Faculty Beliefs, Pedagogical Knowledge, and Adoption Intentions of Generative AI in Mathematics Education: Evidence from Saudi Higher Education

Abstract

Generative artificial intelligence (AI) is increasingly reshaping higher education. In mathematics education, where abstraction, rigor, and proof are central, the pedagogical implications of generative AI are particularly consequential. Tools such as ChatGPT offer opportunities for personalized feedback and instructional scaffolding, yet they also raise concerns related to accuracy, academic integrity, and overreliance. In this context, faculty beliefs and instructional reasoning play a decisive role in determining whether AI is adopted or resisted. This study examined mathematics faculty perspectives on generative AI adoption across three public universities in Saudi Arabia’s Eastern Province. It focused on teaching beliefs, pedagogical and technological knowledge, digital experience, and adoption intentions, drawing on Teachers’ Beliefs Typology, the Technological Pedagogical Content Knowledge framework, and Diffusion of Innovation theory as an integrated analytical lens. A descriptive–correlational design enriched with qualitative insights was employed, with data collected from 144 faculty members and analyzed using statistical and thematic techniques. The findings indicate a pattern of cautious optimism. Connectionist teaching beliefs and pedagogical technological knowledge strongly predicted adoption intentions, while transmission-oriented beliefs were negatively associated with adoption. Digital experience showed a smaller positive association. Qualitative findings highlighted perceived benefits such as efficiency and personalized support, alongside concerns regarding reliability and academic integrity. Differences by academic rank were observed, with lecturers and assistant professors reporting higher adoption intentions than full professors. Overall, the findings demonstrate how disciplinary beliefs and professional knowledge jointly shape faculty adoption intentions in mathematics education, offering actionable insights for institutional policy and professional development initiatives.