Understanding Generative AI : mapping media effects within McLuhan’s Tetrad model

Abstract

This thesis explores the media effects of Generative AI by applying the Tetrad model developed by Marshall McLuhan. As the study revealed, public discourse around GenAI is largely shaped by binary oppositions, which limits a deeper understanding of how this technology truly impacts human experience. The research is based on a sequential mixed-methods design. First, academic texts, industry reports, and expert opinions were analysed to identify and collect recurring effects associated with GenAI use. Second, these effects were mapped within the four dimensions of the Tetrad model: enhancement, obsolescence, retrieval, and reversal. Third, an expert survey was conducted to rank these effects. In the final stage, the most-voted effects were selected to design a validated model of GenAI’s media effects. By applying a media ecological framework to GenAI, this thesis contributes to a more holistic understanding of the effects that emerge when GenAI becomes a primary technology to mediate our engagements with the world.