# Generative Artificial Intelligence and the Knowledge Gap: Toward a New Form of Informational Inequality

arXiv: https://arxiv.org/abs/2603.24335 · HTML: https://mlsanity.com/paper/2603.24335/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [The Context Access Divide: Interaction-Level Architecture as a Complementary Dimension of Agentic Inequality](https://mlsanity.com/paper/2607.08495/) (2026)

## Abstract (arXiv)

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.