Atmospheric Analytics: Situated Encounters in the Age of Generative AI

Abstract

This article challenges the view of technologies, and more particularly generative artificial intelligence (AI), as augmenting experience through binary human–computer interactions and offloading tasks. Instead, it argues that such technologies exhibit atmospheric qualities, shaping situated encounters through an interplay of infrastructure and affect. The article proposes atmospheric analytics as a heuristic framework for investigating these encounters. The framework is intended to be relevant for multiple social and cultural contexts, and is illustrated here by applying it to educational settings. The framework attends to the relational complexity of emerging technologies, such as generative AI, through the careful deployment of three analytical optics: density, saturation, and viscosity. These optics direct attention to atmospheric variations in intensity, vitality, and resistance associated with the proliferation of technologies like generative AI in everyday situations. By focusing on situations, atmospheric analytics move beyond simplistic notions of technologies as instrumental tools or threats, instead examining how they are agentially interwoven within practices. Ultimately, atmospheric analytics contributes to a critical understanding of the social, political, and ethical implications of algorithmic technologies, moving beyond determinism to explore the nuanced and multifaceted realities of human–technology coexistence.