[论文解读] Generative Artificial Intelligence and the Knowledge Gap: Toward a New Form of Informational Inequality

Raphael Morisco|arXiv (Cornell University)|Mar 25, 2026

Ethics and Social Impacts of AI被引用 0

一句话总结

本文提出将知识鸿沟视角在理论上扩展，以解释生成式AI如何将不平等从获取转向对AI生成信息的评估，突出教育相关的批判性参与差异。

ABSTRACT

The knowledge gap hypothesis suggests that the diffusion of information tends to increase rather than reduce social inequalities. Subsequent research on the digital divide has extended this perspective by focusing on unequal access to and use of digital technologies. The emergence of generative artificial intelligence raises the question of whether these frameworks remain sufficient to describe current forms of informational inequality. While access to AI systems is increasingly widespread, differences may arise in how users engage with AI-generated content. This paper proposes a theoretical extension of the knowledge gap perspective by arguing that generative AI shifts the focus from access and usage to the critical evaluation of information. It is assumed that individuals with higher levels of education are more likely to question and contextualize AI-generated outputs, whereas individuals with lower levels of education may rely more directly on them. The contribution is conceptual and does not present empirical findings. It aims to provide a framework for future research on the relationship between education, AI use, and knowledge inequality.

研究动机与目标

• 在生成式AI背景下重新审视知识鸿沟假说与数字鸿沟。
• 主张AI生成输出将不平等从获取/使用转移到评估与解释。
• 提出捕捉AI媒介信息的检验性能力的理论扩展。
• 突出教育与社会影响，并勾勒实证研究方向。

提出的方法

• 对知识鸿沟、数字鸿沟与AI特征的文献综合。
• 一个以AI为焦点的新的知识鸿沟框架的概念性发展。
• 对AI使用中的评估、解释与检验性能力的理论检验。

实验结果

研究问题

• RQ1 生成式AI如何将信息不平等的传统焦点从获取改为评估？
• RQ2 哪些教育与认知因素影响用户对AI生成内容的参与方式？
• RQ3 一个特定于生成式AI的知识鸿沟框架能否解释超越数字鸿沟的信息处理差异？

主要发现

• 生成式AI在评估与解释AI输出方面创造了一个新的信息不平等维度。
• 对AI生成内容的批判性参与因教育背景而异，影响对输出的质疑和情境化。
• 一个新的构念——生成式AI知识鸿沟，将知识鸿沟视角扩展到AI媒介的信息实践。
• 教育层面的含义强调发展超越基础数字技能的认知能力。
• 本文概述了社会与研究影响，并呼吁进行实证研究以检验该框架。