Pioneering Smart Leadership: Integrating AI Analytics to Transform STEM Education Management for the Future

Abstract

The mixed-methods study explored how integrating artificial intelligence (AI), innovative leadership practices, leadership competencies, and ethical governance influences management outcomes in STEM education within higher education institutions in Pakistan. A quantitative survey was conducted with 380 respondents to assess institutional readiness and the effectiveness of AI-enabled management practices. Descriptive analyses revealed generally positive perceptions across all constructs, with mean scores ranging from 3.72 to 3.88 on a five-point scale. All measurement scales demonstrated high internal consistency (α = .88–.93), and confirmatory factor analysis indicated an excellent model fit (CFI = .95, TLI = .94, RMSEA = .054), confirming strong construct validity. Correlation results showed significant, moderate-to-strong positive associations among all variables (r = .45–.66), indicating that practical AI usage and leadership practices are aligned with improved STEM management outcomes. Independent t-tests revealed no significant gender differences, whereas ANOVA results showed significant differences by job position. This result suggests that administrators and department heads perceive AI-enabled outcomes more positively than faculty members. Multiple regression analysis demonstrated that AI integration, visionary leadership, leadership competence, and ethical governance significantly predicted STEM management outcomes, explaining 35% of the variance. Structural equation modeling further supported the hypothesized relationships, with visionary leadership (β = .32) and AI integration (β = .30) identified as the strongest contributors. The findings emphasize the crucial role of AI-enabled and ethically grounded leadership in enhancing STEM education management. The study highlights the importance of building institutional capacity, promoting data-literate leadership, and establishing robust governance frameworks to optimize the impact of AI-driven decision-making in STEM environments.