Abstract
<title>Abstract</title> <p>Generative artificial intelligence (GenAI) increasingly mediates the epistemic work of higher education: students use it not only to produce text, but also to locate information, interpret concepts, evaluate claims, obtain feedback, and regulate study. This creates a measurement problem that is not captured directly by existing measures of AI trust, technology acceptance, AI literacy, cognitive offloading, or general reliance. This article conceptualises uncritical epistemic dependence in AI-mediated learning as a context-sensitive tendency to treat AI-generated outputs as epistemically directive, such that learner verification, interpretation, justification, production, or regulation may be displaced. It reports the development and initial validation of the Epistemic Dependence in AI-Mediated Learning Scale (ED-AIL Scale), a risk-oriented instrument designed to distinguish ordinary or productive AI support from epistemically consequential delegation. The scale was developed through construct specification, expert review, cognitive pretesting, and two independent Prolific samples of English-speaking higher education students. Evidence from ordinal exploratory and confirmatory factor analyses, reliability estimates, validation measures, performance-oriented criteria, and measurement-invariance tests supported a provisional five-domain interpretation: knowledge acquisition, conceptual interpretation, epistemic evaluation, knowledge production, and epistemic regulation. Measurement evidence was strongest for acquisition, interpretation, and evaluation; more qualified for production; and weakest for regulation. ED-AIL offers an initial research tool for examining how learners allocate epistemic responsibility between themselves and AI systems, while requiring further validation across institutional, disciplinary, linguistic, platform-specific, and task-specific contexts.</p>