Abstract
Artificial intelligence (AI) adoption among preservice mathematics teachers remains uneven. This study aimed to identify adoption predictors, test trust mediation, examine risk moderation, compare demographic subgroups, and evaluate a sequential awareness-ease-usefulness-trust-intention model. Survey data from 130 South African preservice mathematics teachers were analyzed using regression, mediation, moderation, subgroup analysis, and structural equation modelling. Trust (β = .38), usefulness (β = .26), and awareness (β = .19) positively predicted intention; risk predicted it negatively (β = -.16), while ease had no direct effect. Trust partially mediated all cognitive pathways (44.2%-58.5%), and risk weakened the usefulness-, trust-, and awareness-intention relationships. Usefulness was strongest for men, trust for women, and explained variance was greater in years 3-4 than years 1-2 (64.8% vs. 52.4%). The sequential model had acceptable fit and a significant full indirect effect (0.031, 95% confidence interval [0.009, 0.061]).
License
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Article Type: Research Article
EURASIA J Math Sci Tech Ed, Volume 22, Issue 9, September 2026, Article No: em2912
https://doi.org/10.29333/ejmste/19241
Publication date: 09 Sep 2026
Article Views: 13
Article Downloads: 6
Open Access References How to cite this article
Full Text (PDF)