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

Authors: Christoph Trattner

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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.

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.37875 [cs.AI]

(or

arXiv:2610.37875v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.37875

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arXiv-issued DOI via DataCite

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From: Christoph Trattner [ view email ]

[v1]

Tue, 27 Oct 2026 19:18:18 UTC (2,057 KB)

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Ancillary files ( details ) :

README.md

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

(8 additional files not shown)

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