Doom Researching: A Conceptual Framework for Repetitive AI-Assisted Information Seeking, Cognitive Offloading, and the Illusion of Knowing

Abstract

Generative artificial intelligence (GenAI) systems such as ChatGPT, Claude, and Gemini have made information seeking faster, more conversational, and more cognitively comfortable. These affordances can support learning and productivity, but they can also encourage a repetitive pattern in which users continue querying AI systems for explanations, summaries, comparisons, plans, and reassurance without converting those interactions into durable understanding, decisions, or finished work. This conceptual paper proposes the term doom researching to describe this AI-mediated pattern of repetitive information seeking without proportional synthesis or output. Building on research on doomscrolling, information seeking, cognitive offloading, transactive memory, human-AI interaction, productivity loss, and the illusion of knowing, the paper develops a framework in which fluent AI responses reduce cognitive effort, inflate perceived knowledge, and increase the likelihood of further querying. The framework distinguishes doom researching from doomscrolling, cyberchondria, ordinary research, and productive AI-assisted work. It introduces a formal model of the doom researching loop, a candidate risk index for empirical measurement, and testable propositions for future studies. It then extends the construct through the lens of the extended mind thesis, distinguishing assistive, substitutive, and disruptive forms of cognitive offloading, and connects individual doom researching to the broader literature on AI-driven homogenization and knowledge collapse. The paper argues that doom researching is not simply "too much AI use" but a misalignment among inquiry, metacognition, and output. The goal is to provide a vocabulary and research agenda for studying when AI-assisted inquiry becomes a substitute for thinking, synthesis, and action.