AI-Tpack and the Pre-Service Teacher: Mapping the Structural Disconnect Between Personal AI Use and Instructional Design

Abstract

The rapid infusion of Artificial Intelligence (AI) into education requires empirical evidence on the readiness of pre-service teachers across disciplines. This descriptive-correlational study examined the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) readiness of 264 pre-service teaching interns enrolled in Bachelor of Elementary Education (BEEd) and Bachelor of Secondary Education (BSE) programs, majoring in English, Mathematics, and Science, at Taguig City University. Using an instrument adapted from Ning et al. (2024), the study assessed participants across seven domains: Content Knowledge (CK), Pedagogical Knowledge (PK), AI-Technological Knowledge (AI-TK), Pedagogical Content Knowledge (PCK), AI-Technological Content Knowledge (AI-TCK), AI-Technological Pedagogical Knowledge (AI-TPK), and integrated AI-TPACK. It further examined whether the purpose of respondents’ personal AI use (educational, social media, or media/video) predicted their instructional design readiness, and whether readiness varied across program cohorts. Descriptive results showed high self-reported competency in CK (94.3%) and PK (93.2%), and somewhat lower but still high competency in the AI-integrated domains (82.5%–87.9%). However, multiple linear regression analyses revealed that personal AI use patterns did not significantly predict any AI-TPACK indicator (p > 0.05, R² ≤ 0.016), and one-way ANOVA revealed no significant differences across program cohorts. These findings point to a structural disconnect between personal familiarity with AI and professional instructional design readiness, underscoring the need for teacher education institutions to move beyond passive exposure to technology toward explicit, discipline-embedded AI-TPACK instruction.