When better work hides missing judgment
Read together
A student submits an excellent paper with help from an AI agent. The agent helped define the problem and develop the argument. To understand what the student learned, we need to hear which choices they made and see how they respond when an assumption changes.
AI can help people do more demanding work. It can also take over the decisions through which they learn. As educators, we have to decide where learners need to exercise judgment and what would show it has developed.
Opening passage adapted from The Irreducible Officer. Fictional education example.
Which claim would you challenge?
Choose one to open. Bring an example, an objection, or a different view.
1The work can get better while our evidence of learning gets worse.What can the learner now do?
What would you need to see before calling this learning?
A fictional example
A student submits a stronger essay after an agent finds sources, organizes the argument, and revises the prose. The essay improves. We still need to discover which choices the student can explain or carry into a new problem.
An objection
Using tools well is itself a capability. A learner might develop judgment by directing an agent, comparing its alternatives, and rejecting weak suggestions. An unaided test alone could miss that learning.
Discuss
Ask someone to explain a consequential choice and respond when an assumption changes. Offer a choice of spoken, written, or visual explanations so participants can show their reasoning.
2By the time a human approves an AI recommendation, the most important judgments may already have been made.Who framed the choice?
Where would a person need to intervene to have meaningful influence?
A fictional example
An agent shortlists attendance interventions and recommends family reminders. A leader approves the strongest option. None of the options addresses transport because the task was framed as improving family responsiveness.
An objection
AI can expose alternatives a person misses. A person can own a decision built from AI-generated options by examining the assumptions and changing the frame when needed.
Discuss
Ask which objective, excluded option, or affected person could change the recommendation. Who can change the agent’s instructions before it acts? Who remains responsible afterward?
3Some of the friction AI removes is how judgment develops.Which struggle is doing the teaching?
Which struggle should education protect? How would we distinguish it from busywork?
A fictional example
An agent connects three readings before students have tried to reconcile them. The synthesis is useful, but students may miss the experience of finding the conflict that makes the question worth asking.
An objection
Difficulty can exclude learners or waste attention. A scaffold may make deeper thinking possible. The same assistance can be premature for one learner and essential for another.
Discuss
Name the capability a particular effort is meant to develop. Consider what a learner should attempt first, what help would support the next attempt, and what later performance would show progress.
4Asking AI to “make this better” can hand over the meaning of better.Who chose the standard?
Which changes are editing, and which require an educational judgment?
A fictional example
A teacher asks an agent to improve a rubric. It makes the criteria clearer, but also shifts the emphasis from original reasoning to easily measured performance. The teacher now has to decide whether those criteria reward the learning the assignment was meant to develop.
An objection
Our original standards may be weak. AI could reveal omissions or suggest fairer criteria. Keeping the first human definition of “better” would also be a choice worth questioning.
Discuss
Compare the original and revised criteria. Identify whose interests each serves, what each rewards, and which change the teacher is prepared to defend.
5A classroom full of different answers can still be thinking inside the same frame.Different words. Different assumptions?
Which assumptions would you want students to disagree about? Could AI help them find alternatives?
A fictional example
Students propose different school-improvement plans. Every plan treats efficiency as the goal. None asks whether access, belonging, or the quality of learning should take priority.
An objection
People also repeat institutional defaults. AI can introduce unfamiliar perspectives, particularly when someone deliberately asks for competing frames and checks them against lived experience.
Discuss
Compare what the proposals assume, whose interests count, and how success is defined. Ask what a student, family member, or colleague outside the room might dispute.
Bring it back to your work.
Where could AI make a judgment you overlook in something you teach or lead? What would make that judgment visible again?
Choose one example and name the person responsible. What would they need to explain or demonstrate?
Keep exploring
These claims adapt Jack Shaw’s The Irreducible Officer for an education conversation.