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

TL;DR

This paper extends the knowledge gap hypothesis to the era of generative AI, emphasizing how differences in education influence critical evaluation of AI-generated information, potentially affecting social inequalities.

Contribution

It offers a theoretical framework that shifts focus from access to AI to the critical engagement with AI outputs, highlighting new dimensions of informational inequality.

Findings

Proposes a shift from access to critical evaluation in AI use.

Suggests higher education correlates with better AI content critique.

Provides a conceptual basis for future empirical research.

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.