# Epistemic Transfer in AI-Assisted Verification: A Framework and Evaluation Protocol

arXiv: https://arxiv.org/abs/2608.08882 · HTML: https://mlsanity.com/paper/2608.08882/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 51 references that are in the map.
- First posted on arXiv 9 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web](https://mlsanity.com/paper/2305.13117/) (2023)
- [Explanations Can Reduce Overreliance on AI Systems During Decision-Making](https://mlsanity.com/paper/2212.06823/) (2022)
- [When combinations of humans and AI are useful: A systematic review and meta-analysis](https://mlsanity.com/paper/2405.06087/) (2024)
- [Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning](https://mlsanity.com/paper/2202.05402/) (2022)
- [How AI Impacts Skill Formation](https://mlsanity.com/paper/2601.20245/) (2026)
- [Fact-checking information from large language models can decrease headline discernment](https://mlsanity.com/paper/2308.10800/) (2023)
- [Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills](https://mlsanity.com/paper/2410.04253/) (2024)
- [AI Assistance Reduces Persistence and Hurts Independent Performance](https://mlsanity.com/paper/2604.04721/) (2026)
- [Dialogues with AI Reduce Beliefs in Misinformation but Build No Lasting Discernment Skills](https://mlsanity.com/paper/2510.01537/) (2025)

## Abstract (arXiv)

AI tools can improve claim judgments while leaving open what users can do later without them. This paper develops an evaluation framework for epistemic transfer: the effect of prior AI-assisted verification on delayed judgments of novel claims under a specified access regime. The contribution is a verification-specific synthesis of learning, transfer, and human--AI evaluation, organized around two complementary estimands. The Epistemic Transfer Effect (ETE) compares delayed performance after alternative practice conditions. Tool-Removal Cost (TRC) compares immediate performance with and without assistance after practice; despite its name, it measures a current availability effect, not skill loss or psychological dependence. The proposed randomized protocol includes answer-first and evidence-first interfaces, active practice, a no-additional-practice comparator, and held-out claims. It specifies how to account for learning opportunities introduced by assessment, elicit confidence probabilities, average model predictions over a target population, and handle attrition and uncertainty. Reading ETE and TRC together distinguishes relative capability gains, equivalence, transfer penalties, and unresolved outcomes. A ``verification-on-loan'' profile is explicitly comparator-relative and cannot be inferred from a nonsignificant delayed contrast. A brief illustration from a two-wave verification study shows why these distinctions matter: an uncertain delayed interface contrast and an ordered assisted--unassisted probe cannot establish a clean transfer profile. The framework makes a practical demand: when independent judgment matters, evaluate both what assistance contributes now and what prior use changes later.