AI Usage Typologies Among Korean Adults: SOM Approach
Moon-Koo Kim, Jong‐Hyun Park, Junghwan Lee · Journal of Computer Information Systems · 2025
Although AI is rapidly spreading, research classifying user types and analyzing their characteristics remains limited. This study uses the Self-Organizing Map (SOM) algorithm to classify AI usage patterns among Korean adults and analyze characteristics, factors, and outcomes. Analyzing 4,324 adults aged 20–59, three types were identified: Diverse (13.5%), Selective (42.7%), and Limited AI Users (43.8%). Diverse users were primarily young, educated males in professional occupations with superior digital competence and self-efficacy, yet showing highest AI risk concerns. Selective users utilized AI for daily convenience, while Limited users showed minimal engagement. Both Diverse and Selective users found AI services beneficial, but only Diverse users showed significantly higher life satisfaction. Males demonstrated higher usage and competence. These findings reveal new digital divides in the AI era, confirming digital self-efficacy and competence as key factors. This study suggests customized policies, competence support, and solutions for an inclusive AI society.