ARCHETYPES OF GENAI USERS: CASE OF GEORGIA
Abstract
We explore how individuals in Georgia engage with generative artificial intelligence (GenAI) by identifying distinct user archetypes based on engagement frequency, the number of tools used, and whether users apply these tools in professional contexts. Drawing on BTU’s Quantitative Research on GenAI Users in Georgia, we develop a classification model that captures a wide range of behavioral patterns and reveals six dominant user archetypes that shape the country’s evolving AI landscape.
We actively examine how users integrate GenAI into daily routines, either through single-tool specialization or multi-platform flexibility, and whether their use is linked to work or personal exploration. The most prominent archetypes include Power Users, who engage heavily with multiple tools for professional tasks; Dedicated Specialists, who master a single tool for consistent work-related use; and Occasional Explorers, who turn to GenAI casually and infrequently. Additional profiles such as Curious Part-timers, Versatile Performers, and Balanced Professionals reflect intermediate or hybrid usage styles, combining varying levels of frequency, diversity, and purpose.
Our findings reveal strong differences by gender and age. Female users tend to adopt a structured and consistent approach to GenAI, often aligning with archetypes that reflect efficiency, reliability, and tool mastery. Male users more often appear in archetypes marked by either intensive use or sporadic experimentation. Meanwhile, younger users display curiosity-driven behaviors, while older individuals integrate GenAI more strategically into their professional workflows.
This research addresses the current gap in AI user classification by offering an empirically grounded, multidimensional framework. We move beyond narrow definitions and propose a scalable typology relevant to education, policy, and product development. The Georgian case demonstrates how digital societies in transition adopt AI not just as a tool but as a working habit-shaped by professional roles, digital maturity, and socio-demographic factors. These insights support the design of targeted training programs and AI tools adapted to specific user needs and expectations.
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