Computer Science AI-Driven Performance Analytics for Educational Staff: An Empirical Study in Iraqi Schools Evaluating the Role of Artificial Intelligence in Enhancing Teaching Efficiency and Decision-Making Quality
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Abstract
This quantitative study investigates the impact of AI-based decision support systems on teacher performance and administrative efficiency in Iraqi secondary schools. A structured questionnaire was administered to 350 teachers and administrators across four governorates (Sulaimaniyah, Nineveh, Baghdad, and Al-Nasr District). Data were analyzed using SPSS v25 through reliability tests (Cronbach's alpha), validity tests (Pearson correlation), descriptive statistics, and regression analysis. The results revealed a strong positive correlation between AI-based systems and teacher performance (R = 0.939, R² = 0.875, p < 0.001), indicating that 87.5% of the variance in teacher performance is explained by AI system use. Additionally, AI tools significantly improved administrative planning efficiency (R² = 0.694) and decision quality (R² = 0.570). Staff acceptance of AI technologies also showed a substantial impact on institutional performance (R² = 0.642). The study recommends promoting AI integration in schools, enhancing technological infrastructure, and providing training programs for educational staff. These findings offer evidence-based insights for policymakers seeking to modernize educational management through smart analytics.
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