Althea: The Fact-Checking--Metalearning Tradeoff in AI-Assisted Verification

Abstract:Fact-checking systems must be scalable and epistemically trustworthy. We introduce Althea, a retrieval-augmented system for user-driven claim evaluation that matches standard pipelines on AVeriTeC while improving supported/refuted discrimination. A longitudinal survey experiment (N=961) treats a ten-day follow-up as a fading test: after modeling a verification procedure, we remove the system and ask whether users reproduce it unaided, testing metalearning rather than one-time accuracy. We compare two AI-assisted treatments, Exploratory (guided reasoning) and Summary (synthesized verdicts), against two baselines, unrelated news and Self-search. The treatments yield the strongest immediate accuracy and confidence gains but do not survive the fading test: on unseen claims they perform no better than news, while Self-search, with no procedure to fade, retains a large advantage. This reveals a factchecking-metalearning tradeoff: conditions that most improve immediate accuracy are least likely to produce metalearning, cautioning against treating AI-delivered verdicts as a source of durable literacy gains.

Human-Computer Interaction (cs.HC); Computation and Language (cs.CL)

arXiv:2602.11161 [cs.HC]

(or arXiv:2602.11161v3 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2602.11161 Focus to learn more arXiv-issued DOI via DataCite

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