COMPONENTS, INDICATORS, AND MANAGEMENT GUIDELINES FOR DEVELOPING TEACHERS’ ARTIFICIAL INTELLIGENCE COMPETENCIES

Abstract

          This study aimed to (1) analyze the components and indicators of teachers’ artificial intelligence (AI) competency and (2) identify administrative guidelines for developing teachers’ AI competency. A mixed-methods research design was employed, integrating both quantitative and qualitative approaches. Quantitative data were collected through questionnaires focusing on competency components and administrative development strategies, while qualitative data were gathered through semi-structured interviews. Quantitative data were analyzed using descriptive statistics, mean, and standard deviation, along with instrument quality assessment through the Index of Item-Objective Congruence (IOC), item discrimination, and reliability testing. Hypothesis testing was conducted using Confirmatory Factor Analysis (CFA) at both first-order and second-order levels. Model fit was evaluated using standard indices, including GFI, AGFI (> 0.90), CFI (> 0.95), /df (< 2), SRMR (< 0.08), and RMSEA (< 0.06). Qualitative data were analyzed through content analysis based on Colaizzi’s approach, involving in-depth reading, extraction of significant statements, interpretation of meanings, thematic categorization, and synthesis into descriptive explanations, followed by member checking for validation.

          The findings revealed that teachers’ AI competency, based on the TAICS Scale framework, consisted of six components: AI knowledge, AI-integrated instruction,
AI-based assessment, AI ethics, human-centered education, and professional engagement, encompassing a total of 24 indicators. The proposed model demonstrated a strong fit with empirical data ( = 186.63, df = 159, p = 0.066, RMSEA = 0.021). Furthermore, the administrative guidelines for enhancing teachers’ AI competency comprised six components, emphasizing the integration of AI technology into learning management, the implementation of appropriate assessment practices, and the continuous professional development of teachers. These findings provide a practical framework for educational institutions to strengthen teachers’ readiness for AI-driven educational transformation.