The M-CaReM-AI Method: Defining Work for AI / Unit 2 MCM 045 review, 2 to 3 minutes
Where the Group Hesitated
The idea
A worked example, constructed for this course, turns the library team's hesitations into a list of decisions, and tries the tool before and after.
In practice
The facilitator lists every point where the group paused in the exercise. There are four: how renewals are counted, which date the council figures use, whether a lost item ends a Loan, and whether a laptop Loan follows the same rules as a book Loan. Each becomes an open question with an owner: circulation for the first and third, the reporting lead for the second, and the branch manager for the fourth. Then the team tries an AI tool twice. First, without a definition, it asks the tool to describe a library loan. The answer is fluent and typical. It counts each renewal as a separate loan, assumes fines accrue daily, and invents a loan category field the library has never used. Second, the team gives the tool its rough definition, the six agreed fields and the four open questions, and asks for a one-page summary using only what is given. This time the summary lists the four questions as unresolved instead of answering them.
Where people slip
The tool did not become smarter between the two attempts. The team became more precise, and the second answer reflected that, including honest gaps where the first one had invented confident answers. Had the team accepted the first answer, the invented category field and the daily fines would have entered their requirements looking like research. That is the method's case for doing material cause first, and for treating a fluent answer about your own domain with suspicion until it matches your own definition.
Your place on the trail
Every lesson rests on earlier ones and opens later ones. Tap any to jump.
Unlocks
- Nothing yet depends on this.
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