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

arXiv: https://arxiv.org/abs/2512.03671 · HTML: https://mlsanity.com/paper/2512.03671/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 64 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 25 of 25)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [Don't Trust ChatGPT when Your Question is not in English: A Study of Multilingual Abilities and Types of LLMs](https://mlsanity.com/paper/2305.16339/) (2023)
- [Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations](https://mlsanity.com/paper/2503.04761/) (2025)
- [Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.05656/) (2023)
- [The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models](https://mlsanity.com/paper/2401.03205/) (2024)
- [The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions](https://mlsanity.com/paper/2310.12418/) (2023)
- [Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies](https://mlsanity.com/paper/2309.13063/) (2023)
- [Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQ](https://mlsanity.com/paper/2403.03814/) (2024)
- [The impact of generative artificial intelligence on socioeconomic inequalities and policy making](https://mlsanity.com/paper/2401.05377/) (2023)
- [User Intent Recognition and Satisfaction with Large Language Models: A User Study with ChatGPT](https://mlsanity.com/paper/2402.02136/) (2024)
- [Working with AI: Measuring the Applicability of Generative AI to Occupations](https://mlsanity.com/paper/2507.07935/) (2025)
- [The AI Gap: How Socioeconomic Status Affects Language Technology Interactions](https://mlsanity.com/paper/2505.12158/) (2025)
- [Artificial Intelligence across Europe: A Study on Awareness, Attitude and Trust](https://mlsanity.com/paper/2308.09979/) (2023)
- [Cross-Platform Social Dynamics: An Analysis of ChatGPT and COVID-19 Vaccine Conversations](https://mlsanity.com/paper/2310.11116/) (2023)
- [Attention to Non-Adopters](https://mlsanity.com/paper/2510.15951/) (2025)
- [The Unintended Consequences of Censoring Digital Technology -- Evidence from Italy's ChatGPT Ban](https://mlsanity.com/paper/2304.09339/) (2023)
- [Is ChatGPT Massively Used by Students Nowadays? A Survey on the Use of Large Language Models such as ChatGPT in Educational Settings](https://mlsanity.com/paper/2412.17486/) (2024)
- [The Emerging Generative Artificial Intelligence Divide in the United States](https://mlsanity.com/paper/2404.11988/) (2024)
- [Linguistic Landscape of Generative AI Perception: A Global Twitter Analysis Across 14 Languages](https://mlsanity.com/paper/2405.20037/) (2024)
- [What Shapes User Trust in ChatGPT? A Mixed-Methods Study of User Attributes, Trust Dimensions, Task Context, and Societal Perceptions among University Students](https://mlsanity.com/paper/2507.05046/) (2025)
- [Investigating Thematic Patterns and User Preferences in LLM Interactions using BERTopic](https://mlsanity.com/paper/2510.07557/) (2025)

## Built on by (most cited first, 2 of 2)

- [Puzzled By ChatGPT? No more! A Jigsaw Puzzle to Promote AI Literacy and Awareness](https://mlsanity.com/paper/2605.20404/) (2026)
- [Do Language Models Know Their Slang? Queer Slang Understanding in User-Generated Content](https://mlsanity.com/paper/2608.04847/) (2026)

## 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.