Development and validation of teacher artificial intelligence (AI) competence self-efficacy (TAICS) scale

Abstract

Evaluating teacher AI competence levels and building effective, safe, and healthy learning environment are crucial steps in transitioning to AI-based education. Current established digital competence frameworks may indirectly address AI competence but often overlook the impact of AI on society, ethics, and assessment. Research on teacher AI competence is at its first stage, primarily focusing on theoretical and professional discussions, along with qualitative investigations. This study aims to propose and confirm the reliability and validity of a scale measuring teacher AI competence self-efficacy (TAICS) in K-12 education. The scale was developed using a Delphi method, and includes six dimensions: AI knowledge, AI pedagogy, AI assessments, AI ethics, human-centered education, and professional engagement. Each dimension contains four items. The scale was evaluated on a sample of 434 K-12 teachers through confirmatory factor analysis and model comparisons. The analyses showed that the scale is consistent across male and female teachers, as well as scientific and non-science teachers. The completed TAICS scale consists of 24 items and encompasses six dimensions of AI competence. It can be used to examine interventions and correlational research, as well as to inform the creation of new strategies and policies for AI in relation to teacher AI competence development.