The Curiosity Erosion Model

A Theoretical Framework for Understanding the Impact of Large Language Models on Human Curiosity

Bibliographic Data

ID23039797
AuthorsVenkat Ram Reddy Ganuthula (0000-0002-6899-895X, Indian Institute of Technology Jodhpur, corresponding author), Nidhi Kumari (0000-0001-9849-1888, Indian Institute of Technology Jodhpur)
EditorsPinaki Chakraborty (0000-0002-2010-8022)
Year2026
Volume2026
Issue1
Publication date2026-01-01
Open AccessYes
TypeARTICLE
VenueHuman Behavior and Emerging Technologies (JOURNAL)
Journal identifiersISSN: 2578-1863 • E-ISSN: 2578-1863
PublisherWiley (PUBLISHER • GB)
DOI10.1155/hbe2/6725387
OpenAlexW7154683177
LanguageEN
References cited80

This paper presents the curiosity erosion model (CEM), a conceptual framework that investigates the way large language models (LLMs) affect human curiosity—a vital catalyst for learning. Although LLMs provide immediate access to extensive information, their influence on curiosity has not been thoroughly examined. The CEM delineates four mechanisms through which LLMs may either promote or inhibit curiosity: information accessibility, depth of engagement, metacognitive awareness, and serendipitous discovery. Each mechanism encompasses testable hypotheses that analyze how interactions facilitated by AI modify exploratory motivation. Utilizing meta‐analytic data and empirical studies conducted in educational settings, the model uncovers bifurcated effects, demonstrating that while LLMs can enhance initial engagement, they may simultaneously pose a risk of diminishing independent inquiry, with results being significantly contingent upon the implementation methodology, learner proficiency, and educational context. By synthesizing perspectives from cognitive science, pedagogical theory, and human–AI interaction, the CEM offers a comprehensive framework for comprehending how LLMs transform learning experiences and presents actionable strategies for the development of AI systems that promote rather than hinder curiosity. These recommendations assist educators, designers, and developers in cultivating environments that bolster curiosity‐driven learning, nurturing adaptive and self‐directed learners both within traditional educational frameworks and beyond.

Affect (linguistics) · Cognition · Curiosity · Empirical evidence · Empirical research · Mechanism (biology) · Metacognition · Educational Strategies and Epistemologies · Grit, Self-Efficacy, and Motivation · Psychological and Educational Research Studies

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