift: institution-wide AI adoption

among higher-education institutions rose from 49% in 2024

to 66% in 2025, an increase of seventeen percentage points in

a single year, while individual administrator use of AI tools

reached 91% [2].

This rapid uptake has outpaced the theoretical and empirical

understanding of how AI translates into better institutional