Observers, Seekers, and Professionals in <scp>AI</scp> Adoption: An Investigation of <scp>AI</scp> Divide through Social Cognitive Perspective

ABSTRACT Extant research lacks comprehensive identification of distinct AI adopter groups necessary for targeted educational interventions to enhance AI literacy and mitigate the AI divide. Grounded in Social Cognitive Theory (SCT), this study categorizes AI adopters based on social cognitive characteristics, they are, fear of missing out (FoMO), AI attitudes, and self‐efficacy. Further, we profile these groups by AI literacy and educational backgrounds. A survey of 620 participants using K‐means clustering revealed three adopter types: (1) observers, characterized by lower education, AI literacy, FoMO, AI attitudes, and self‐efficacy; (2) seekers, with intermediate educational levels, high FoMO, and strong AI literacy; and (3) professionals, highly educated individuals with low FoMO but high AI literacy. Findings demonstrate how educational disparities shape AI literacy through social cognitive factors. Theoretically, this research introduces an innovative SCT‐based classification of AI adopters, offering practical insights for policymakers and educators to design tailored interventions addressing the AI divide.