From Plausibility to Verifiability: The PEARLS Framework for Developing Epistemic Agency in Generative AI-Mediated Higher Education
Abstract
Generative artificial intelligence (GenAI) has sharply reduced the cost of producing fluent explanations, syntheses, analyses, code, and recommendations, but it has not reduced the intellectual work required to establish whether those outputs deserve belief or use. This asymmetry creates a plausibility-verifiability gap: an AI-generated artifact can display the linguistic and structural markers of expertise while the evidence, provenance, and limitations needed to warrant reliance remain difficult to inspect. Existing AI literacy and evaluative-judgement frameworks specify broad competencies for critical and responsible engagement, yet students and novice users still need an actionable method for evaluating a particular AI-mediated artifact. To address this gap, this conceptual paper introduces and theoretically grounds the PEARLS framework, an artifact-level verification protocol organized around six interdependent dimensions: Process, Evidence, Access, Reproducibility, Legitimacy, and Source. The framework integrates insights from epistemic cognition, epistemic vigilance, cognitive offloading, calibrated trust, evaluative judgement, and open-science principles to treat AI output as a provisional knowledge claim whose warrant must be assembled and examined. It further advances verification-driven learning as a pedagogical mechanism through which learners develop expertise by iteratively focusing consequential claims, tracing and testing their warrant, judging uncertainty and legitimacy, acting on the results, and reframing subsequent inquiry. Five interdisciplinary cases - psychology theory, educational statistics, computer science, history, and health sciences - demonstrate how the relative emphasis of the six dimensions varies with disciplinary standards and the consequences of error. The paper concludes by deriving implications for assessment design and proposing a research agenda encompassing construct validation, intervention studies, disciplinary calibration, equity, and human-AI interface design. PEARLS is therefore offered as a theoretically informed and practically usable scaffold for preserving human epistemic responsibility as knowledge production becomes increasingly AI-mediated.
