Is it a good idea to use artificial intelligence (AI) for math help? Yes — and no. That’s based on the results of a new survey. AI can be a great tutor, it finds, but only if you use it in certain ways. 

The data come from a poll of 1,300 high school students from across the United States, England and Wales. The Society for Industrial and Applied Mathematics surveyed some top math students in each nation. The goal was to identify the habits behind their academic success. 

AI has many strengths as a math tutor, the study found. Students can use AI to explain things in different ways and to check their homework answers. It can answer “why” questions about material to deepen their understanding. AI can tailor learning by identifying a student’s knowledge gaps. Using AI as a tutor could even reduce math anxiety, some students said.

But there are potential downsides to AI tutoring as well, notes Robbie Torney. AI was designed to reduce friction — meaning it aims to do things quickly and efficiently. But learning “requires friction,” says Torney, who was not involved in the survey. He heads up AI & Digital Assessments at Common Sense Media. It’s a nonprofit group working to make the digital world safer for children and teens.

Science shows that learning requires grappling with problems, Torney notes. You have to work through things that feel hard and come up with your own answers.

Tiago C.P. agrees. To this Colorado high-school senior (who did not take part in the survey), the most important thing is “trying the problem first.” This is where students struggle, he adds, but “that’s when you get the learning down.” Says Tiago, “I won’t use AI before I’ve fully attempted a problem.” 

Survey says

The teens in the study had all taken part in an international math contest. Afterward, they gave their take on whether AI could be an effective math tutor. About half said yes — if the AI was used in specific ways. More than 200 math teachers also took part. More than six in every 10 agreed that an AI tutor could be beneficial. But AI could also decrease learning if used improperly, they noted.

Almost two thirds of students surveyed (63 percent) said they would always try a math problem on their own before seeking help from AI. If they were struggling, where would the students turn? Most recommended first asking teachers (58 percent), followed by classmates (50 percent). Others said they’d continue to try on their own (48 percent) or look for tutorials on the internet (46 percent). At 39 percent, AI came in fifth place.

That suggests using AI after trying most other sources of help. The number one suggestion for how to do well in math was doing regular practice problems. That’s according to both students (67 percent) and teachers (62 percent).

Overall, about half of the students said they believe AI has a place in math education. But they added that it is best used along with — not in place of — help from their classroom teacher. For one thing, “It’s hard to get AI to teach you the same way your teacher would,” says Amelie M., another Colorado senior (who did not participate in the survey).

How to use AI for homework wisely

More than half of the teachers in the study (55 percent) said AI’s biggest benefit is its ability to explain — step by step — how to solve a problem.

But relying on it can harm learning in the long run. Almost three in every four teachers surveyed said their biggest concern was that AI can quickly become a crutch.

Amanda Brown teaches math at Broomfield High School in Colorado. When learning something new, you can use AI for help, she says. But as you become more familiar with the subject, studies show, you should turn to AI less and less. 

Torney recommends asking yourself (or even the AI): “Who’s doing most of the work?” Whenever it’s AI, he says, that likely points to a problem.

“I see kids that have perfect homework scores,” Brown says, who later “don’t pass their test.” AI might have helped them ace the homework. But in the end, Brown notes, “You’re gonna have to take the test [without AI].” And that’s when it comes out: Do you know the material or not?

The trick is using AI in moderation, agrees Amelie. Try to ask it very specific questions, she says, such as: “What are different ways I could solve this?” Amelie also suggests adding to your prompt: “Don’t give me the answer.” 

Tiago warns that you also need to check whether AI is correct. Although chatbots keep improving, they sometimes “hallucinate” (make errors). This is because AI doesn’t actually know the answer. It just predicts what is the most likely answer from the data it has ingested. “At least a quarter of the time,” Tiago says, “I can catch a mistake it’s made.”

Remember that you can also work on problems with classmates. And teachers are likely your best source for homework help. “Learning has always been a social task,” Torney points out — “something that we do with other people.”

