Measuring the skills of university students in the Education career using Artificial Intelligence: right brain vs left brain with structural equation models

This research uses an explanatory quantitative design with a structural equation modelling (SEM) approach to test whether left or right brain dominance affects the skills of preservice teachers in using artificial intelligence (AI). Participants in this research were 342 students majoring in multidisciplinary education (preservice teachers). The distribution of participant data reflects variations in these aspects, which aims to provide a comprehensive picture of the factors that have the potential to influence students' skills in using AI. This research highlights that students with left-brain dominance show superior abilities in using AI technology compared to students with right-brain dominance. This is shown by the factor loading value on the d60 path which reached 0.98, indicating the high representational power of students with left brain dominance in mastering AI. These findings have a significant impact on approaches to technology education in universities. First, these results can encourage the development of curricula that place more emphasis on analytical and logical skills for all students, as well as introducing creative elements that can attract the interest of students with right-brain dominance. Thus, educational programs can be designed to accommodate both types of brain dominance, ensuring that students receive comprehensive and balanced training. Additionally, these findings highlight the need for universities to create inclusive and supportive learning environments, especially for women who may face gender stereotypes that hinder their participation in STEM fields.