AI

Verification Learning Theory: A Process Model of Checking and Retention in AI-Assisted Judgment

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 30/10/2026, 06:48:25 UTC.

Briefings

Verification Learning Theory (VLT) proposes that AI assistance improves immediate judgments by reducing independent practice opportunities, with learning driven by evidence checking rather than practice. The model predicts that perceived fallibility can enhance delayed unaided judgment even without observed assistant errors, a finding supported by simulations examining parameter dependence. Key constraints include the assumption that task demands and reliability influence learning only through checking, with equal learning per genuine check, and that the advantage disappears when checking is already required. The framework distinguishes between practised rule retention and broader rule recognition, with simulations not constituting empirical validation.

Key points

• Verification Learning Theory formalises AI assistance as a mechanism for improving immediate judgments while reducing opportunities for independent practice.
• The theory predicts that perceived fallibility can improve delayed unaided judgment even without observed assistant errors, supported by simulations examining parameter dependence.

Context

This research addresses AI-assisted judgment in claim-evaluation practice, where accurate assistance can improve immediate performance but reduce opportunities for independent rule-specific practice. The model links perceived assistant reliability to evidence checking and links checking history to retained rule use, formalising a process of verification learning.

Limitations

Simulations examine consequences and parameter dependence but do not constitute empirical validation. Comparisons with depth-of-processing and feedback-encoding accounts identify divergence points but remain observationally equivalent. The framework specifies experiments to test the mechanism, yet the specific experimental conditions and results are not detailed in the provided evidence.

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