Beyond homework

AI can be a good helper as you prepare for tests, Tiago finds. It can generate sample problems, create flash cards or drill you on concepts. Or if you were out sick for a few days, AI might help you catch up on topics you missed.

But AI will “pull from so many things,” says Amelie, that it can return “long, long paragraphs” of info. This “might actually be more overwhelming” than helpful, she points out. What might prove more beneficial are YouTube instructional videos that AI references, she says. Such videos show someone actually teaching you.

A math hack: You can also ask AI to tell you about your own thinking and learning. The surveyed teachers suggested letting AI test you to find gaps in your knowledge. Students recommended asking AI “why” questions about math. These could make you a better learner.

The long game

Math is a bit like learning a sport. You have to learn certain skills and in a certain order. Consider ice hockey. Until you learn how to skate — and how to stop — you’ll have a tough time mastering a slap shot.

In math, if you don’t learn factoring, you’ll have trouble in algebra. If you don’t learn algebra well, calculus will be a bear. This is why Torney advises that you “keep your eye on the long game.”

And learning takes work. “When it’s easy,” Torney says, “learning is probably not happening.” If you’re using AI for help with math and don’t feel like you’re working very hard, change how or where you get help.

Torney offers a last tip: Choose your AI platform wisely. Chatbots such as ChatGPT, Gemini, Claude and Copilot are “multi-use” systems. They can tackle any topic — not just math. Platforms such as Mathos and Wolfram|Alpha answer questions just on math. And the discontinued (but still available) Khanmigo focuses on education.

Some students who use multi-use systems for math can start relying on them for social advice or emotional support, too. This can lead to dependency in some people. That’s why systems focused on a single topic, Torney says, “tend to be less risky for kids and teens.”

Using AI to help you understand math better can be a great thing. Just keep in mind this mantra from the teacher survey: “It’s critical for students to arrive at the correct answers themselves.” Use AI to learn concepts, not get answers.

For the first time since 2021, fresh data from Google gives us a look at how we’re using emoji and there’s a major shakeup at the very top: 😂 and ❤️ are no longer our most used emoji. I know what you’re thinking. “Jennifer, is it because the world is collapsing? Do we live joyless lives now? Is love … dead??” No, sweet child. Tears of Joy (😂) didn’t drop out of first place because people stopped laughing. We just crave more drama. We collapse from laughter. We weep from laughter. We scream from laughter.

Number 1 Emoji Over Time:
2021 😂→2024 🤣→2025 😭

Loudly Crying Face (😭) took the top spot because it became the default reaction for when something is so funny, embarrassing, or cute that you can't cope. It's three in one. It’s the KenTacoHut of emoji. When people do want a traditional lol, they lean into the physical chaos, keeping rofl 🤣 at #2.

Y R U SO MAD 🤬

For years, we used smiley faces to make it very clear that I am friendly and not threatening :))))) Sometimes plain text lacks nuance and I really wanna make sure you like me!!!! That politeness still exists but unvarnished frustration has officially broken through. Middle Finger (🖕) jumped from #113 in 2021 to #17 in 2025 (!!) Enraged Face (😡) broke into the top 15 (!!!!!!) and Face with Symbols on Mouth (🤬) was ranked #120 and is now #26 (???!!!!!!)

Jumping nearly 100 spots into the global top twenty isn’t a gentle trend line; Once you crack the top twenty, the sheer volume of taps is astronomical. It’s a dead giveaway that our digital manners have changed: in addition to cushioning our messages we also throw heat. It's the same hyperbolic shift that pushed 😭 to #1.

Highs and Lows 👑

When you scroll through your emoji keyboard looking for the perfect expression, you’ll find nearly 4,000 tiny drawings: an amphora 🏺, a card index 📇, a roller coaster 🎢, and a dozen trains 🚂 🚃 🚄 🚅 🚆 🚇 🚈 🚊 🚋 🚞 🚝 🚟 🛤️. And yet … out of our massive emoji catalog just ten (that’s right, one-zero 10) account for over one third of all global emoji shares: 😭🤣😂❤️🙏😘🥰👍😍😁. How can this be?

