AI in Math: Does the Tool Position Students as Mathematical Thinkers?
When math teachers worry about AI, they are not being alarmist.
They are fully aware that AI can solve a problem, show the steps, explain the method, check an answer, and produce a polished explanation in seconds. For teachers who have spent years trying to help students build conceptual understanding, that raises an obvious concern: What happens when students can bypass the thinking?
Karen Levin hears that concern all the time.
Karen, founder of Math for Humans, brings an unusual blend of experience to the AI conversation. She began as a high school math teacher in Boston, worked as a math coach, supported blended learning initiatives across schools, and now helps educators think about math teaching in an age of AI.
When she works with math teachers, she often begins by asking them what worries them most. The word that rises to the top is almost always cheating.
But cheating is only part of the challenge.
Teachers also worry about inaccuracy. They have heard that AI can solve advanced Olympiad-level math problems, but they have also seen it make mistakes on basic arithmetic. They worry about students outsourcing their thinking. They worry that AI will become a crutch. They worry that students will get the answer without developing the understanding.
You get the picture.
But Karen’s key contribution is that she reframes the question. The issue is not simply whether students can get an answer from AI. The deeper question is this:
How does the tool position students as mathematical thinkers?
That may be the most important AI question for math classrooms right now.
Listen to my conversation with Karen Levin
The Answer Was Never the Whole Story
Math learning cannot be reduced to answer production. A correct answer can hide shallow understanding. An incorrect answer can reveal thoughtful reasoning. A student may understand the concept but struggle with notation. Another may know the procedure but be clueless as to why it works. A third may be able to solve the problem silently but be unable to explain the reasoning behind it.
In math, the evidence of learning often lives in the messy middle, such as the strategy attempted, the explanation offered, the error noticed, the revision made.
AI did not invent answer-getting. It merely accelerates it.
Karen made a point in our conversation that surprised me. She said she always liked that the answers were in the back of the textbook. Not because the answer did not matter, but because the answer was never the whole story. What mattered was how students were thinking about the problem, what method they were using, and what they understood about the mathematical idea.
Boy, that distinction matters even more now.
If AI turns math into a faster path from problem to answer, then we have a problem. But if AI can help students reason, explain, question, compare, and revise, then we may have something worth exploring.
Step-Giver or Sense-Maker?
The difference is between AI as a step-giver and AI as a sense-maker.
The step-giver says: Here is step one, here is step two, here is step three.
The sense-maker asks: Show me what you tried. Why did you choose that strategy? Where are you uncertain? Can you explain why that step works? What would happen if the numbers changed?
The sense-maker is instructional.
A step-giver may help a student complete a problem. Sometimes that may even be useful. Students need models. At times, they need hints. They need worked examples, too. They also need support when they are stuck.
But if the dominant AI experience is “give me the steps so I can follow them,” then students may learn to imitate mathematical procedures without developing mathematical judgment.
A sense-making tool, by contrast, keeps the student inside the thinking. It does not treat the student as a passive recipient of explanation. It asks the student to contribute, explain, justify, and revise.
Making Mathematical Thinking Visible
That is where Karen’s frame connects so strongly to the underlying challenge of making student thinking visible.
In a world of AI-generated answers, teachers need more than final products. They need evidence of process. They need to see how students are approaching the work, where their reasoning is developing, and where misconceptions are taking shape.
In math, that evidence might take many forms. A drawing. A spoken explanation. A comparison of two solution paths. A reflection on where the student got stuck. An error analysis. A revised explanation after feedback.
This is why tools that ask students to explain their reasoning are so interesting.
In our conversation, we discussed Snorkl, a platform that allows students to draw, write, and record their voice as they explain their mathematical thinking. Teachers can create criteria, and the AI can provide feedback aligned to those criteria. A student might get the answer right but still be asked to explain more clearly, justify a step, or show deeper understanding.
That is a very different use of AI from simply asking a chatbot to solve the problem.
It also reveals a possibility that is often overlooked in conversations about AI and math: maybe one of AI’s most valuable uses in math is not solving, but speaking.
Karen emphasized the importance of math discourse. When she coaches teachers, she notices the familiar classroom pattern: teacher asks, student answers, teacher responds, another student answers, teacher responds. The teacher remains the center of the conversation.
But strong math classrooms require something more. Students need to talk to one another. They need to hear different strategies. They need to explain their thinking out loud, defend it, question it, and refine it.
That kind of discourse is hard to build.
It is especially hard for multilingual learners, who may be developing mathematical understanding and academic language at the same time. But it is hard for native English speakers, too. Explaining math clearly is difficult because it requires more than getting the answer. It requires organizing thought.
In a past post I related the experiences of Bret Xu, a NYC math teacher whose students use Snorkl to explain their mathematical reasoning aloud. Many of them are English language learners. So when they explain a math problem, they are practicing both mathematical reasoning and English academic language.
And they groan when he tells them they are going to use it.
That may sound like failure. But actually it’s the opposite.
They groan because it is hard. They have to think. They have to explain. They have to make their reasoning visible. They cannot simply produce an answer and move on.
That is productive friction.
And productive friction is precisely what AI can easily remove if we are not careful.
AI Tutoring Needs Architecture
This brings us to the crutch problem.
Bret built a custom AI tutor for his students, many of whom arrive with wide differences in math preparation and language background. The tool can respond in Spanish when needed. It can support students at different levels. It can extend his reach as a teacher.
That is powerful.
But he also noticed that students sometimes go to the tutor too quickly. They use it before they have tried to develop a strategy themselves. The tool is so helpful that it can become a shortcut around productive struggle.
This is not an argument against AI tutoring. It is an argument for better architecture.
Karen is cautious here, and I think rightly so. She is not calling for every school to rush into broad student-facing AI use in math. She sees real promise, especially with teachers who are early adopters and who keep student thinking at the center. But she also notes that we do not yet have enough evidence to assume that whole-school AI use with students in math will go well.
That caution is worth noting.
It also points to a practical starting place: teacher use.
Karen sees strong possibilities for teachers using AI to improve planning, differentiation, discourse, language access, and curriculum-connected lesson design. But she is not talking about vague prompts like “differentiate this lesson.” She argues for context. Give the AI research-based frameworks. Connect it to the curriculum. Use it to solve a specific instructional problem.
That echoes lessons from earlier edtech and blended learning efforts. Technology works better when it is connected to a clear learning goal, embedded in the curriculum, used with fidelity, supported by teacher training, and given time inside the school day.
AI is not exempt from those conditions.
In fact, it may need them even more.
So perhaps math departments should begin not with a yes-or-no debate about AI access, but with a better set of questions.
When students use AI, what evidence of mathematical thinking remains?
Did the tool help them reason, explain, revise, compare, question, and justify?
Did it keep them inside the productive struggle long enough to learn?
Or did it simply move them more efficiently from problem to answer?
Karen Levin’s challenge is useful because it shifts the conversation from access to design. AI tools will keep getting better at solving math problems. That is not the hardest question anymore.
The harder and more important task is making sure students keep getting better at thinking mathematically.