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Case study · Curriculum and course development

Building a course where none existed

Hundreds of thousands of people do this job. There was no textbook, no accreditation precedent and no competitor syllabus to borrow from. Here is how a decade of practitioner experience became ten modules a college could adopt, staff and stand behind.

1. A course nobody had built

The subject was a job that hundreds of thousands of people do and for which no formal training existed anywhere. There was no textbook, no accreditation precedent, no competitor syllabus to borrow from, and no agreed body of knowledge. There was, on the other hand, eleven years and nine thousand hours of direct experience doing the work.

That combination — deep practitioner knowledge, zero existing structure — is the hardest kind of curriculum to build and the most valuable when it works.

10modules, one hour each
11 yrsof practitioner experience behind it
1stcollege-backed program of its kind in the country
3audiences the same course had to serve

2. Build it backwards

Figure 1

The sequence, and where everyone gets it wrong

The order the work has to happen in 1. Audience who, and why now 2. Outcomes what they can do 3. Assessment how you would know 4. Modules the sequence 5. Content last, not first Almost everyone starts at step five, because writing content feels like progress. Content written before the outcomes exist has nothing to be measured against, and most of it gets thrown away. An accreditation reviewer reads steps two and three. They do not read the slides.

Backward design is standard instructional practice and routinely ignored. The discipline is refusing to write a single slide until the outcomes and the assessment exist.

The test that keeps it honest

Every module has to answer one question: what can the student do at the end of this hour that they could not do at the start? If the answer is “know more about it,” the module is not finished. That single rule removed more material than it added.

3. Where AI helped, and where it could not

The course was built with heavy AI assistance and it would not have been finished otherwise. But the division of labor matters, and overstating it is how bad courses get made.

Figure 2

The honest split

Dividing the work honestly AI did this well Drafting module scaffolding at volume Generating assessment item banks Reformatting to a house style Producing the paperwork a committee needs A person had to do this Knowing what the job is actually like Deciding what to leave out Judging what a beginner will misread Standing behind every factual claim

AI compressed the production work by a large factor. It did not and could not supply the practitioner judgment, which is the only reason the course is worth taking.

The failure we had to design against

An early draft contained illustrative stories that read well and were not verifiable. They were cut. A workforce course that carries an institution’s name cannot contain a single anecdote that will not survive a student asking where it came from. Every claim in the final version is either the author’s own experience or externally sourced and linked.

4. What an institution actually needs

A good course and an approvable course are not the same document. The second one is mostly paperwork, and it is the part that stalls projects.

DeliverableWho reads itWhat it has to survive
Learning outcomes, per module and overallAcademic affairsMapping to the institution’s own outcome framework
Assessment structure and weightingAcademic affairs“How do you know they learned it?”
Student handbookStudents and administrationGrading, academic integrity, appeals, expectations
Disclaimers on earnings and outcomesLegal and administrationAnything that could read as an income guarantee
Instructor materialsWhoever teaches it nextSomeone other than the author running it cold
Delivery and platform planContinuing educationAsynchronous online at their standard
The one that gets missed

Instructor materials. A course that only its author can teach is not a course, it is a performance — and an institution will not adopt it, because adoption means owning something it cannot staff.

5. What this case is meant to show

This is the same discipline as everything else on this site. Define the outcome, work out how you would measure it, then build toward that — and refuse to produce content that has nothing to be measured against.

The usual approachWhat was done here
Start writing contentStart with what the student can do afterward
Use AI to produce volumeUse AI for production, a person for judgment, and say which is which
Good storiesOnly verifiable ones
A syllabusA package an institution can actually adopt and staff
Where this applies outside a classroom

Onboarding, certification, internal training and documentation are all the same problem. Most corporate training fails for the same reason most courses do: it was written as content first, with nobody able to say afterward whether anyone learned anything.

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