Teachers’ Readiness to Integrate Artificial Intelligence in Mathematics Instruction: Evidence Based on the Will–Skill–Tool Framework
Isaac Bengre Taley, Emmanuel Taley
Source abstract
The integration of artificial intelligence (AI) into mathematics instruction requires teachers to demonstrate readiness that extends beyond access to digital tools. Guided by the Will–Skill–Tool (WST) framework, this study examined how teachers’ AI-related dispositions (Will), competencies (Skill), and access conditions (Tool) predict AI integration readiness and personal mathematics teaching efficacy (PMTE). A cross-sectional survey design was employed, with 186 pre-service and in-service mathematics teachers in Ghana. Data were analysed using descriptive statistics, hierarchical multiple regression, and mediation analyses. The results indicated that demographic factors, including sex and teacher status, had a minimal influence on AI integration readiness or PMTE. Teachers’ engagement with AI technologies significantly predicted higher skill, tool, readiness, and teaching efficacy. Will, Skill, and Tool jointly contributed to AI integration readiness and PMTE, with Skill and Tool emerging as the strongest predictors. Mediation analyses further showed that AI integration readiness partially mediated the effects of Skill and Tool on PMTE, but Will did not. The study extends the WST framework by empirically demonstrating the mediating role of AI integration readiness in linking teachers’ competencies and access conditions to instructional efficacy. These findings offer insights for designing professional development initiatives that promote effective AI integration in mathematics education.
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