I believe AI can explain a concept in a way I understand well enough to apply it in my life. That is why I see value in using AI for practical learning. An explanation is useful to me when I can connect it to a situation outside the classroom. Getting an answer is one thing; knowing what to do with it is another.
The discussion about AI and critical thinking often focuses on whether students are taking shortcuts. I understand that concern, but I think we should also ask what happens after an explanation appears. Do I question it? Can I explain it in my own words? Can I use the idea in a different situation? Those questions matter more to me than how quickly the answer arrives.
How AI for Practical Learning Supports Independent Thinking
In AI and Critical Thinking: Is It Hurting or Helping College Students?, Shahd Gazzaley writes, “Another thing is that they can use AI to challenge their ideas, not to just agree with them.” I agree with this point because a helpful explanation should give me something to examine. If I only accept an answer because it sounds confident, I have skipped an important part of learning.
I would rather ask why an explanation makes sense, what assumptions it uses, and where it might fall short. Stanford’s discussion of critical thinking in the age of AI also emphasizes keeping learners actively involved in the decisions behind their work. For me, that means treating a response as a starting point for questions instead of the end of the conversation.
A Hypothetical Example: Search Intent and Pest Control
Consider a hypothetical example from marketing. Imagine I am learning about search intent and want an explanation connected to a pest control business. AI could compare someone searching “signs of carpenter ants” with someone searching “carpenter ant treatment near me.” The first person may want information about a possible problem. The second may be closer to hiring a service. These are reasonable interpretations, not guarantees about every searcher.
Turning the Explanation Into a Decision
That comparison makes the concept easier for me to picture. For the first search, I might plan an educational article explaining what someone should look for and when to seek help. For the second, I might plan a service page that clearly explains the business’s service area and how to contact it. The difference is the reader’s likely purpose.
I would still need to decide whether those pages actually answer the reader’s questions. Google’s guidance on helpful, people-first content stresses creating useful information for an intended audience. In this hypothetical situation, that gives me a way to evaluate the idea: would the visitor leave with the information they came for? AI can suggest a connection, but I have to judge whether it is useful.
Using Explanations Without Becoming Dependent
Marissa Gleason describes using AI early in the process rather than generating a finished assignment. In Navigating the Tightrope, she writes, “we should use it at the very beginning as a structural scaffold.” This connects with my view because support is most valuable when it helps me begin understanding something and leaves room for my own decisions.
My Test: Explain It Without the Screen
One approach I would use is to close the response and explain the concept in my own words. With search intent, I should be able to describe why two searches about the same pest might need different pages. Then I could try another example, such as comparing “why do mice enter houses” with “mouse removal near me.” If I cannot make that connection without copying the response, I need more practice.
For me, using AI for practical learning works when the explanation leads to independent understanding. I do not think a simple explanation makes learning less valuable. What matters is whether I can question it, check it against reliable sources, and apply it myself. That is the kind of help I want from AI: a clearer way into an idea, followed by thinking I can carry beyond the screen.