Exploring Math Teachers’ Artificial Intelligence Self-Efficacy (TAISE)

Abstract

In the rapidly changing technological landscape of today, teachers' ability to use AI effectively is becoming more and more important for facilitating successful pedagogical practices and incorporating technology into the classroom. One important aspect affecting teachers' performance, motivation, and eventually their students' academic success is their Artificial Intelligence Self-Efficacy (TAISE). TAISE is still largely unexplored despite its importance. This study examines the AI self-efficacy of 280 mathematics teachers in Bahrain and how their self-efficacy in four areas- assistance, anthropomorphic interaction, comfort with AI, and technological skills- is influenced by their gender, age, and academic background. The TAISE scale's psychometric validation using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) revealed great structural integrity and outstanding reliability. The findings showed a generally high level of self-efficacy in all domains, with anthropomorphic interaction showing a slight gender preference for females. In every self-efficacy characteristic, teachers with 6-10 years of experience outperformed their more seasoned peers. Furthermore, teachers between the ages of 20 and 30 showed greater technological proficiency and comfort using AI tools. These observations highlight how crucial it is to provide specialized training for professionals that improves technological proficiency while taking experience and generational differences into consideration.