Generative AI Practices, Literacy, and Divides: An Empirical Analysis in the Italian Context
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 25 of its 64 references that are in the map. First posted on arXiv 3 Dec 2025.
Builds on
25 of 64 references.
References · 25
- 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
- 2025Gemma 3 Technical Reportcited by 1,686
- 2025Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Modelscited by 1,175
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
- 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
- 2023Don't Trust ChatGPT when Your Question is not in English: A Study of Multilingual Abilities and Types of LLMscited by 103
- 2023Combating Misinformation in the Age of LLMs: Opportunities and Challengescited by 94
- 2025Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversationscited by 88
- 2024The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Modelscited by 87
- 2023The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactionscited by 45
- 2023Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomiescited by 34
- 2024Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQcited by 27
13 more
- 2023The impact of generative artificial intelligence on socioeconomic inequalities and policy makingcited by 20
- 2024User Intent Recognition and Satisfaction with Large Language Models: A User Study with ChatGPTcited by 19
- 2025The AI Gap: How Socioeconomic Status Affects Language Technology Interactionscited by 15
- 2025Working with AI: Measuring the Applicability of Generative AI to Occupationscited by 12
- 2023Artificial Intelligence across Europe: A Study on Awareness, Attitude and Trustcited by 9
- 2023Cross-Platform Social Dynamics: An Analysis of ChatGPT and COVID-19 Vaccine Conversationscited by 5
- 2023The Unintended Consequences of Censoring Digital Technology -- Evidence from Italy's ChatGPT Bancited by 3
- 2024Is ChatGPT Massively Used by Students Nowadays? A Survey on the Use of Large Language Models such as ChatGPT in Educational Settingscited by 3
- 2025Attention to Non-Adopterscited by 3
- 2024The Emerging Generative Artificial Intelligence Divide in the United Statescited by 2
- 2024Linguistic Landscape of Generative AI Perception: A Global Twitter Analysis Across 14 Languagescited by 2
- 2025What Shapes User Trust in ChatGPT? A Mixed-Methods Study of User Attributes, Trust Dimensions, Task Context, and Societal Perceptions among University Studentscited by 1
- 2025Investigating Thematic Patterns and User Preferences in LLM Interactions using BERTopiccited by 1
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Citing papers per year: 2026: 2.
2 papers.