Contrary to popular belief, aerial tramway (🚡) isn’t the least-used emoji. That title belongs to Japanese “here” button (🈁). But like aerial tramway, without metaphorical flexibility, thousands of literal objects sit untouched. Over 55% of the entire library gets near-zero use 😲

(Note: This data was collected in 2024/2025 and only includes emoji available for at least a year, giving the new emoji enough time to reach everyone's devices.)

This data sheds light on a fascinating tension: our keyboards present emoji as a visible encyclopedia, but in practice we use them for the invisible stuff like sarcasm, tone, and emotional shorthand. Faces and gestures across the board account for over 80% of all global volume. Staples like ❤️ and 🙏 remain frequently used because they’re the digital version of warm gratitude or eye contact. Basic human stuff.

The Reality of Encoding a Living Language

Once inducted into the Unicode Standard, an emoji is never removed. It’s there forever. Forever ever. But, as the data shows, people naturally gravitate around a flexible core, leaving a wide long tail of highly specific concepts that collect dust 😬 That leaves digital standards with a real dilemma: how to support rich expression without running up against keyboard limits, search discoverability, accessibility, and font architecture. If we don’t continue to explore and understand how people actually talk, we’ll just end up creating thousands of new emoji that nobody uses.

What I love about this data is the reminder that language thrives when we learn the rules then break the rules to make them our own. Lately, I’ve taken to using the Fountain (⛲️) whenever I’m overflowing with energy or feeling bonita. So go ahead, give that emoji picker a twirl, dig into the corners you rarely visit, and find a lonely emoji and make it yours ⛲⛲⛲

Methodology

Emoji use data was collected with Google’s Federated Analytics, capturing anonymized, aggregated usage in Gboard. The collection periods spanned November 2024 to January 2025 and November 2025 to January 2026. Special thanks to Alex, Wilder, and the Gboard team for making this analysis possible. 🗿📊

[2:50 AM on a Sunday is also a particularly scammy time for this to be sent.]

This past Sunday, I received an email from “Do Not Reply” with the subject line “Vehicle Registration Renewal Reminder”. Here’s what it looked like:

This is a very, very bad email, beginning with the awful A.I. slop image at the top. Here’s a larger look at just that:

Lest you have any doubts whatsoever as to the origin of this image, note that they didn’t even crop out the Gemini logo from the bottom right. While I’ve compressed the image above, the original of this needless image was also a needlessly large 760 KB png file. Awful.

When I saw this email, I was quite certain it was a phishing scam. I assumed that the link inside would take me to a scammy replica of a government website, one that would steal credit card numbers. But no! As far as I can determine, this is a legitimate email from the RMV.¹ The lone link in it does indeed go to http://mass.gov/rmv, which forwards on to the official RMV site.² And indeed, my car registration is expiring in a couple of months.

In the past, I have always received an official form in the mail which can be used to renew my registration. For many years, that letter has simply prompted me to go online to renew. Given that, a switch to email notifications make sense. They sure ought to look a lot more official than this, however.

Footnotes:

1. What’s most often known as the “Department of Motor Vehicles” (DMV) is indeed called the “Registry of Motor Vehicles” (RMV) in Massachusetts. Why? Just to be difficult, I assume. But we’re far from the only ones. ↩︎

2. I’m not sure why the original link is http:// rather than https://, but the forward does lead to a secure page. ↩︎

With the numbers 1 through 9, mathematician Ben Sparks introduces the 15-game, a two-player strategy game where the goal is to be the first to choose three numbers that add up to 15. From Sparks in this episode of Numberphile, Brady Haran’s long-running video project about numbers and mathematics:  “If...

