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.

• The paper introduces doom researching, a repetitive AI-mediated querying loop that decouples felt research productivity from genuine knowledge creation.
• It formalizes a mechanistic model linking unresolved uncertainty, response fluency, and cognitive offloading to a widening gap between perceived and actual expertise.
• The study highlights both individual and collective epistemic risks and proposes interventions, such as synthesis checkpoints, to counteract AI-driven homogenization.

Doom Researching: A Conceptual Framework for Repetitive AI-Assisted Information Seeking and Its Epistemic Implications

Overview and Central Claims

The paper "Doom Researching: A Conceptual Framework for Repetitive AI-Assisted Information Seeking, Cognitive Offloading, and the Illusion of Knowing" (2607.02723) introduces the construct of doom researching—a recurrent behavioral pattern where users engage in repetitive AI-mediated information-seeking without proportional synthesis, understanding, or output. Drawing on analogies with doomscrolling, but reconceptualized for the context of AI chatbots and GenAI tools, the work positions doom researching as a distinct loop, characterized by high-frequency querying, increased perceived knowledge, and minimal durable learning or production.

The central claim is that doom researching represents a misalignment among inquiry, metacognition, and productive action—a pattern not reducible to heavy AI use or procrastination, but rather to the AI-enabled decoupling of felt research productivity from actual knowledge formation and artifact creation. This dynamic is contextualized within broader literatures on information seeking, cognitive offloading, metacognitive illusions, and the extended mind.

Theoretical Contributions and Formalization

Definitional Boundaries

The paper delineates doom researching via five essential components:

1. Repetition: Cycles of re-prompting or tool-switching.
2. AI Assistance: Mediation via generative models.
3. Information-Seeking Intent: Aiming at knowledge, explanation, comparison, or planning.
4. Low Conversion: Minimal translation of interaction into durable knowledge or externalization.
5. Metacognitive Inflation: Growth of perceived knowledge not matched by actual competence or output.

This construct is contrasted with adjacent phenomena:

• Doomscrolling: Passive, feed-based, and affectively negative, whereas doom researching is active, agentic, and content-neutral.
• Procrastination: Although doom researching often functions as a guise for avoidance, it is characterized by the subjective impression of productive inquiry.
• AI Overreliance: A broader term; doom researching formalizes a specific dysfunctional loop where repeated AI interaction supplants synthesis.

Mechanistic Model

The paper introduces a formal model describing the likelihood of continued querying ($P(Q_{t+1}=1)$) based on a logistic function of unresolved uncertainty, response fluency, reassurance, cognitive offloading, synthesis effort, and progress toward output. The model theorizes that fluent, reassuring AI responses amplify perceived knowledge ($K^p_t$) more than actual knowledge ($K^a_t$), driving a perceived-actual knowledge gap ($G_t$) that widens as unproductive querying persists. A Doom Researching Risk Index (DRI) is proposed for operationalization in empirical studies, incorporating behavioral, cognitive, and metacognitive measures.

Notably, the model predicts that beyond a certain threshold, increased AI prompt count negatively correlates with artifact output—a dissociation that signals dysfunctional inquiry.

Cognitive Offloading and the Extended Mind

The phenomenon is analyzed via the lens of the extended mind thesis, distinguishing:

• Assistive Offloading: AI supports memory or processing but does not substitute for intrinsic effort.
• Substitutive Offloading: AI replaces discrete cognitive operations (e.g., summarizing literature) at the cost of active synthesis.
• Disruptive Offloading: AI substitutes the reflective and evaluative processes that underpin intellectual agency.

The manager-junior illusion is identified as a phenomenological mechanism: users experience the AI interaction as a form of management or oversight, masking the erosion of evaluative capacity and fostering a false sense of competence.

Practical and Epistemic Risks

Individual Risks

The paper synthesizes evidence that high-frequency, unstructured AI use can:

• Enhance the illusion of knowing, where easy access and fluent articulation inflate perceived mastery [fisher2015internet].
• Lead to loss of self-monitoring and metacognitive accuracy due to substitutive and disruptive offloading [jose2025outsourcing].
• Entrench superficial engagement with scholarly or domain knowledge in both novice and low-performing users [wang2025cognitive].

Collective Risks and Knowledge Homogenization

At the collective level, doom researching amplifies the epistemic risk of AI-driven homogenization and knowledge collapse. Since LLM outputs are probabilistically centered within their training distributions, recursive adoption by researchers feeds central tendencies back into academic and public knowledge, narrowing the diversity of ideas [moon2025homogenizing, derooij2025homogenization]. Empirical studies demonstrate measurable declines in diversity growth rates for AI-generated text and observable "lock-in" effects with widespread LLM usage [qiu2025lockin, peterson2024collapse].

This process threatens the generative substrate from which originality, creativity, and boundary-challenging research emerge.

Design Interventions and Empirical Agenda

The paper proposes practical interventions to mitigate doom researching—output prompts, synthesis checkpoints, redundancy indicators, session transparency (duration, cost), and enforced AI-free recall tasks, among others. However, it cautions that excessive friction risks impeding equitable AI accessibility.

A multifaceted research agenda is outlined, targeting construct discrimination, trait predictors of vulnerability, domain specificity, and intervention efficacy. Of particular importance is empirical validation of the perceived-actual knowledge gap ($G_t$) and dissociation between querying behavior and output quality.

Limitations

The framework is conceptual and unvalidated; empirical work is needed to confirm the discriminant validity of doom researching relative to existing constructs. Furthermore, the risk locus is at the intersection of behavioral habit, tool affordances, and platform economics—an intersection the current work does not model in full generality.

Conclusion

Doom researching conceptualizes a maladaptive loop wherein repeated AI-assisted querying substitutes for internal synthesis and externalization, inflating perceived knowledge without durable gains. The implications are significant for individual expertise development and collective epistemic diversity. The phenomenon challenges researchers, educators, and system designers to calibrate AI integration in ways that optimize for genuine learning, critical evaluation, and productive artifact creation, while resisting convergence toward epistemic stasis. Further empirical investigation is essential to operationalize and mitigate these risks, with particular urgency for knowledge institutions that depend on originality and critical thought.