AI integration model in teaching and learning of mathematics: its influence on teachers' perceived technology usefulness and intentions to use technology in mathematics instructional delivery

Purpose This study aims to develop a comprehensive AI Integration Model, specifically tailored for mathematics education, and to examine its influence on teachers' perceived usefulness of technology and their intention to integrate technology into mathematics instruction. Design/methodology/approach The study employs a quantitative, correlational research design underpinned by structural equation modeling (SEM) to explore the predictive relationship between the replace, interactive, amplify, creativity and transformation (RIACT) model, and teachers' intentions to integrate technology into mathematics instruction. The study involved 320 mathematics students drawn through stratified random sampling from a population of educators in a public university, ensuring representation across undergraduate, MPhil and Ph.D. Mathematics Education programs. Data were collected using both online (Google Forms) and structured paper questionnaires. The collected data were analyzed using SEM in Amos (ver. 23) to test the hypothesized paths. Findings The analysis demonstrated that the RIACT model positively predicts both perceived usefulness and intention to use technology among mathematics teachers. Originality/value Several studies have been conducted on the effect of technology integration model on students' learning outcomes, but there remains a notable gap in empirical research that directly examines how structured frameworks, such as the RIACT Model, influence teachers' perceived usefulness and their intentions to employ technology in mathematics instruction.