How AI is Changing Math Education: Lessons from Testing Four Large Language Models

In recent years, Large Language Models (LLMs) — the powerful AI systems that can understand and generate human-like text — have been making their way into classrooms, online learning platforms, and educational tools. While they can help in many subjects, one area where their potential is especially exciting is mathematics education.

Imagine a student struggling with algebra. Instead of simply getting the correct answer from a calculator, an AI could walk them through each step of the solution, explain why each step is taken, and even point out where the student might be making common mistakes. This is the kind of capability that LLMs are beginning to offer.

But here’s the catch: for AI to be useful in teaching math, it must be consistently accurate — not just with the final answer, but also in the reasoning that leads to it. A wrong step in the middle of a solution could mislead students, causing confusion or reinforcing bad habits.

To explore this challenge, a team of researchers recently conducted an in-depth study comparing the performance of four prominent LLMs in solving different kinds of math problems. The goal was to figure out how accurate these models really are and what kinds of mistakes they tend to make.

The Four AI Models Tested

The study looked at four different LLMs:

OpenAI GPT-4o – A general-purpose AI capable of handling text, images, and complex reasoning tasks.
OpenAI o1 – A reasoning-enhanced version of OpenAI’s technology, designed to perform better on step-by-step problem-solving tasks.
DeepSeek-V3 – A large language model developed with strong problem-solving capabilities.
DeepSeek-R1 – Another DeepSeek model, also tuned for reasoning tasks, but with different architecture and training strategies.

By testing these models side by side, the researchers could see which ones were best at solving math problems and where they fell short.

The Math Problems: Designed to Be Tricky

Instead of using the standard math benchmarks that AI models often perform well on, the researchers created custom problem sets to really challenge the models.

The problems came from three main areas:

Arithmetic – Basic calculations and number manipulation.
Algebra – Solving equations, manipulating expressions, and working with unknowns.
Number Theory – Problems dealing with the properties of numbers, such as factors, divisibility, and prime numbers.

These were not just textbook questions. They were carefully crafted to include situations that are prone to AI mistakes — problems that require careful reasoning, multiple steps, and attention to detail.

Measuring Accuracy: Not Just the Final Answer

Many AI benchmarks only check whether the AI’s final answer is correct. But in real education, that’s not enough. If an AI is teaching a student, how it gets to the answer matters just as much as the answer itself.

The researchers took a more thorough approach:

They checked whether the final answer was correct.
They analyzed each step in the solution, identifying exactly where reasoning errors occurred.
They categorized mistakes into different types, such as:
- Procedural Slips – Simple mistakes in calculations or operations, even when the AI understood the concept.
- Conceptual Misunderstandings – Misinterpreting the problem or applying the wrong method.
- Logical Breakdowns – Steps that didn’t follow logically from the previous ones.

This step-by-step analysis is crucial for understanding how to make LLMs better at teaching math.

The Role of Single-Agent vs. Dual-Agent Setups

The researchers also tested two ways of running the AI models:

Single-Agent Configuration – The model solves the problem alone, step-by-step, without external checks.
Dual-Agent Configuration – One AI solves the problem, and another AI acts as a “reviewer,” checking for mistakes and suggesting corrections before the final answer is given.

The idea here is similar to how humans work better when a second person proofreads their work. The “reviewer AI” can catch errors that the “solver AI” might miss.

The Results: Who Came Out on Top?

The standout performer was the OpenAI o1 model, which is specifically tuned for better reasoning. It scored higher or nearly perfect accuracy across all three types of math problems. This suggests that reasoning-focused training pays off when it comes to solving math step-by-step.

The other models, while capable, were more prone to certain types of mistakes — especially procedural slips, which turned out to be the most common error across all systems. These are the AI equivalent of a student making a small arithmetic mistake in the middle of a correct method, leading to a wrong final answer.

Interestingly, conceptual misunderstandings were much less frequent. This means that in most cases, the AI understood the type of problem and what approach to take, but stumbled in the details of execution.

The Impact of Dual-Agent Systems

When the researchers switched to the dual-agent setup, performance improved noticeably for all models. The reviewing AI was able to catch many procedural errors and correct them before finalizing the solution.

This finding could be a game-changer for integrating AI into education. Instead of relying on one model to be perfect, pairing two models — one to solve and one to review — could make AI-generated solutions far more reliable for students and teachers.

Why This Matters for Education

The implications of this study go far beyond academic curiosity. Here’s why it matters:

1. Building Trust in AI for Education

If students and teachers are going to rely on AI for math help, they need to know it’s accurate. Studies like this help identify weak points so developers can fix them, leading to more trustworthy tools.

2. Improving Step-by-Step Explanations

An AI that gets the answer right but can’t explain how is not much help in education. By focusing on step-level reasoning, this research ensures AI can both do and teach.

3. Making AI a Better Tutor

The dual-agent setup shows that AI can be made more reliable without needing one perfect model. This mirrors human teaching practices, where collaboration and review improve quality.

The Road Ahead

While the OpenAI o1 model performed impressively, there’s still room for improvement in all models. Future research might focus on:

Reducing procedural errors through better training on careful calculation.
Expanding problem variety to cover more math topics.
Integrating adaptive feedback so AI can tailor explanations to a student’s skill level.
Combining AI with human oversight for high-stakes educational settings.

As these systems improve, we may be heading toward a future where every student has access to a patient, always-available AI tutor that can guide them through even the trickiest math problems — without losing patience or skipping important steps.

Conclusion

This study is more than just a comparison of AI models. It’s a roadmap for how to make LLMs more reliable, especially in education. By focusing on both accuracy and reasoning quality, and by experimenting with collaborative AI setups, the researchers have taken a step toward AI systems that are not just problem solvers, but effective math teachers.

The message is clear: AI can help teach math — but only if it learns to think like a good teacher, not just a calculator.

Global Trend Times