OpenAI’s 700+ Math Papers: If AI Can Solve Math Problems, What Should Students Still Learn?

What comes to mind when you think about the future of mathematics education?

For me, one question keeps coming to mind: If AI can solve increasingly difficult math problems, why do children still need to spend years learning mathematics?

On October 6, 2026, the world-renowned journal Nature noted in an article that OpenAI released more than 700 AI-generated mathematical preprints addressing a wide range of research problems. The release sparked debate among mathematicians about verification, research standards, and how AI-generated results should be understood and used. While some welcomed the progress, others questioned how the work was presented and evaluated.

For me, this debate raises a question that matters just as much in a school classroom as it does in advanced research: When AI can produce an answer in seconds, what does it mean for a child to truly learn?

I do not believe AI will make mathematics unnecessary. Instead, I worry that we may continue teaching math as though getting the right answer were the only goal.

Getting the Right Answer Is Not the Same as Understanding Math

Imagine a student sitting at the kitchen table, struggling with a fractions assignment. One problem just won’t make sense, so the child turns to an AI tool, a few seconds later, there’s a correct answer with every step neatly explained, the homework gets finished, and everyone can move on. The next day, however, the teacher asks the student to explain why dividing by one-half can produce a number larger than the original number. Suddenly, the student struggles to explain what happened.

This is a familiar challenge in education: children can follow a worked example without knowing how to apply the same idea independently, they may memorize a formula but fail to recognize when to use it in an unfamiliar problem. The issue is not always a lack of effort; sometimes, students have learned a procedure without understanding the reasoning behind it. AI makes this distinction harder to ignore because it can produce convincing explanations that look like learning. A student may understand every step while reading an answer, yet be unable to reproduce the reasoning alone.

That is why I would not focus too much on whether students should use AI for homework, I am more interested in what happens after they get an answer. Do they stop there, or do they try a similar problem on their own? Do they ask why a method works, or simply accept the explanation? Used thoughtfully, AI can help students work through confusion and explore different approaches. But if it does all the thinking for them, finishing the assignment may mean very little when it comes to learning.

What the OpenAI Debate Teaches Us About Learning

AI-generated mathematical research and a middle school homework assignment may seem worlds apart, but both highlight the importance of checking and understanding results.

OpenAI’s release included mathematical results and formalizations of many proofs using Lean, a system that can check mathematical reasoning. Yet producing a large collection of results is not the same as making every result easy to interpret, verify, and build upon. Researchers still need to examine the work and determine what it contributes to existing knowledge.

Students face a similar challenge on a smaller scale. An AI tool may correctly solve a geometry problem using the Side-Angle-Side (SAS) rule. But if a student cannot identify the corresponding sides and included angle in a new diagram, the answer has not yet become usable knowledge.

I would not consider that student a failure. I would see it as a signal that the child needs more opportunities to connect mathematical rules with the ideas behind them.

As AI makes answers easier to obtain, students need to become better at evaluating those answers. That requires more than memorization. It requires a solid foundation and the confidence to ask, “Does this make sense, and why?”

Three Skills Children Still Need to Develop

1. Understanding the reasoning behind an answer

Consider a fifth grader solving this problem: A recipe requires two-thirds of a cup of flour, but the family wants to make one and a half batches. How much flour is needed?

AI can quickly provide the answer: one cup. But the more valuable learning experience comes when the child understands why multiplying two-thirds by one and a half works. The student might draw a diagram, divide the quantities into parts, or explain the relationship between the original recipe and the larger batch.

When children understand the reasoning, they can apply it to unfamiliar situations. When they only remember the answer, their knowledge is much more fragile.

2. Learning to question results

I would like to see more classroom activities in which students examine a solution and look for mistakes. A teacher might present an incorrect calculation or ask students to compare two approaches to the same problem.

For example, an AI tool might calculate the area of a rectangle correctly but use the wrong dimensions after misreading a word problem. Students who know how to reread the question, check units, and estimate whether an answer is reasonable have a better chance of catching the error.

This kind of mathematical judgment develops through practice. Children need opportunities to make decisions rather than simply watching a tool make them.

3. Learning to work through difficulty

This is the skill I worry we may overlook in our enthusiasm for efficiency. Learning often involves trying an approach, realizing it does not work, drawing a diagram, and trying again.

Of course, children should not be left frustrated without support. A hint or a different explanation can help them move forward. But there is a difference between helping a child through a difficult problem and removing every opportunity to think.

Some of the most satisfying moments in learning happen when a child finally understands why an approach failed. If AI completes every difficult step too early, students may miss opportunities to build independence and confidence.

It is interesting, then, that while AI appears to be helping various industries streamline their work, I feel that for students, the widespread adoption of AI has actually increased their workload—they now have more to learn.

Math Education Must Reward More Than Correct Answers

When assignments are graded mainly on final answers, students have little incentive to show how they think. As AI becomes more accessible, teachers can place greater emphasis on explaining solutions, comparing methods, identifying errors, and applying familiar concepts to unfamiliar problems.

This does not mean abandoning technology. AI can offer alternative explanations, generate practice questions, and help students explore concepts from different angles. Teachers can use it as a resource while continuing to assess whether students understand what they are learning.

In my view, the teacher’s role becomes more important in this environment. AI can generate explanations, but educators must decide which explanations meet a child’s needs, when to offer help, and when to let the student try independently.

We should also avoid turning mathematics into a competition between children and machines. Students do not need to calculate faster than AI. They need to develop the judgment to choose a sensible method, recognize mistakes, and use mathematical ideas in situations they have never encountered before.

The Goal Is Understanding, Not Outperforming AI

The recent debate around AI-generated mathematics is a reminder that impressive results still need to be examined, understood, and put into context. For children, the same principle applies every day in the classroom.

I do not expect students to outperform advanced AI tools at calculation, and I do not think they need to. What I want is for a child to look at a problem, understand what it asks, choose a reasonable approach, and explain why the answer makes sense.

Those abilities take time. They develop through practice, thoughtful teaching, mistakes, and the moment when a difficult idea finally clicks. AI can support that process, but it cannot make the process unnecessary.

After an AI-assisted assignment, perhaps the most important question is not: “Did the tool give the right answer?”

It is: “What can my child understand or do now that they could not do before?”

If we keep that question at the center of mathematics education, AI does not have to make learning less meaningful. It can remind us what learning has always required: curiosity, reasoning, and the confidence to think for ourselves.

Simon Holmes | Contributing Education Reporter

Simon Holmes is an education reporter who contributes to WuKong Education, covering K–12 education, education technology and issues affecting students and families around the world.

With a background in education and experience as a teacher, Simon brings firsthand insight into classroom learning, student development, and the challenges educators face.

For WuKong Education, he reports on education research, emerging learning trends, policy developments, technology in the classroom, and issues relevant to parents, teachers, and students.