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

Jul 16, 2026

Generative AI systems enable faster, more conversational information seeking, supporting learning and productivity, but they can also foster a repetitive cycle of querying for explanations, summaries, comparisons, plans, and reassurance without producing durable understanding or completed work. This conceptual paper introduces “doom researching” to describe AI-mediated repetitive information seeking with insufficient synthesis or output. Drawing on related research, it explains how fluent responses reduce effort, inflate perceived knowledge, and increase further querying, and distinguishes it from adjacent constructs.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages16

Reading Time40 min

Introduction

Definitions and motivation

Differences from related constructs

Central claim and contributions

arXiv :2607 .02723v 1 [ cs .HC] 2 Jul 2026 Doom Researching: A Conceptual Framework for Repetitive AI-Assisted Information Seeking, Cognitive Offloading, and the Illusion of Knowing Santosh Premi Adhikari 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. Keywords: generative AI, human-AI interaction, information seeking, cognitive offloading, metacognition, illusion of knowing, doomscrolling, extended mind, knowledge collapse, epistemic diversity, AI overreliance 1 Introduction Generative AI tools have changed the practical experience of research. A user can ask a chatbotto summarize a field, compare theories, explain a paper, generate a research plan, criticize a draft, design a study, create a reading list, and reformulate the same ideas in multiple levels of difficulty. This interaction style makes research feel immediate and personal. It also reduces many of the frictions that traditionally forced researchers to slow down: searching through sources, reading full arguments, taking notes, deciding what matters, and writing preliminary synthesis. The observation motivating this paper is personal. While working on a thesis and exploring unfamiliar research areas, I noticed a recurring pattern: hours spent querying AI systems for explanations, comparisons, and frameworks, accumulating many ideas in conversations, yet producing little written output and retaining less than expected. The feeling of having researched something was vivid; the ability to explain or act on it independently was not. This experience, I suspected, was not unique. The reduction of friction is valuable. AI-assisted search and writing can help users explore unfamiliar domains, overcome blank-page problems, and receive feedback. However, the same affordances can create a paradox: the user may spend more time researching because research has become easier, more stimulating, and less demanding than producing. In this pattern, the user repeatedly asks AI systems for more explanations or better plans, accumulates a large amount of conversational information, feels increasingly informed, but produces little output and retains little stable understanding. This paper pr

FAQs

What mechanism does the paper propose for why doom researching continues?

Fluent AI responses reduce cognitive effort, inflate perceived knowledge, and increase the likelihood of further querying even after marginal value declines.

Jul 16, 2026

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