VET: A Framework for Analyzing AI Discourse

2026-06-01

AI Summary

Public discourse on artificial intelligence has become increasingly polarized, with extreme narratives in the media—such as hype, doomsday scenarios, denialism, and normalization—impeding the development of AI literacy. This study proposes the VET framework, which introduces three operational dimensions: Valence, Efficacy, and Trajectory, to systematically deconstruct and categorize AI-related discourse. Through discourse analysis and qualitative content analysis, the framework effectively identifies exaggerated elements within these narratives, offering a structured tool for critically evaluating polarized viewpoints. The research demonstrates the practical utility of the VET framework in AI literacy education, thereby fostering more informed and rational public understanding of AI issues.

Abstract

Public discourse on AI has become polarized; exaggerated positions on AI in traditional and social media threaten the development of AI Literacy among the general public. In this article, I introduce the VET Framework, a method for categorizing AI discourse along the dimensions of valence, effectiveness, and trajectory. I show how this framework can be used to identify, compare, and critique prevalent narratives of AI Hype, AI Doom, AI Denial, and AI Normalcy. Using VET, I analyze how each of these four stances exaggerates some aspects of the current state and/or likely evolution of AI, and illustrate how the VET framework can serve as an AI Literacy tool by supporting the ``vetting'' of polarized AI discourse.

Problem

• AI discourse
• polarization
• AI literacy
• public understanding
• media narratives

Innovation

• VET Framework
• AI Discourse Analysis
• AI Literacy
• Narrative Critique
• Polarization