Sensemaking: More Than Finding Answers

In the first post of this series, I focused on judgment: the human ability to choose wisely among possibilities. AI can generate options, suggest recommendations, and produce polished responses, but students still need to evaluate those possibilities and decide what makes sense.

That leads directly to another important form of human thinking: sensemaking.

For much of modern education, finding information was a major part of the learning process. Students had to search for sources, locate explanations, gather evidence, and identify useful material. That work still matters. But the challenge has changed.

Information is no longer scarce. Answers are everywhere. Explanations are everywhere. Summaries, examples, definitions, outlines, and recommendations can now be generated almost instantly.

At first, that may seem like a solution. If students have easier access to information, shouldn’t learning become easier? In some ways, yes. AI can help students access explanations, explore unfamiliar concepts, and encounter ideas they might not have found on their own. That access can be valuable.

But access to information is not the same thing as understanding. And having an answer is not the same thing as making sense of it. That distinction may become one of the most important educational challenges of an AI-rich world.

When information is difficult to find, students may spend much of their effort locating it. But when information is abundant, the more important question becomes: What does this information mean? How does it connect? Why does it matter? When does it apply? What should I do with it?

Those are sensemaking questions.

Sensemaking is the process of organizing information into understanding. It is what students do when they connect a new idea to something they already know. It is what happens when they compare explanations, notice patterns, identify contradictions, recognize context, and decide what is important.

It is not simply collecting information. It is building meaning.

That is why sensemaking matters so much right now. AI can generate a response that looks complete, but students still need to understand how that response fits into a larger context. They need to know whether the explanation matches what they have learned in class. They need to recognize when an answer is too general, too narrow, too confident, or missing important details.

They need to ask: What is the main idea here? How does this connect to what I already know? What context is missing? What seems important, and what seems secondary? What assumptions are being made? Where might this explanation break down? How would I explain this in my own words?

Those questions move students beyond finding answers and toward making meaning.

This matters because AI can easily create the appearance of understanding. A student can ask for an explanation and receive something clear, organized, and persuasive. They can ask for a summary and receive something that sounds complete. They can ask for an example and receive something that seems relevant.

But students may still not understand the concept. They may recognize the words without understanding the relationships. They may be able to repeat the explanation without being able to apply it. They may accept the summary without noticing what was left out. They may mistake familiarity for understanding.

That risk is not unique to AI. Students have always been able to memorize definitions, copy notes, or follow procedures without deeper understanding. But AI makes this easier because it can produce fluent explanations so quickly. It can reduce the friction that sometimes forces students to work through meaning for themselves.

And that friction matters.

Learning often happens in the struggle to connect ideas. It happens when students pause and ask, “Wait, how does this fit?” It happens when they compare two ideas that seem similar but are not the same. It happens when they realize an example does not quite match the rule. It happens when they have to explain a concept in their own words and discover where their understanding is still incomplete.

Those moments are not obstacles to learning. They are part of learning.

If AI removes too many of those moments, students may complete the task without doing the sensemaking work the task was designed to develop.

That is why educators need to think carefully about how we design for sensemaking.

One way to do that is to ask students to connect ideas rather than simply retrieve them. Instead of asking students to define a concept, we might ask them to explain how that concept relates to another idea from the course. Instead of asking for a summary, we might ask students to identify the most important point and explain why it matters. Instead of asking students to find an answer, we might ask them to compare multiple explanations and decide which one is most useful for a particular audience or situation.

The goal is not to make assignments more difficult for the sake of difficulty. The goal is to make the thinking more meaningful.

For example, students might use AI to generate three explanations of a concept at different levels: one for a beginner, one for a professional, and one for someone applying the idea in a real-world situation. Then the assignment could ask students to evaluate the differences. What changed across the explanations? What stayed the same? Which explanation best supports understanding? What would they revise?

That is sensemaking.

Or students might ask AI to summarize a reading and then compare that summary to their own interpretation. What did AI emphasize? What did the student notice that AI missed? What changed after reviewing both? What parts of the reading still require closer attention?

That is also sensemaking.

In a technical course, students might ask AI to explain a troubleshooting process. But instead of simply accepting the explanation, they could map the steps to a specific scenario. Which steps apply? Which do not? What information would they need before deciding what to try first? How would the explanation change if the symptoms were different?

Again, the focus shifts from receiving information to making meaning from it.

This is the same shift I keep returning to throughout this work. AI is very good at generating content. But education is not only about content. Education is about helping students understand what content means, how ideas connect, and how knowledge can be used.

That requires active thinking.

It requires students to slow down long enough to examine relationships between ideas. It requires them to notice patterns and gaps. It requires them to move from “I found an answer” to “I understand why this answer matters.”

This is especially important because students will encounter AI-generated information far beyond the classroom. In their workplaces, they may receive AI-generated reports, recommendations, summaries, analyses, or plans. In their personal lives, they may encounter AI-shaped search results, media feeds, health information, financial advice, or political messaging.

The challenge will not be whether information is available. The challenge will be whether they can make sense of it.

That means sensemaking is not just an academic skill. It is a civic skill. It is a workplace skill. It is a lifelong learning skill. Students need to practice asking better questions of information: What is this really saying? How does this connect to what I already know? What does this mean in context? What should I do with this? What do I still not understand?

These questions help students remain active participants in their own learning. They help prevent AI from becoming a tool that simply delivers answers while students remain passive. They shift AI from an answer machine to a thinking partner, but only if students are doing the work of interpretation.

That is where educators matter.

We can design learning experiences that ask students to organize, connect, compare, interpret, and explain. We can ask students to identify the difference between information and understanding. We can create moments where students must explain meaning, not just repeat content.

And we can model that process ourselves.

When we ask students to slow down and make sense of ideas, we are reminding them that learning is not simply about getting to the answer faster. Sometimes learning requires staying with the question longer. Sometimes it requires wrestling with confusion. Sometimes it requires comparing imperfect explanations. Sometimes it requires recognizing that the first answer is not always the deepest one.

AI may make information easier to access. But meaning still has to be constructed. That is the work of sensemaking. And in an AI-rich world, it may become 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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