Heterogeneous self-efficacy effects in mathematics pre-service teachers’ AI adoption: a Bayesian moderated mediation analysis

Abstract

Introduction – This study examines heterogeneity in the mediating role of self-efficacy between prior AI training and adoption intentions among pre-service mathematics teachers. Methods – Using data from 79 pre-service teachers at the University of the Free State, South Africa, Bayesian moderated mediation analysis was employed to assess whether this pathway operates uniformly across demographic subgroups. Results – Findings revealed pronounced heterogeneity: the indirect effect was strong for female participants (indirect effect = 0.311, P(>0) = 94.8%) but negligible for males (indirect effect = −0.064, P(>0) = 37.8%). Additionally, self-efficacy predicted intentions more strongly among untrained (β = 0.746) than trained teachers (β = 0.195). Discussion – These results suggest that training may homogenise intention formation and that self-efficacy operates differently across subgroups. The findings challenge uniform models of technology adoption and highlight the need for differentiated, context-sensitive teacher education strategies