Teachers' artificial intelligence self-efficacy: development and validation of a multidimensional scale grounded in social cognitive theory

A study published in Frontiers in Education outlines the development and initial validation of the Artificial Intelligence Self-Efficacy Scale for Teachers (AISES-T). Grounded in social cognitive theory, the research addresses a critical gap in assessing educator confidence in using artificial intelligence tools, utilizing empirical data collected from teachers across regions in Turkiye.

Measuring educator self-efficacy is essential for identifying human capital readiness as artificial intelligence systems integrate into educational environments. Without standardized assessment tools, institutions cannot reliably determine whether faculty possess the confidence required to translate technological access into effective practice. Establishing validated metrics ensures that transformation investments directly address behavioral and psychological barriers to adoption.

Existing research has predominantly examined artificial intelligence literacy, readiness, and competency, leaving validated measures of teacher self-efficacy underrepresented.

Forward consideration, not a verified fact.