Supporting Teachers’ Professional Development With Generative AI: The Effects on Higher Order Thinking and Self-Efficacy
Jijian Lu, Ruxin Zheng, Zikun Gong, Huifen Xu
Abstract
Generative AI has emerged as a noteworthy milestone and a consequential advancement in the annals of major disciplines within the domains of human science and technology. This study aims to explore the effects of generative AI-assisted pre-service teaching skills training on pre-service teachers’ self-efficacy and higher order thinking. The participants of this study were 215 pre-service mathematics, science, and computer teachers from a university in China. Firstly, a pretest-posttest quasi-experimental design was implemented for an experimental group (teaching skills training by generative AI) and a control group (teaching skills training by traditional methods) by investigating the teacher self-efficacy and higher order thinking of the two groups before and after the experiment. Secondly, a semi-structured interview comprising open-ended questions was administered to 25 pre-service teachers within the experimental group to present their views on generative AI-assisted teaching. The results showed that the scores of pre-service teachers in the experimental group, who used generative AI for teachers’ professional development, were considerably higher than those of the control group, both in teacher self-efficacy (F = 8.589, p = 0.0084< 0.05) and higher order thinking (F = 7.217, p = 0.008 < 0.05). It revealed that generative AI can be effective in supporting teachers’ professional development. This study produced a practical teachers’ professional development method for pre-service teachers with generative AI.