Accurate AI assistance can improve immediate judgments while reducing opportunities to practice the operations needed for independent performance. Verification Learning Theory (VLT) formalises this phenomenon, focusing on claim‑evaluation followed by correct, independent, rule‑specific feedback. The model links perceived assistant reliability to evidence checking and ties the history of those checks to long‑term rule use. In its strict form the theory assumes that task demands and perceived reliability affect learning only through checking, with each genuine check providing an equal amount of learning. Consequently, the model predicts that merely perceiving the assistant as fallible can boost delayed unaided judgments even when no assistant errors are observed; this advantage disappears when checking is already required and performed. Rule‑specific gains and forecasts derived from checking histories offer further tests. Simulations illustrate the consequences and parameter dependence of these commitments, though they do not constitute empirical validation. Comparisons with depth‑of‑processing and feedback‑encoding accounts reveal where predictions diverge and where the accounts remain observationally equivalent. The framework specifies experiments that can test the proposed mechanism and separates retention of practised rules from the broader problem of recognising which rule applies to a new claim.
Review