Artificial Intelligence Integration and Teaching Effectiveness Based on RPMS-PPST Indicators: Basis for a Deped-Aligned AI Instructional Framework

Abstract

This study examined the relationship between Artificial Intelligence (AI) integration and teaching effectiveness as measured by the Philippine Professional Standards for Teachers (PPST) among public secondary school teachers. Guided by the Technology Acceptance Model (TAM) and DepEd Order No. 003, s. 2026 on the foundational guidelines for AI in basic education, a quantitative correlational design was employed involving 42 purposively selected teachers from Santa Fe National High School, Nueva Vizcaya, Philippines. Data were collected using a validated survey instrument aligned with Results-Based Performance Management System (RPMS- PPST) indicators and Department of Education (DepEd)- guided AI integration practices. The instrument demonstrated high internal consistency, with a Cronbach’s alpha coefficient of 0.87. Descriptive analysis indicated high levels of AI integration (M= 3.77) and teaching effectiveness (M= 3.64). Pearson product–moment correlation analysis revealed a strong positive relationship between AI integration and teaching effectiveness (r= 0.89, p< .05), suggesting that AI-supported instructional practices are associated with enhanced teaching performance across PPST domains, including pedagogy, classroom management, assessment, and professional development. The findings highlight AI as a pedagogical support system that strengthens competency-based teaching performance rather than functioning as an independent determinant. The study contributes empirical evidence supporting policy-aligned digital transformation in Philippine basic education and informs the development of an AI-enhanced instructional framework anchored on PPST standards and DepEd policy direction.