Investigation of pre-service mathematics teachers' instructional competencies in chatgpt-supported lesson planning process

Abstract

In today's world, where digitalization in education is gaining momentum, it is important to investigate the impact of AI-supported lesson planning on teacher competencies. The purpose of this study is to analyze the effect of ChatGPT-supported lesson planning on the instructional competency scores of pre-service mathematics teachers. A one-group pre-test-post-test experimental design was employed. The study group consisted of 17 thirdyear pre-service secondary school mathematics teachers from a state university in western Türkiye, selected via criterion sampling. Data were collected using three instruments: the Mathematics Teaching Efficacy Scale (MTES), the Mathematics Teaching Anxiety Scale (MTAS), and the Self-Efficacy Beliefs Regarding the Teaching Process Scale (SB-TPS). The data were analyzed using SPSS 20.0, following normality tests. Results indicated a decrease in the total scores for mathematics teaching competencies and mathematics teaching anxiety, while an increasing trend was observed in self-efficacy beliefs regarding the teaching process; however, these changes were not statistically significant. This study represents an initial step, highlighting the need for further research to advance a comprehensive understanding of AI integration in teacher education.