Generative Artificial Intelligence as a Lifelong Learning Self Efficacy: Usage and Competence Scale

OpenAlex · English

The aim of this study is to develop a scale to measure the usage and competence levels of generative artificial intelligence as a lifelong learning self-efficacy among young and adult lifelong learners. Research data were collected from 248 individuals aged between 18 and 55. After a thorough review of the literature and theoretical frameworks such as the Technology Acceptance Model, Self-Efficacy Theory and Connectivism, an item pool for the scale was created. Similar scales in the related field were examined, and the item pool was developed accordingly. The items were reviewed by experts in educational technology, lifelong learning, and scale development. After making the necessary revisions, the trial form of the scale was presented to the participants. To determine the construct validity of the scale, exploratory factor analysis was conducted. The results of the exploratory factor analysis indicated that the scale consisted of two factors. The first factor comprises 10 items, while the second factor consists of 9 items. Confirmatory factor analysis was performed to reveal the relationships within the factors, the relationships between the variables and the factors, and the explanatory power of the factors on the model. The internal consistency coefficient, Cronbach’s alpha reliability value, was determined to be .833, and the Spearman-Brown coefficient was found to be .711, both of which indicate acceptable reliability. In conclusion, the Generative Artificial Intelligence Usage and Competence (GAIUC) Scale is expected to fill a gap in the literature by providing a validated tool to measure both the usage and competence of lifelong learners in using AI. This scale can serve as a foundation for future studies exploring AI-supported learning in various educational contexts.