Judgment: Choosing Among Possibilities

In the first series, I argued that AI is exposing problems that were already present in education. In the second, I focused on practical instructional design strategies that can help make student thinking more visible. But that raises a deeper question: What kind of thinking are we trying to make visible in the first place?

If AI can generate answers, summaries, drafts, explanations, and recommendations, then educators have to be even more intentional about the human capacities we are helping students develop. This series will focus on those capacities: judgment, sensemaking, curiosity, ethical reasoning, transfer, intellectual humility, and metacognition.

I want to begin with judgment.

Judgment may be one of the most important forms of human thinking in an AI-rich world. Not because AI is useless. Quite the opposite. AI can be incredibly useful. It can generate ideas, compare options, summarize information, suggest approaches, identify patterns, and offer recommendations.

But generating possibilities is not the same thing as choosing wisely among them.

That distinction matters.

When students use AI, they are often presented with something that looks complete. A response appears. It is organized. It is fluent. It may sound confident. It may even be helpful. But the presence of an answer does not remove the need for judgment. In many cases, it increases it.

Students still need to ask:

·      Is this accurate?

·      Is this appropriate for the context?

·      What assumptions is this making?

·      What evidence supports this recommendation?

·      What might be missing?

·      Who could be affected by this decision?

·      Is this the best option, or simply the most convenient one?

Those are judgment questions. And they are not new. Students have always needed to evaluate information, make decisions, and justify their reasoning. AI has simply made that need more visible.

For a long time, many educational tasks were built around producing answers. Students were asked to write the response, solve the problem, complete the discussion post, generate the summary, or submit the project. Those tasks still have value. But in an environment where AI can help produce many kinds of answers, we need to think more carefully about what we are really asking students to practice.

If the task is only to produce an answer, AI can often participate heavily in that work.

But if the task is to evaluate competing answers, explain a decision, defend a recommendation, identify weaknesses, or adapt a solution to a specific context, then students are practicing something deeper.

They are practicing judgment.

This is why I think judgment needs to become a more explicit learning goal. We often assume students will develop judgment along the way. We give them content. We give them assignments. We ask them to complete work. And over time, we hope they become better thinkers.

But judgment does not always develop automatically. It develops through practice. It develops when students are asked to compare, evaluate, explain, revise, and decide. It develops when they encounter uncertainty and have to make a choice anyway. It develops when they are asked not only what they think, but why they think it.

AI can actually create useful opportunities for this kind of practice.

For example, instead of asking students to generate one solution to a problem, we might ask them to compare three possible solutions, including one generated by AI. Which solution is strongest? Which is weakest? What criteria did they use to decide? What trade-offs did they notice?

Instead of asking students to summarize an article, we might ask them to evaluate an AI-generated summary. What did it capture well? What did it miss? What did it oversimplify? What would need to be revised before the summary could be trusted?

Instead of asking students to produce a recommendation, we might ask them to review multiple recommendations and explain which one they would implement in a particular context. Why that option? Why not the others? What evidence shaped their decision?

These are small changes, but they shift the cognitive work.

The focus moves from producing information to evaluating information. From completing the task to explaining the decision. From accepting an answer to exercising judgment.

That shift is important because students are going to live and work in environments where AI-generated possibilities are increasingly common. They may have tools that draft emails, suggest diagnoses, generate code, summarize policies, recommend designs, analyze data, or propose business strategies. But someone will still need to decide what to trust, what to question, what to use, and what to reject.

That someone is still human.

This is where the role of educators becomes especially important. We are not simply preparing students to use tools. We are helping them develop the thinking needed to use tools responsibly. That means creating learning experiences where students practice making decisions, not just producing outputs.

In my own teaching, I see this most clearly in troubleshooting. A student may be able to find a possible fix quickly. AI may even suggest several possible fixes. But the learning is not only in finding a fix. The learning is in deciding which fix makes sense based on the evidence available.

·      What symptoms are present?

·      What changed recently?

·      Which explanation is most likely?

·      What should be tested first?

·      What risks come with this solution?

·      What would we try next if this does not work?

Those questions require judgment. They require students to slow down, examine evidence, weigh alternatives, and make a decision they can explain. That is the kind of thinking I want students to practice.

And this applies far beyond technical fields. In writing, students need judgment to decide which evidence belongs in an argument. In healthcare, students need judgment to interpret symptoms, context, and patient needs. In business, students need judgment to evaluate risks and opportunities. In education, students need judgment to decide what supports learning and what merely creates efficiency. In civic life, students need judgment to evaluate claims, media, policies, and competing perspectives. Across fields, judgment is not just a professional skill. It is a human one.

That is why I worry when AI literacy is framed too narrowly as prompt writing or tool use. Those skills matter. Students should learn how to interact effectively with AI systems. But AI literacy cannot stop there. Knowing how to get a response from AI is not the same as knowing what to do with that response.

The deeper skill is judgment.

·      Can students evaluate the response?

·      Can they recognize when it is incomplete?

·      Can they identify when it sounds right but may not be right?

·      Can they adapt it to context?

·      Can they explain why they accepted part of it, revised part of it, or rejected it entirely?

Those are the questions that matter.

If we want students to develop judgment, we need to design opportunities for them to practice it. That does not require redesigning an entire course. It may begin with one assignment, one prompt, or one discussion question.

·      Ask students to choose between alternatives.

·      Ask them to explain their criteria.

·      Ask them to identify trade-offs.

·      Ask them to defend a decision with evidence.

·      Ask them to evaluate an AI-generated answer instead of simply generating one.

·      Ask them what would change their mind.

These small instructional moves help students practice the kind of thinking that cannot be reduced to answer production. They also make that thinking more visible, both to instructors and to students themselves.

And that may be one of the most important shifts we can make.

Because in an AI-rich world, students will not be judged only by whether they can produce information. Increasingly, they will be judged by whether they can make sense of information, evaluate it, and use it wisely.

AI can generate possibilities. Students need to learn how to choose among them.

That is judgment. And it may be one of the most important forms of human thinking we can help students develop.

Continuing the Conversation

Series 1: AI Is Exposing Existing Problems ✓ Completed
Series 2: What We Do About It ✓ Completed
Series 3: Cultivating Human Thinking
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