Transfer: Applying Knowledge in Unfamiliar Situations

In the previous post, I focused on ethical reasoning: the ability to ask not only what AI can do, but whether it should be used in a particular way, for a particular purpose, in a particular context.

That word, context, matters. Because one of the most important signs of learning is not whether students can use knowledge in the exact situation where it was taught. It is whether they can recognize when, how, and why that knowledge applies somewhere else.

That brings us to another important form of human thinking in an AI-rich world: transfer.

Transfer is the ability to apply knowledge, skills, or strategies in new and unfamiliar situations. It is what happens when students take something they learned in one context and use it meaningfully in another.

That sounds simple, but it is not.

Students may understand a concept in class but struggle to recognize it in a workplace scenario. They may solve a problem on a worksheet but miss the same pattern when it appears in a messy real-world situation. They may memorize a definition but not know when the idea matters. They may complete an assignment successfully but not see how the skill connects beyond the course.

That gap has always been one of the central challenges of education.

AI makes it even more important. When AI can generate explanations, examples, solutions, and recommendations quickly, students may be able to complete tasks without fully developing the ability to apply ideas independently. They may be able to ask for help in the moment, receive a response, and move forward. That can be useful. But it can also hide a deeper question:

Can the student recognize what knowledge is needed when the situation changes?

That is transfer. And it may become one of the most important indicators of meaningful learning.

For years, many assignments have focused on whether students can demonstrate knowledge in a familiar format. We teach a concept, give students practice, and assess whether they can reproduce or apply that concept in a predictable way. That structure has value, especially when students are first learning something new.

But real life rarely announces which chapter the problem belongs to. A workplace problem does not usually say, “Use the concept from Week 4.” A patient, client, customer, student, or community issue does not arrive neatly labeled. A technical problem does not always identify which system, process, or principle is involved. Students have to notice patterns. They have to decide what knowledge matters. They have to adapt what they know to the situation in front of them.

That is the work of transfer. And it requires more than recall.

It requires judgment, sensemaking, curiosity, uncertainty, humility, and ethical reasoning. In many ways, transfer brings together all the forms of thinking explored in this series.

Students need judgment to decide which ideas apply. They need sensemaking to understand the new context. They need curiosity to ask what is different about this situation.

They need to think under uncertainty when the connection is not obvious. They need intellectual humility to recognize when their first interpretation may be wrong. They need ethical reasoning to consider the consequences of applying knowledge in a particular way.

Transfer is not just using knowledge. It is using knowledge thoughtfully.

That is why transfer can be difficult to teach. We cannot simply tell students, “Apply this somewhere else,” and assume they will know how. Students need opportunities to practice noticing connections. They need examples of how ideas travel across contexts. They need feedback on how they adapt their thinking when conditions change.

AI can support that process, but it can also complicate it.

On one hand, AI can generate varied examples. It can help students see how a concept might apply in healthcare, business, education, technology, civic life, or everyday decision-making. It can offer scenarios, analogies, and practice problems. Used well, that can help students expand their understanding beyond a single example.

On the other hand, AI can also provide the transfer for them.

If students ask AI, “How does this apply to my situation?” and then simply accept the response, they may miss the thinking involved in making that connection themselves. They may receive an application without developing the ability to recognize or evaluate the application.

That distinction matters. The goal is not for students to outsource transfer. The goal is for students to practice it.

One way educators can support this is by designing assignments that ask students to apply a concept in a new context and explain their reasoning. Not just, “Here is the answer,” but “Here is why this concept applies here.”

For example, instead of asking students to define a principle, we might ask them to identify where that principle appears in a case study, workplace scenario, current event, lab problem, or personal experience.

Instead of asking students to solve a familiar problem, we might change one condition and ask how their approach would need to adapt.

Instead of asking students to summarize a reading, we might ask them to use the reading to interpret a new situation.

Instead of asking students to explain a concept once, we might ask them to explain how the same concept would look different across two settings.

These are not large redesigns. They are small shifts that ask students to move knowledge. That movement is where transfer begins.

In my own technical courses, transfer shows up constantly. A student may learn a troubleshooting process in one lab, but the deeper learning appears when they can use that process in a different problem later. They begin to recognize patterns. They ask better diagnostic questions. They identify what information matters. They adapt when the first approach does not work.

That is more valuable than memorizing a single fix. Because in technology, the exact problem will change. The hardware will change. The software will change. The tools will change. But the ability to reason through a problem, recognize patterns, test assumptions, and adapt to new conditions remains valuable.

That is transfer.

The same is true across disciplines. In writing, transfer happens when students use feedback from one assignment to improve a different kind of writing task. In science, transfer happens when students apply a principle from a controlled lab to a messy real-world issue. In healthcare, transfer happens when students connect classroom knowledge to patient context. In business, transfer happens when students use a concept from one case to analyze a new market or organizational challenge. In education, transfer happens when students move from learning about a strategy to deciding when and why that strategy would support learning.

Across fields, the question is not only, “Did students learn this?” The question is, “Can they use it when the situation looks different?”

AI raises the stakes of that question because students may increasingly have access to tools that can provide immediate support in unfamiliar situations. That support can be helpful. But students still need to know enough to evaluate the support. They need to recognize whether an AI-generated application fits the context. They need to know when the suggestion is too generic, when it ignores constraints, or when it transfers an idea poorly.

In other words, students need to evaluate transfer, not just receive it. That creates an interesting instructional opportunity.

Students might ask AI to apply a concept to a new scenario, then critique the response. Did the tool apply the concept appropriately? What context did it miss? What would need to be revised? What alternative application might be stronger?

Students might generate multiple examples of a concept and sort them from strongest to weakest. Which examples truly show the concept? Which only seem related? What makes the difference?

Students might compare how a concept works in two different fields. What transfers directly? What changes? What assumptions cannot be carried over?

Those activities help students see that transfer is not automatic. It requires interpretation. And that may be one of the most important lessons we can teach. Because students are preparing for a world where knowledge will need to move. They will change jobs. Technologies will evolve. Disciplines will overlap. Problems will become more complex. The specific tools they use today may not be the tools they use ten years from now.

If education focuses only on task completion, students may leave with knowledge that works only in the original container. But if education helps students practice transfer, they develop something more durable. They learn how to carry ideas forward. They learn how to recognize patterns in unfamiliar situations. They learn how to adapt what they know. They learn how to keep learning.

That may be one of the most important human capacities in an AI-rich world. Because AI can generate an answer for the situation in front of us. But students still need to understand how ideas connect across situations. They need to know when prior knowledge applies, when it does not, and how it must change.

That is transfer. And if we want students to develop it, we need to give them opportunities to practice applying knowledge beyond the first example, beyond the original assignment, and beyond the familiar context.

Learning should not stay where it was first taught. It should travel.

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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