From Plausibility to Verifiability: The PEARLS Framework for Developing Epistemic Agency in Generative AI-Mediated Higher Education

Synthesis: Wang (2026) addresses the plausibility–verifiability gap created by Generative AI: 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. The paper introduces PEARLS, an artifact-level verification protocol organized around six interdependent dimensions — Process, Evidence, Access, Reproducibility, Legitimacy, and Source — that treats AI output as a provisional knowledge claim whose warrant must be assembled and examined. It advances verification-driven learning as a pedagogical mechanism through which learners develop expertise, and it demonstrates how the relative emphasis of the six dimensions varies across disciplines.

The Plausibility–Verifiability Gap

Generative AI 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 — plausible surface, hard-to-inspect warrant — creates a distinctive challenge for learners. 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.

The PEARLS Verification Protocol

PEARLS is an artifact-level verification protocol organized around six interdependent dimensions:

1. Process — examining the reasoning and procedure behind the AI-generated output.
2. Evidence — assessing the grounds, data, and support for the claims made.
3. Access — determining the Accessibility and availability of sources and materials.
4. Reproducibility — evaluating whether the result can be reproduced or verified independently.
5. Legitimacy — judging the authority, standing, and trustworthiness of the source.
6. Source — tracing the provenance and origins of the information.

The framework integrates insights from epistemic cognition, epistemic vigilance, Cognitive Offloading, calibrated trust, evaluative judgement, and open-science principles. Its core move is to treat AI output as a provisional knowledge claim whose warrant must be assembled and examined rather than accepted on the basis of fluency.

Verification-Driven Learning

The paper advances verification-driven learning as a pedagogical mechanism: 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. This positions the act of verifying AI output not merely as a protective skill but as a genuine learning activity that builds domain expertise and Critical Thinking.

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.

Connected Concepts

• Generative AI
• Higher Ed
• AI Literacy
• Metacognition
• Trust Calibration
• Cognitive Offloading
• Critical Thinking
• Agency
• Assessment
• Ethics
• Equity In AI Education

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• Semantic Variability LLM Conversation Assessment 2026 — Semantic Variability in LLM Conversation Assessment
• Reclaiming Epistemic Agency Co Agency 2026 — Reclaiming Epistemic Agency
• Coauthorship Integrity Reconceptualising Assessment Validity For The Age Of Gene — Coauthorship Integrity
• AI Feedback Critical Thinking Writing 2026 — Using AI-Generated Feedback to Improve Critical Thinking and Writing Proficiency

Citation

Wang, Z. (2026). From Plausibility to Verifiability: The PEARLS Framework for Developing Epistemic Agency in Generative AI-Mediated Higher Education. EdArXiv preprint.