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