AI readiness scale for teachers: Development and validation

AI readiness scale for teachers: Development and validation

연구 분야: Software Development

학회: Education and Information Technologies

One of the most important indicators of artificial intelligence applications used to improve quality, effectiveness, and student success by optimizing instructional processes is readiness. Readiness is the cognitive, affective, and behavioral state of performing a behavior or using a technology. In this context, the development of a data collection tool for readiness in artificial intelligence applications seems necessary. In this study, a scale was developed to measure teachers' readiness for artificial intelligence applications. The sequential design model, one of the mixed research methods, was used in the research. The exploratory factor analysis (EFA) phase of this study was conducted with 616 samples while the confirmatory factor analysis (CFA) and concurrent validity phase were conducted with 345 and 128 samples, respectively. After ensuring validity and reliability in the research, the final version of the Readiness for Artificial Intelligence Applications Scale (RAIS) consisted of a total of 19 items and three dimensions. These dimensions are technology self-efficacy, interaction with students, and ethical awareness. In addition, concurrent validity was tested by examining the correlation between the General Attitudes Toward Artificial Intelligence Scale and the Artificial Intelligence Literacy Scale. The results of the analysis show that RAIS is a valid and reliable measurement tool for determining teachers' readiness for artificial intelligence applications.