This is the first post in our three-part series diving into the technology behind MathVizy, our new AI product.
Math is hard. For some, it’s a world of logical puzzles and satisfying patterns; for others, it’s a source of frustration and self-doubt. But whether you’re solving for x or graphing parabolas, math demands more than just answers – it demands understanding.
That’s where AI should come in, right? A perfect digital tutor, ready to explain every step. Unfortunately, that’s not what we’ve got. While AI has transformed industries from healthcare to finance, it hasn’t cracked the code on teaching math. Sure, it can solve problems faster than any human, but when it comes to explaining how or why, it’s like asking a calculator to teach algebra.
What’s holding these tools back? And why does it matter so much for the future of education?
What’s Wrong with Current Math AI?
At its core, the problem isn’t just that today’s AI models make mistakes. It’s that they’re not built to teach, they’re built to deliver. They focus on providing answers quickly, rather than ensuring students understand how to reach them. Here’s where the cracks start to show:
1. Bad Data, Bad Output
Imagine a tutor who learned math by skimming random notes left behind in an unlocked classroom. That’s essentially how most AI systems are trained. They pull from the internet, a treasure trove of information, but also a landfill of errors, inconsistencies, and oversimplifications.
Sure, some data is high-quality, but a lot of it isn’t. When your training material is hit-or-miss, your results will be too. This is why math AI often generates answers that are technically incorrect, poorly phrased, or just plain unhelpful.
Take a common example: calculating the area of a triangle. A good AI might give you the correct answer. A mediocre one might get the formula wrong or skip steps. But the worst-case scenario? An AI that confidently provides the wrong result and leaves you none the wiser.
2. No Teaching Skills
Think back to your favorite teacher. What made them great? It wasn’t just their knowledge—it was how they explained things. They broke concepts down, used examples, and checked to make sure you were following. That’s what good teaching looks like.
AI, unfortunately, doesn’t do that. Most models are built by engineers, not educators. So instead of step-by-step breakdowns, they often deliver dense, jargon-heavy explanations—or worse, skip straight to the answer.
It’s like asking a tutor for help with fractions, and they just say, “Here’s the solution. Figure it out yourself.” Useful? Hardly.
3. Logical Errors (a.k.a. Hallucinations)
Here’s a wild one: sometimes, AI just makes things up. It generates responses that sound plausible but are completely incorrect. These “hallucinations” are especially dangerous in math, where precision is everything.
Imagine struggling with quadratic equations, only for your AI tutor to confidently teach you a “shortcut” that doesn’t actually work. It’s not just confusing—it’s counterproductive.
4. The “Test-Prep Trap”
AI loves benchmarks. Many models are designed to ace standardized tests, which might sound impressive—until you realize those tests aren’t representative of real-world learning.
Students don’t need AI that can pass exams; they need AI that can teach them how to solve problems, understand concepts, and build confidence. But when the focus is on scoring high rather than teaching well, students are left in the lurch.
Why Most Online Math Content Isn’t Helping Either
If AI doesn’t work, you might turn to the internet for help. And if you’ve done that, you know how quickly things can go downhill.
📐📈 No Visuals: Math is visual by nature. Diagrams, animations, and step-by-step illustrations make concepts easier to grasp. But most online resources are just walls of text—and not even good text at that.
❌👩🎓 SEO Over Students: A lot of math content is written to rank well on Google, not to teach effectively. It’s optimized for clicks, not clarity.
📚🤖 Inconsistent Quality: Unlike textbooks, which go through rigorous editing, online math solutions are hit-or-miss. Even well-meaning creators can make mistakes, leaving students confused or misinformed.
Why This Matters
Let’s zoom out. What happens when students rely on tools that confuse them more than they help?
- Confidence Drops: Struggling with math already feels overwhelming. When AI or online resources fail to deliver, students internalize that failure. “Maybe I’m just bad at math,” they think, when the truth is they’ve been let down by bad tools.
- Gaps Widen: Math builds on itself. If a student doesn’t grasp algebra, they’ll struggle with calculus. Poor tools compound these gaps, making it harder for students to catch up later.
- Learning Feels Out of Reach: At its best, math is empowering—a way to solve problems and see the world differently. But when the tools aren’t built to teach, math feels inaccessible and alienating.
We can do better.
Conclusion
The future of math education doesn’t need just smarter AI. It needs AI that teaches. AI that adapts to different learners, explains clearly, and doesn’t settle for quick answers.
In the next post, we’ll explore what this kind of AI looks like—and why it has the power to transform math education for good. Stay tuned.