The Kid Should See This
The 15-Game and the power of mathematical visualization

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Yesterday in London, Jason Arday, the controversy-plagued British academic and author, was found dead. Made famous as the youngest Black professor in Cambridge’s more than 800-year history, Arday had been dogged by weeks of scrutiny amid accusations of plagiarism, fake qualifications, and a fabricated backstory filled with hard-to-believe details.

He claimed to have been mute until age 11 and illiterate until 18, disadvantages that did not deter him from getting a Ph.D., which he earned by age 30. He claimed, too, that he had suffered from various medical conditions throughout his life, including autism, epilepsy, locked-in syndrome, testicular cancer, and a brain tumor. He was, by his account, a world-class endurance athlete who had racked up a variety of impressive feats, including running 600 miles in six days. Then there were his academic accomplishments: Arday was billed as a once-in-a-generation talent, whose research in his field—the sociology of education—was “the best in the world,” according to Hilary Cremin, the head of Cambridge’s faculty of education. He claimed to have received a book advance of 1.4 million in unspecified currency for his memoir, released this week by Simon & Schuster. It was a meteoric rise, until it wasn’t.

Last month, Nathan Cofnas—a former Cambridge researcher who may politely be described as a philosopher with a special interest in race and IQ, and who may impolitely be described as a professional racist—published in his newsletter what appeared to be significant evidence of Arday’s plagiarism. This month, as it became apparent that much of the Arday legend was most likely just that, the academic became an object of international obsession, especially in British media and on X. He resigned from Cambridge last week, though this did not stem the flood of attention.

The online hive mind, in particular, was brutal until the moment of Arday’s death, at which point the collective swarm did an about-face. The same platform that loosed an algorithmically accelerated torrent of abuse is now dominated by kind remembrances and mawkishness. I find the whole pageantry grotesque.

What I propose is that the public give Arday a courtesy in death that he was often denied in life: Let’s treat him like a person. Not like Cambridge’s magical negro, or like Simon & Schuster’s golden goose, or like the fetish object of internet racists, but like a flawed human being who should still be alive but is not. That requires being honest about who Arday was, not despite his tragic death but because of it. After all, it was systemic, ritualized dishonesty—his own and others’—that undid him.

[Read: Icarus in the faculty lounge]

Here is the truth about Jason Arday: A preponderance of evidence suggests that he was a serial fabulist and unrepentant plagiarist who successfully conned a British academic aristocracy so blinded by do-gooder racial neurosis that it was unable to spot lies that would have been obvious to any Joe Schmo. (Arday denied intentional misconduct and suggested that his apparent plagiarism was a result of his autism, which he said meant that he tended to learn through imitation.) As evidence of those seeming lies mounted, the powerful institutions that had made him continued to back him, doubling down and raising the stakes. Simon & Schuster stood by its author—or perhaps more accurately, stood by its product. Even as the evidence of fraud became all but undeniable, the publisher did not merely declare that it would press forward with Arday’s book; it had the audacity to tout “the professionalism and integrity he has brought to every stage of the publication.”

Then those on the racist right—frothing with glee that they took no trouble to conceal—pressed their advantage, punch-drunk on the double intoxicant of incandescent liberal hypocrisy and a Black man’s failure. The mob swelled, online and in the media. Some people joined out of perfectly legitimate indignation at the apparent obliteration of academic standards that the ordeal attested to. Some joined out of ghoulish pleasure at seeing a beneficiary of DEI, as they cast him, cut down to size.

Everyone following this story would do well to engage in some reflection. I will confess that, until yesterday, I had watched the Arday saga unfold with some combination of amusement and distress. Amusement because of the undeniable comic absurdity to the revelation that the best and brightest minds at Cambridge had been so easily deceived by a man who claimed to have run nine marathons in about as many days on a fractured fibula, and who maintained that his future had been predicted by a Brazilian shaman. Distress because I could relate to some of the things that Arday might have experienced.

