Bio-Inspired Cyber Defense: Enhancing Intrusion Detection through the Waterwheel Plant Optimization Algorithm

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 (R 2 = .597,adjusted R 2 = .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.

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