Build a China AI Course Evidence Table: Source, Demonstration, Exercise, Result
Do not use one AI tool page, a live demonstration and learner outcomes as the same proof. A four-column evidence table makes each claim reviewable.
Published analysis
Use each article for the decision and evidence scope stated on the page.
Do not use one AI tool page, a live demonstration and learner outcomes as the same proof. A four-column evidence table makes each claim reviewable.
A public China training document can reveal scope, roles and records. It cannot prove private-market demand. Use it to form better buyer questions.
Video production, live sessions, channel operations and reporting are separate workstreams. Scope them before an annual package becomes an unlimited promise.
A workflow template shows nodes and outputs. A teachable method also needs assumptions, judgment points, failure paths and a reviewable learner output.
Uploading course documents to a knowledge base can make text retrievable. It does not design the learner task, evidence or feedback loop a course needs.
The same videos can be sold as a one-off item, a structured column or ongoing membership. Choose the promise you need to test before choosing packaging.
A course video can be playable but still impossible to localize safely. Use a source-package handoff that records editable inputs, boundaries and acceptance.
Zhihu’s question search and paid-column card flows describe different content objects. Use public answers to clarify a problem, not to promise a funnel.
Separate attendance, feedback, skill evidence and business outcomes when reporting China-facing training. A completion report is only one evidence layer.
Chinese course projects separate maintenance, consultation, training and technical response. Treat support as a bounded scope, not an implied promise.