[From the September 2026 issue: Why I quit the tenure track]

I have written, in this magazine and elsewhere, about what it is like to be the beneficiary of affirmative action in elite spaces. I have seen, firsthand, the way a specific kind of white progressive treats Black men who have overcome obstacles. They cannot conceal a certain blend of pity and glee when they learn, for example, that my biological father left before I was old enough to walk, or that I was raised for a time by a single mother. It is a look that says, How far you’ve come, and how kind I am for helping you go further. I have always found such encounters stomach-churning. But I can also imagine—if you came up hard and are unaccustomed to kindness—how good it might feel, and how tempting it might be, to do and say things that make people give you that look again and again, whether or not the things you’re saying are strictly true.

Two images have rattled around in my head since the news of Arday’s death. The first is from a 2023 BBC clip celebrating his hiring at Cambridge. Cremin, Arday’s Cambridge colleague, clasps both of his hands in the manner of a cherished loved one as they stare into each other’s eyes. The camera zooms in on their white and Black hands interlocked and lingers there. The second image was posted on X last night: a screenshot of a list of article headlines about the Arday scandal, stretching to several pages, published by one British outlet alone. It represents but a few watts of the international spotlight that had been trained on the academic.

I have been dwelling on these two images—the theatrical interracial handclasp; the pages and pages of monomaniacal media coverage of what was ultimately a niche academic scandal—because together they speak to a terrible reality. Arday was fetishized in his ascendancy by white liberals, for whom he served as a mirror in which they could see their own goodness. And he was fetishized in his downfall by white conservatives who saw him as a test case for their own obsessions with an academic apparatus that they believe is not meritocratic enough—which is to say, not white enough.

The right would have us believe that the Arday scandal is a story of affirmative action taken to its most caricaturish conclusion. Many progressives see it as something else: a racist witch hunt in which self-righteous zealots with suspect motives hounded a man to death. The problem with these narratives—and it is a sticky problem—is that they are both right.

To be a Black person in elite institutions, and particularly in elite academia, is to have the standards simultaneously set too low and too high: too low because only a Black person with Arday’s dubious qualifications and seemingly invented biography could have been elevated to the top of the mountain in academia and publishing; and too high because only a Black person could have been pushed off that mountain with such nakedly racist enthusiasm, his tumbling fall so ruthlessly documented, analyzed, and cheered. When a famous white man errs, people tend to rightly understand his mistakes or transgressions as an individual’s missteps. When a famous Black man does, the errors are typically turned into spectacle, characterized as proof of the inferiority, suspected all along, of his race.

Around the same time yesterday afternoon that I learned of Arday’s fate, I came across another piece of news: an announcement that Ross Barkan, a white novelist and journalist, had just been dropped by New York magazine after the publication—having investigated allegations regarding dozens of instances of plagiarism in Barkan’s writing—concluded that his work “did not live up to our editorial standards.” Swiftly afterward, Barkan announced that he would now focus on writing a new column at The Nation, another venerable outlet, where he will get a very public second chance.

It is impossible to know what is in someone’s head or heart in their final moments. I have to imagine, though, that Arday understood well enough the world he had entered and how its rules tend to work: When you are Black, some people will open the door for you far wider than they should. And when that door abruptly comes swinging shut, you will be offered no second chances.

But then users start to report a weird bug. It's the 4th time your team has been trying to fix it. I mean... asking AI to fix it. Unfortunately, it seems like not even Fable can figure it out.

You go talk to the person who worked on this feature.

"So where does the data come from?"

"Hmm... actually I don't know. Let me ask Claude."

You sit next to each other watching an endless wall of text appear on the screen. Neither of you has any idea whether any of it is true but Claude seems very confident. [...]

This project has become so convoluted, with so many layers and services, that no one on your team could possibly start to understand what's going on.

— Florian Herrengt, AI is removing the middle class of software engineering

Tags: ai-misuse, cognitive-debt, generative-ai, ai, llms, ai-assisted-programming