Practical guide
AI fluency for university administrators: check course changes
Reconcile a fictional course-change request across timetable, catalogue and participant messages without announcing unapproved details.
University administrators can use AI to coordinate course information while checking that a change is approved and consistent across surfaces. This fictional short-course exercise follows a room and time change. It produces a publication checklist, not an admissions decision, student evaluation or claim about any real institution’s rules.
Separate the request from the approved course record
A change request should not immediately become a public instruction.
The fictional course catalogue lists a workshop on Tuesday at 10:00 in Room A. A coordinator requests Wednesday at 11:00 in Room B. The room team confirms Room B is available, but the course owner has not approved the time change. The exercise provides no other authority to make that decision.
Ask AI to build a state sheet with current published details, requested details and confirmed facts. Room availability supports one prerequisite; it does not approve the whole change. Keep the public version unchanged during this stage and label draft messages so they cannot be mistaken for final instructions.
Map every surface that carries actionable details
A correct catalogue entry can coexist with an outdated message or calendar invitation.
The fictional course appears in a catalogue, an enrollment confirmation, a calendar invitation and a preparation email. List the time, room and status on each. A reference to Tuesday in the preparation email may be just as consequential as the main timetable because readers could act on either.
Ask AI to locate these details in the supplied synthetic texts. Check the originals after extraction, including attachments and subject lines. Do not infer that an unlisted surface was updated. The inventory should distinguish reviewed material from a possible additional dependency that somebody still needs to confirm.
Apply an approval without importing the whole request
An owner may approve only part of the proposed change.
Now the fictional course owner approves Room B but retains Tuesday at 10:00. The correct update changes the room while leaving the day and time intact. A draft prepared from the original request may still say Wednesday at 11:00, so compare it with the actual approval before use.
Create a field-level change note showing old value, approved new value and supporting decision. Do not treat the approval message as blanket acceptance of every earlier proposal. AI can help produce the revised texts, but the approval state must remain the source of truth for each actionable detail.
Prepare a coordinated release and correction route
A change plan should say which version people should rely on and how discrepancies will be handled.
For the exercise, prepare all four updated surfaces and inspect them together before any publication. Identify who can publish each one and how an outdated calendar entry would be corrected. Do not actually email participants or alter a live timetable as part of this practice task.
If one surface cannot be updated, keep that limitation visible to the course owner. Avoid claiming that everyone has been informed merely because a message draft exists. Drafted, sent, delivered and understood are different states; the case does not supply evidence for the latter three.
Deliver a field-by-field consistency check
The handoff should make remaining mismatches easy to identify.
Include the approved Tuesday 10:00 Room B details, the four revised artifacts, owner roles and unresolved publication dependencies. Check headings and calendar fields as well as body text. Record what was inspected so another administrator can repeat the review without reconstructing the original request chain.
To extend the exercise, introduce a later cancellation and ask which surfaces and approval questions reopen. Review status discipline and cross-document consistency. The workplace operations resource chooses feasible spaces; this guide coordinates authoritative course information after a change has been requested.
Sources and scope
NIST: AI risk management. Skills England: workplace AI foundations.
These references provide background, not validation or endorsement of this exercise. The case details, calculations and suggested review questions are original instructional material. Use them to discuss observable work, not to infer customer outcomes, professional credentials or performance in every setting. Before adapting the exercise, confirm the relevant facts, approved tools, data permissions and decision owners. If you change the case, revisit the expected answers and checks as well. These examples describe practice tasks, not a promise that a particular product includes the fictional features.
Sources: [1] [2]
Sources
- 1.AI RMF Core · NIST
- 2.AI foundation skills for work benchmark · Skills England