{"id":385,"date":"2026-09-20T15:30:00","date_gmt":"2026-09-20T07:30:00","guid":{"rendered":"https:\/\/oribridge.org\/blog\/?p=385"},"modified":"2026-09-08T09:38:05","modified_gmt":"2026-09-08T01:38:05","slug":"china-ai-course-evidence-table-source-demo-exercise-result","status":"publish","type":"post","link":"https:\/\/oribridge.org\/blog\/china-ai-course-evidence-table-source-demo-exercise-result\/","title":{"rendered":"Build a China AI Course Evidence Table: Source, Demonstration, Exercise, Result"},"content":{"rendered":"

\u201cThis agent can turn a China market brief into a summary.\u201d<\/p>\n

That sentence appears in many AI-course outlines. It may be a statement about an official feature page, a presenter\u2019s demo, a learner exercise, or a genuine result from someone\u2019s work. Those are not the same kind of evidence. Yet course pages regularly blur them into one smooth claim.<\/p>\n

The repair is not a longer disclaimer. It is a small evidence table with four columns: source, demonstration, exercise, result<\/strong>. Put each claim in one column before it reaches a course page, a workshop slide or a sales conversation.<\/p>\n

The quick answer<\/h2>\n

An official tool page can support a narrow claim about a product surface. A controlled demonstration can show that an instructor produced an output under stated conditions. A learner exercise can show that a participant completed a defined task. A result needs its own before-and-after evidence. If a claim has no matching evidence column, label it as unverified rather than letting it borrow credibility from a nearby demo.<\/p>\n

This is particularly important in China-facing AI education, where tool names, product interfaces, access conditions and workflows can change quickly. A good evidence table makes the course easier to revise without turning every new feature into a promise.<\/p>\n

Four boxes, four different statements<\/h2>\n
\n\n\n\n\n\n\n\n\n
Evidence box<\/th>\nThe narrow question it answers<\/th>\nExample of a defensible statement<\/th>\nIt does not<\/strong> answer<\/th>\n<\/tr>\n<\/thead>\n
Source<\/td>\nWhat does an official document describe?<\/td>\n\u201cThe documentation describes a workflow configuration surface with specified inputs and outputs.\u201d<\/td>\n\u201cThe workflow is reliable for every learner.\u201d<\/td>\n<\/tr>\n
Demonstration<\/td>\nWhat happened in a controlled presentation?<\/td>\n\u201cIn this recorded demo, the instructor used the stated inputs to produce this draft.\u201d<\/td>\n\u201cLearners can reproduce it in their environment.\u201d<\/td>\n<\/tr>\n
Exercise<\/td>\nWhat did a learner actually attempt or submit?<\/td>\n\u201cParticipants completed a source-citation exercise using the stated rubric.\u201d<\/td>\n\u201cTheir workplace performance improved.\u201d<\/td>\n<\/tr>\n
Result<\/td>\nWhat changed outside the exercise?<\/td>\n\u201cA defined workflow metric changed over a stated period, subject to attribution limits.\u201d<\/td>\n\u201cThe tool caused the change by itself.\u201d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

The table is intentionally conservative. Its purpose is not to make a course sound less capable. Its purpose is to stop it from making a claim it has not earned.<\/p>\n

A familiar failure: one sentence, four borrowed meanings<\/h2>\n

Consider a workshop called \u201cBuild a China Research Agent.\u201d The instructor opens a tool, connects a knowledge source and gets a clean answer. The slide says: \u201cThe agent saves analysts hours.\u201d<\/p>\n

What is actually known?<\/p>\n