Development of a Teacher's AI Literacy Scale Based on Meta-Analysis

With the recent advancements in generative artificial intelligence (AI) technology, the need for AI utilization in school education has increased, leading to active research on teacher AI literacy (competency). This study aims to develop self-assessment items for teacher AI literacy, recognizing the importance of developing and evaluating teachers' AI literacy in the context of school education where AI-integrated education is expected to become widespread. Using a research methodology that involved a meta-analysis of domestic and international literature on teacher digital literacy and digital competency frameworks, common core constructs were extracted. These constructs were integrated into Eisner’s (2002) conceptual framework of teachers' three types of professional knowledge, forming a novel AI literacy assessment framework. The resulting self-assessment items comprise 36 questions across nine domains: understanding the basic principles of AI, foundational knowledge of data science, educational applications of AI, practical use of AI tools, foundational programming skills, problem-solving capabilities, understanding AI ethics, analyzing social impacts, and responsible AI usage. To verify the content and structural validity of the items, a participatory design approach was employed, applying the developed AI literacy assessment items to in-service teachers. Feedback was collected through focus group interviews (FGI) with research participants, and the results were used to refine the final self-assessment items for teacher AI literacy. The self-assessment scale developed in this study serves as a practical tool for teachers to evaluate and enhance their educational competencies in alignment with the technological environment of the AI era.