An examination of the perception and competencies of mathematics teachers and teacher candidates on the use of artificial intelligence in mathematics education

Objective: The purpose of this study is to examine the perceptions of mathematics teachers and pre-service teachers regarding the use of artificial intelligence (AI) in mathematics education and their competence in this area. The rapid developments in educational technologies today require teachers to be able to use artificial intelligence effectively and pedagogically. In this context, the study comparatively evaluated both teachers' and pre-service teachers attitudes toward AI and their competencies in this area. Material and Methods: The research was conducted using a descriptive survey model, one of the quantitative research methods. The study group consisted of primary school mathematics teachers and pre-service teachers. Data were collected using two separate 5-point Likert-type scales designed to measure perception and competence levels. The data obtained were analysed using the SPSS programme. Findings: The findings of the study indicate that both in-service teachers and preservice teachers hold generally positive perceptions toward the use of artificial intelligence in mathematics education. Preservice teachers obtained higher perception scores compared to in-service teachers. The results of the confirmatory factor analysis demonstrated that the four-factor structure of the perception scale and the fifteen-factor structure of the competency scale were strongly validated within the sample. Regarding competency levels, preservice teachers appeared more prepared in certain areas related to technology integration, whereas in-service teachers showed greater strength in practice-based competencies stemming from classroom experience. Conclusion: In conclusion, both groups recognize the potential of artificial intelligence in mathematics education; however, their competency levels differ. Preservice teachers scored higher in theoretical and technology-oriented competencies, while in-service teachers were stronger in application-based areas. These results indicate that both groups have different developmental needs in AI-supported instructional processes. The findings highlight the necessity of enhancing AI literacy and technology-integration components within teacher education programs and in-service training.