研究意大利人使用生成式AI的差异，发现素养决定能否真正用好它。
Generative AI Practices, Literacy, and Divides: An Empirical Analysis in the Italian Context
- 基于1906人的调查，分析生成式AI的使用与数字素养关系。
- 40%用户因能力不足不用，训练水平决定能否用于创作学习。
- 教育程度低、年龄大、技术不熟者更难使用，性别也有影响。
生成式AI聊天机器人通过对话界面正重塑数字交互并带来经济潜力。然而，这些工具可能加剧现有不平等——不仅体现在社会分层导致的使用不均，还在于有目的、批判性使用的差异。基于对1906名意大利语成人的原创调查数据，本文全面分析了生成式AI的采纳、素养与使用模式。研究发现，生成式AI正支持多样化的个人与职业活动，并替代传统信息获取工具。但教育程度较低、年龄较大及技术熟悉度较低的人群更难采纳；40%受访者将能力障碍视为主要阻碍。在使用者中，AI训练水平是推动有目的、提升资本行为（如内容创作、学习、创意增强）的首要因素，而被动娱乐用途（如陪伴、信息获取）则与能力水平无关。因此，数字素养不仅是是否使用的关键，更是如何有效利用的关键。最后，性别始终构成跨维度的差异，影响采纳与使用频率。这些发现挑战了‘高可访问即普惠’的假设，揭示了生成式AI时代下多层次的新兴不平等，对技术最终如何影响结果与分配具有深远意义。
原文摘要 · Abstract (English)
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.
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