Abstract
This study aimed to explore the perceptions and implementation challenges of science and mathematics teachers regarding the integration of artificial intelligence (AI) in education. A descriptive cross-sectional survey design was employed. Data were collected through an online questionnaire administered to 218 Jordanian science and mathematics teachers working in public schools during the 2023/2024 academic year. Descriptive statistics and one-way analysis of variance were used to analyze the data. Results revealed that science and mathematics teachers reported both highly positive perceptions of AI and substantial implementation challenges. A statistically significant difference was observed in teachers’ perceptions based on their educational background, favoring those with higher qualifications. Teachers’ perceptions also differed significantly according to their reported job satisfaction. However, no significant differences were found in various factors such as specialization, gender, experience, information and communication technology proficiency, and school location. Similarly, there were no significant differences in the challenges faced by the teachers when using AI across the study’s independent variables. The findings indicate that positive teacher perceptions coexist with substantial implementation challenges, suggesting that AI adoption in schools is constrained more by systemic conditions than by teachers’ individual characteristics. The findings suggest that educational policies should prioritize institutional readiness through improved infrastructure, teacher professional development, and curriculum support to facilitate effective AI integration in schools.
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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 11, November 2026, Article No: em2958
https://doi.org/10.29333/ejmste/19682
Publication date: 27 Oct 2026
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