AI literacy-related domains and AI-TPACK readiness among preservice mathematics teachers: A factor-informed structural equation modelling study

Abstract

In this factor-informed exploratory CFA/SEM study, AI-literacy-related domains were treated as theoretically informed and empirically tested predictors of AI-TPACK readiness rather than as fully validated independent latent variables. Artificial intelligence (AI) is increasingly entering mathematics education, making it important to understand how preservice teachers become ready to integrate AI-supported tools pedagogically. This study examined AI-TPACK readiness among 130 preservice mathematics teachers at a South African public university. Exploratory factor analysis using polychoric correlations indicated that the AI-TPACK readiness items were essentially unidimensional; one weak design-confidence item was removed. The refined seven-item measurement model fitted better than the original eight-item specification, although discriminant-validity evidence for the broader AI-literacy-related domains was mixed. The primary gender-controlled latent SEM (sample n = 129) showed good approximate fit, χ 2 (602) = 789.92, p < .001, CFI = .981, TLI = .984, RMSEA = .049, SRMR = .082, and explained 53.0% of the variance in AI-TPACK readiness. Positive associations were observed for prior AI use, critical-ethical appraisal, and support/enablers. The support/enablers path had the largest standardised coefficient, but should be interpreted cautiously because the construct had marginal AVE and overlapped with information-source engagement. Year level was significant in the primary model but less stable in sensitivity analysis. Overall, the findings suggest that readiness was associated with direct AI experience and critical-ethical judgement, while the contribution of support/enablers remains provisional. The study contributes a cautious empirical account of AI-TPACK readiness in a Global South teacher education context.