MODELING THE DIGITAL MATURITY OF EDUCATORS IN THE AI ERA: A COMPUTATIONAL ANALYSIS OF TECHNOLOGY ADOPTION PATTERNS AND THEIR PERCEIVED IMPACT

Abstract

omputational study on digital maturity comprising the teaching community and their perception towards the impact of Digital Educational Technologies (DET) and Artificial Intelligence (AI) in the learning process. The objective of this study is to determine digital maturity profiles among teachers, pattern recognition in the use of AI tools, and modeling their correlation with subjective assessment
towards educational outcomes. The research that took place among 832 teachers from various educational organizations combines a quantitative-qualitative approach (online survey) with further advanced computational methods. PCA was applied for determination of latent factors within the usage of AI tools while K-Means clustering enabled us to detect three empirical profiles concerning digital maturity. Linear regression analysis between tool usage and supposed better student outcome has been executed. Findings reveal high levels of educator engagement, with traditional tools (MS Office) still topping usage and ChatGPT being the most used AI tool (80.4%).

Three clusters were identified through clustering: "AI-oriented," "Basic AI," and "Interactive-traditional" that reflect digital maturity’s different aspects. A very large proportion of teachers-84.6%-reported increased student motivation and interest thanks to DLT. The maturity profiles identified and the relationships modeled provide an empirical basis for targeted, cluster-specific professional development programs to be designed.