Beyond awareness: A Bayesian cross-classified analysis of contextual factors and preservice teachers’ AI tool familiarity

Abstract

Preservice mathematics teachers may recognise the educational affordances of artificial intelligence (AI) while remaining unevenly familiar with specific AI tools. This exploratory cross-sectional study examined how awareness, mentor support, structural pressure, self-initiative, prior training, and informal AI use relate to self-reported familiarity across five mathematics-oriented AI tool categories. We fitted a Bayesian cross-classified cumulative ordinal model to 650 person-tool observations from 130 South African preservice mathematics teachers, treating familiarity as jointly structured by persons and tools. Awareness showed the strongest positive association with familiarity, with an odds ratio (OR) of 2.41 and a 95% credible interval (CrI) of 1.50 to 3.94. Structural pressure was also positively associated with familiarity (β = 0.67, 95% CrI [0.12, 1.16]), but this pattern is interpreted cautiously because reverse causality, omitted variables, and construct ambiguity remain plausible. Tool-level heterogeneity was substantial: problem-solving apps had consistently higher probabilities of reported familiarity (about 44–63% across scenarios) than symbolic engines and explanation bots (below 14%). The findings are therefore presented as hypothesis-generating evidence for tool-specific teacher education and for future longitudinal, experimental, and performance-based research, not as causal guidance for programme design.