Abstract
The integration of artificial intelligence (AI) and advanced networking technologies into higher education curricula requires a systemic understanding of how institutional conditions and instructional structures translate into measurable competency outcomes. This study develops and tests a structural equation model examining the determinants of AI and fiber-optic technology integration within a technology education curriculum at a Philippine state university. Drawing from Institutional Theory, the Resource-Based View and the Technological Pedagogical Content Knowledge (TPACK) framework, the proposed model links institutional readiness, instructional curriculum readiness, AI instructional integration, instructor competency in fiber-optic networks and students’ competent skills. Data were collected from 317 undergraduate students enrolled in computing-related programs and analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) in AMOS. The measurement model demonstrated strong convergent validity (AVE > 0.50) and high composite reliability (CR > 0.90), with acceptable global fit indices (χ²/df = 2.912; RMSEA = 0.078). Structural analysis revealed that AI instructional integration significantly predicts instructor competency (β = 0.804, p < 0.001), which in turn strongly influences students’ competent skills (β = 1.000, p < 0.001). Direct paths from institutional and instructional readiness to student skills were not supported; instead, their effects operate indirectly through a sequential pathway: Readiness → AI Integration → Instructor Competency → Student Skills. The findings highlight instructor competency as the critical mediating mechanism through which institutional and technological investments translate into technical skill development. This study contributes a validated readiness-driven framework for AI-supported technology education and offers practical implications for institutional curriculum development.
Keywords: Artificial Intelligence in Education, Fiber-optic Network Education, Institutional Readiness, Instructional Curriculum Readiness, Instructor Competency, Structural Equation Modeling.