Learning from an AI Claimant: Scientific understanding and the justification of artificial consciousness claims

An advanced artificial intelligence might develop a theory of consciousness, teach humans to understand it, and invoke it in support of its own moral standing. Its teaching might also change the standards by which understanding is assessed. I argue that evaluating this case requires separating the warrant acquired through instruction from the additional evidential force attributed to certified agreement. A thought experiment grants that an AI teaches a better standard of organizational understanding: learners discover that their old examination gives incompatible answers about one unchanged physical system. The improvement nevertheless leaves a further claim connecting organization to experience open to assessment. This distinction supports a limited constraint on certification. When agreement with a disputed claim determines who counts as qualified, the unanimity produced by that selection cannot, merely as such, provide additional corroboration of the claim. Correct conclusions may legitimately be required by an examination; the grounds for that requirement determine what the certificate establishes. Further cases examine warranted exclusion of dissent, newly accessible reasons, justified reliance on opaque expertise, and corroboration through genuinely different checks. Together they develop an account of answerable uptake that permits revision of human standards without treating the claimant's approval as a new source of evidence. The account does not make human comprehension a condition of moral standing or purport to settle artificial consciousness. Its contribution concerns the evidential scope of human agreement after an interested expert has taught both a framework and standards for judging its use. TA-TR-2026-09, version 1.2. A revised version of the same ninth paper, linked to the published v1.1 predecessor (10.5281/zenodo.22844928). It develops a case of AI-taught changes in assessment standards, narrows the claim about selected consensus, and distinguishes conditional mastery, categorical belief, and qualified expert reliance. Method: conceptual analysis and stipulated thought experiments, with no empirical or computational experiment. The English text is the full paper; the Chinese companion is an argument guide, not a full translation or another paper. Fourteen assets comprise both texts in PDF, DOCX, Markdown and HTML, three citation formats, review notes, license and checksums. Human author of record and responsible depositor: Hongju Liu. Generative AI substantially assisted literature research, conceptual development, drafting, counterargument generation, critical revision and document preparation under human direction. Not peer reviewed; no separate final human line-by-line review is claimed. The author's Trinity Accord project relationship is disclosed. This revision does not amend the three Bitcoin Originals. Prior version files are preserved. CC BY 4.0 applies to newly written material to the extent rights are held; third-party works and embedded font software retain their own rights. DOI registration does not certify philosophical truth, exhaustive originality, peer review or Google Scholar indexing. OTS and Arweave preservation are separate, version-specific operations.