Not a course to browse.
A tutor that teaches you on the work you are already doing.
Claude Learn is a personal AI tutor: point it at your real task, and it teaches you one thing at a time — tied to a named standard, graded against ground truth, and remembered in a ledger that is yours. This site is the demonstration — the thesis, the standards, the evidence they came from, the playbook that generates the lessons, and forty-eight lessons you can read end to end.
Seven chapters, one claim: the craft can be encoded, and here it is.
The job description asks for someone who can define what “great” means precisely enough that a team hits it without them in the room, decide what to trust AI with and where human craft is non-negotiable, and encode that craft into AI-powered systems rather than guarding it. Each chapter answers a piece of that. Read them in order, or jump to what you want to interrogate.
The thesis
Everyone is building courses. Claude Learn is a tutor — it teaches you on the task in front of you, one standard at a time. The argument, and the pedagogy underneath it: don't change the learner, design the environment so they succeed as they are.
Read the thesis → 02 / ChapterWhat “great” means
You cannot hold a bar you have not named. The AIHC learning standards define AI-collaboration capability precisely enough that a team can hit it without the author in the room — 8 practices, 6 core ideas, 11 crosscutting concepts, 11 anchors, across 4 proficiency bands.
Explore the standards → 03 / ChapterCraft that scales
The content team gets world-class at exactly two artifacts — the exemplar and the rigor rubric — and the human is removed from the moment of generation. This is what “encode your craft into the system rather than guard it” looks like, built.
Open the playbook → 04 / ChapterThe evidence
The standards are not asserted from taste. They were derived from 1,702 adjudicated verbatim receipts of one expert practitioner's real working record — distilled into 52 practice families, 38 of them proven across two tool eras. Paucity is a fingerprint, not maturity.
Inspect the families → 05 / ChapterHow it was derived
The full chain, in the open: NGSS/CCSS hybrid method, recall-first finder fleets under a published spec, byte-verified quotes, human-adjudicated structure with licensed dissent, full-corpus adjudication with documented tie-breaks. Rigor you can audit.
Read the protocol → 06 / ChapterThe proof
Four fictional learners — a developer, an enterprise admin, a consumer, a member of the public — each taught a 12-lesson arc on their own real work. Forty-eight lessons, every one standard-tied, doc-set-grounded, and honestly instrumented. Read them end to end.
Meet the learners → 07 / ChapterThe JD, answered
Not “explore and infer.” A direct crosswalk: each thing the Head of Content, Curriculum & Education job description asks for, mapped to the exact artifact on this site that proves it.
See the crosswalk →You are on the hiring committee. This is built for that.
Throughout, amber notes guide the exploration — what to notice, why an artifact is here, where a claim is grounded, and where the honest limits are. Nothing is hidden behind a summary. The standards cross-link to the lessons that teach them; the lessons cross-link back to the standards and to the practice families that evidence them. It is a web of receipts, because that is the argument.