Generative AI (GenAI) is reshaping mathematics education through capabilities such as automated feedback, personalised tutoring, and instructional scaffolding (Memarian & Doleck, 2023; Yoon et al., 2024). Freely accessible tools like ChatGPT present new opportunities to enhance student learning and teacher development by fostering reflective thinking, problem-posing, and pedagogical content knowledge (Biton & Segal, 2025; Gurl et al., 2025). This potential is particularly relevant in developing countries, where limited access to traditional resources may be offset by the accessibility of GenAI tools.
This study investigates how demographic characteristics—including gender, age, teaching experience, school type, and subject area—shape in-service mathematics teachers’ adoption, usage, and preferences for GenAI tools. It also explores teachers’ reasons for use and non-use and preferred applications. Survey data were collected from 264 in-service mathematics teachers across diverse school contexts in Indonesia. Quantitative analysis using chi-square tests examined associations between demographic variables and GenAI engagement.
Findings show a near-even split in GenAI adoption, with 49.6% of teachers reporting use. Users were typically aged 31–40 with 11–20 years of experience. Non-users cited limited tech skills (39.4%), lack of GenAI knowledge (25%), and poor infrastructure (18.9%) as key barriers. GenAI was mainly used for creating learning materials (27.2%) and supporting problem-solving and assessment (21.1%). No significant links were found between demographics and reasons for use or non-use, indicating that engagement is shaped more by personal and contextual factors. Canva AI and ChatGPT were the most widely recognised and used tools across both groups.
This study also highlights a critical disconnect: despite GenAI’s free availability, its meaningful integration is limited by knowledge gaps, trust issues, and a lack of evaluative skills—particularly in under-resourced contexts. The findings emphasise the need for equitable AI capacity-building for teachers in developing countries and provide a data-driven basis for inclusive professional development that supports teacher agency and realises GenAI’s transformative potential in mathematics education.