The deliverable is ready. When someone asks why a particular solution was chosen, you realize you cannot explain it yet. That scene sums up a central question in the relationship between artificial intelligence and education: producing a result and learning to do the work are different goals, and they can coexist.
Defining the learning goal changes the role AI plays. It can offer hints, compare attempts and question choices, instead of taking over the whole execution from the start.
What one study lets us discuss
An Anthropic study on learning to program looked at developers learning a Python library. In the experiment, the group with AI assistance scored lower on a later test of how well they had mastered the material. The study also found differences between ways of using the assistance.
The result covers one task under specific conditions. It does not show long-term cognitive loss, nor does it allow the conclusion that every use of AI harms learning. It is useful for examining how support is organized when building a skill is part of the goal.
Keep chances to explain and decide
If you are learning, make an attempt before asking for the full solution. Ask for comments on your reasoning, compare alternatives and try to explain the result without looking at the answer. These are practice suggestions, to adjust to the content and the situation.
If you already master the activity and need to produce, you can delegate a larger part while keeping the ability to review. Even so, a change in the type of work may require going back to study what is being done.
When training a team, the exercise can ask for a deliverable and an explanation of the choices. The review looks at the document, the sources and the ability to recognize when the solution would not work.
Put learning into the working agreement
When you assign an activity, say what the person needs to be able to do afterward. Producing a presentation and learning to analyze its data are related goals, but they call for different evidence.
The question "can you explain what you just did?" opens a productive conversation. For Dani Hayoshi, whose work connects artificial intelligence, education and entrepreneurship, it is this ability to explain that keeps a person able to decide, review and keep learning. The same principle guides AI Thinking.
Dani Hayoshi is the author of AI Thinking. Her work connects artificial intelligence, education and entrepreneurship. About Dani.