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This paper proposes a theoretical model for how relying on accurate AI assistance can weaken people's retained, unaided skill, and how perceived fallibility might preserve it through evidence checking. The predictions are testable, but the paper contains only simulations and no empirical validation, so the claims remain hypotheses. For AI readers, the practical question is whether AI tools that reduce practice opportunities carry a learning cost that accuracy metrics alone won't reveal.
Verification Learning Theory: A Process Model of Checking and Retention in AI-Assisted Judgment
arXiv:2610.37875v1 Announce Type: new Abstract: Accurate AI assistance can improve immediate judgments while reducing opportunities to practise the operations needed for independent performance. Verification Learning Theory (VLT) formalises this possibility for claim-evaluation practice followed by correct, independent, rule-specific feedback. The model links perceived assistant reliability to evidence checking and links the history of those checks to retained rule use. Its strict form assumes that task demands and perceived reliability influence learning only through checking, with equal learning per genuine check. It therefore predicts that perceived fallibility can improve delayed unaided judgment even without observed assistant errors, and that this advantage disappears when checking is already required and performed. Rule-specific gains and forecasts from checking histories provide further tests. Simulations examine the consequences and parameter dependence of these commitments; they do not constitute empirical validation. Comparisons with depth-of-processing and feedback-encoding accounts identify 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.
Abstract:Accurate AI assistance can improve immediate judgments while reducing opportunities to practise the operations needed for independent performance. Verification Learning Theory (VLT) formalises this possibility for claim-evaluation practice followed by correct, independent, rule-specific feedback. The model links perceived assistant reliability to evidence checking and links the history of those checks to retained rule use. Its strict form assumes that task demands and perceived reliability influence learning only through checking, with equal learning per genuine check. It therefore predicts that perceived fallibility can improve delayed unaided judgment even without observed assistant errors, and that this advantage disappears when checking is already required and performed. Rule-specific gains and forecasts from checking histories provide further tests. Simulations examine the consequences and parameter dependence of these commitments; they do not constitute empirical validation. Comparisons with depth-of-processing and feedback-encoding accounts identify 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.
Artificial Intelligence (cs.AI)
arXiv:2610.37875 [cs.AI]
(or arXiv:2610.37875v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.37875 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
alternatives_results.json
expected_output_vlt_alternatives.txt
expected_output_vlt_model_sim.txt
expected_output_vlt_s1_summaries.txt
expected_output_vlt_sensitivity.txt
requirements.txt
sensitivity_results.json
sim_results.json
vlt_alternatives.py
vlt_model_sim.py
vlt_s1_summaries.py
vlt_sensitivity.py
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