Generative AI Usage and Digital Competence as Predictors of Mathematics Teaching Effectiveness among Pre-Service Teachers

Abstract

The growing use of Generative Artificial Intelligence (GenAI) in education presents new opportunities for improving mathematics teaching, but its effectiveness may depend on teachers’ ability to use digital technologies critically and appropriately. This study examined Generative AI Usage and Digital Competence as predictors of Mathematics Teaching Effectiveness among pre-service teachers who had completed teaching practice. A quantitative cross-sectional survey design was employed, involving 217 Level 400 pre-service teachers from Colleges of Education in the Hohoe Municipality. Data were collected using a five-point Likert-scale structured questionnaire and analysed using Structural Equation Modelling (SEM) in SPSS AMOS Version 23. The measurement model demonstrated good fit, while all constructs showed acceptable reliability and validity. The findings revealed that Generative AI Usage significantly and positively predicted Mathematics Teaching Effectiveness (β = 0.181, p = .010). Digital Competence also significantly and positively predicted Mathematics Teaching Effectiveness (β = 0.461, p < .001), with a stronger predictive effect than GenAI Usage. The study concludes that while GenAI can support effective mathematics teaching, digital competence is particularly important for enabling pre-service teachers to evaluate, adapt, and integrate digital resources meaningfully into instruction. Teacher education institutions should therefore promote digital competence development alongside responsible and pedagogically informed use of GenAI in mathematics teacher preparation.