Generative AI Practices, Literacy, and Divides: An Empirical Analysis in the Italian Context

TL;DR

This study analyzes how generative AI adoption and literacy vary among Italians, revealing that digital divides persist and are influenced by education, age, gender, and AI training, affecting equitable benefits.

Contribution

It provides empirical insights into the adoption, usage, and literacy of GenAI in Italy, highlighting disparities and the role of digital literacy in purposeful engagement.

Findings

Younger, more educated, and tech-savvy individuals adopt GenAI more readily.

AI training significantly predicts purposeful, capital-enhancing use of GenAI.

Gender influences both adoption rates and usage frequency.

Abstract

The rise of generative AI (GenAI) chatbots accessible via conversational interfaces is transforming digital interactions and holds economic promise. However, these tools might deepen existing inequalities -- not only through uneven, socially stratified adoption, but through differentials in their purposeful, critical use. Drawing on original survey data from 1,906 Italian-speaking adults, we provide a comprehensive analysis of GenAI adoption, literacy, and usage patterns. Our findings show that GenAI is supporting diversified personal and professional activities and replacing traditional information-seeking tools. Yet less-educated and older individuals, and those with lower technology familiarity, are less likely to adopt it; 40% cite competence barriers as a key obstacle. Among users, AI training emerges as the primary predictor of purposeful, capital-enhancing engagement -- content creation, learning, and creativity enhancement -- while more passive, recreational uses (e.g., companionship, information seeking) remain insensitive to competence levels. We thus highlight digital literacy as a lever for how people leverage GenAI, not just whether they use it. Finally, gender operates as a persistent cross-cutting divide, shaping both adoption and usage frequency. These findings challenge the assumption that high accessibility translates into broadly shared gains. Rather, they offer a granular, multi-level account of emerging disparities in the GenAI era -- with implications for how this technology may ultimately drive outcomes and benefit divides.