Mathematics

Middle and High School Students' Struggles in Math: 4 Key Takeaways

Recent NAEP results and an EdWeek Research Center survey show that middle and high school students are struggling in math, with many educators calling the problem severe or very severe. The article highlights four responses schools are using: rebuilding foundational skills like fractions, experimenting with middle-school interventions, trying AI-based personalization, and supporting English learners through confidence-building instruction.
While many school districts have been fixated in recent years on helping young students rebuild flagging math skills, there’s also a growing awareness that math learning among middle and high schoolers is badly off track.

Recent scores on the National Assessment of Educational Progress show a decline in 12th graders’ math scores extending back more than a decade .

A survey conducted by the EdWeek Research Center also offers cause for worry about older students’ experience in the subject.

The portion of middle and high school teachers and administrators who describe students’ struggles in math as severe or very severe (44% for middle school and 40% for high school) is larger than the portion of educators at the upper elementary level (34%).

EdWeek recently published a special report looking at middle and high school students’ shortcomings in math, and how to help them.

Here are four key takeaways about teenagers’ math struggles, and how schools are trying to find solutions.
1. A core group of foundational concepts—such as fractions—scuttle many older students’ progress
An EdWeek Research Center survey asked classroom educators and administrators about baseline skills that trip up students on the way to more advanced math .

Fractions ranked at the top, along with pre-algebraic skills and fluency in basic operations.

EdWeek’s special report describes a number of strategies to support students in these areas.

Senior Staff Writer Sarah Schwartz describes in her story in the report how some educators are focusing on rebuilding students’ very basic computational skills.
Data Literacy Skills Seen as Key for Success in the Age of AI

Parents and educators in a new survey say data literacy is especially important in the age of AI, but many schools are not giving students enough chances to learn it. The article says educators want data literacy integrated across subjects so students can question information, think critically, and use data to make sense of real-world issues.

Parents and educators see data literacy as an important skill—especially in the age of artificial intelligence—but feel schools do not offer enough opportunities to learn it, a new survey shows .

Data literacy is critical at this moment in time because it teaches students to question, evaluate, interpret, and communicate information effectively, said Zarek Drozda , the executive director of Data Science for Everyone , one of the organizations that conducted the survey.

In a workforce that is quickly expanding its use of artificial intelligence, data literacy is more important than ever, he added. “When it comes to AI tools, [students] have to know how to question where underlying data comes from, what might be wrong with it, and whether it’s applied correctly to the problem they’re wrestling with,” said Drozda. “That’s how we enact critical thinking in a world of AI capabilities and digital technology.”

See Also Open image caption Close image caption Students engage in an AI robotics lesson in Funda Perez’ 4th grade computer applications class at Dr. Martin Luther King, Jr. School No. 6 in Passaic, N.J., on Oct. 14, 2025. Erica S. Lee for Education Week Special Report AI Is Picking Up Speed. Are Schools Keeping Pace?

The survey by Mathematica and Data Science for Everyone—which was conducted in February and March of this year and included 1,333 parents and educators in Colorado, Louisiana, and Tennessee—found that 88% said all students should be data literate before graduating high school and 76% said AI has made learning this skill even more important.

Physics

With a feel for physics, AI models simulate a wider range of real-world scenarios

MIT researchers developed GeoPT, a new pre-training approach that helps AI models better simulate physical scenarios like crashes, waves, and wind by teaching them more physics through synthetic dynamics data. The team says the system can reach strong performance faster and with much less labeled data, which could help engineers test vehicle and robot designs more efficiently.

Artificial intelligence models are jacks of many trades, including writing, generating images, and creating 3D models. But they aren’t as helpful when it comes to testing robots or designs for vehicles in diverse environments, since they don’t understand physics as well as they do pixels or text.

To build an AI system that can reliably simulate a variety of physical scenarios, engineers need a range of physics data at a scale that isn’t yet feasible. That’s because it’s very time-consuming to get neural networks just a few data points they can understand. They rely on algorithms called “numerical solvers” to calculate physical properties at different points of a 3D shape. It’s a thorough process, but it takes so long that it limits how much data you’ll have to, say, test if your plane designs are safe and aerodynamic.

A new pre-training approach known as “GeoPT,” developed by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Tsinghua University, gives simulation models a chance to learn physics in a broader, more efficient way. It virtually reenacts everyday mechanical interactions in 3D, showing how particles stop when reaching some part of an object. These simulations give the models a sense of how physics works, helping them model the real world more accurately, reach peak performance twice as fast, and train on up to 60 percent less data compared to leading models.

Soon, the project could help engineers predict how vehicles (like cars and planes), everyday items (including chairs and containers), and robots respond to various physical elements, such as wind, water, and collisions. The researchers believe their work could also be a step toward a physics foundation model, a backbone system trained on lots of data that can help AI tools generalize to different tasks.

“We believe physics is the third modality for AI models, after text and pixels,” says MIT PhD student and CSAIL researcher Minghao Guo, a co-lead author on a paper introducing GeoPT. “Our general-purpose model has the versatility to help build a world model for physics. Many models, such as those that generate robotics data and videos, are already well-versed in textual and visual data, but with physical accuracy, they’ll get more-realistic results.”
Scientists used AI to crack one of water's biggest mysteries

Researchers at the University of Osaka used AI to compare different ways of describing the structure of supercooled water, helping identify which measures best capture key molecular changes. The study may improve understanding of why water behaves so unusually and support better tools for studying its hidden structure.

Water covers most of Earth's surface, yet it behaves in ways that set it apart from nearly every other liquid. One of its most unusual traits is that it expands instead of contracts when it freezes. Scientists have long linked these odd behaviors to changes in water's microscopic structure as temperature and pressure vary, but they have lacked a consistent way to describe and compare those structural changes.

Now, researchers at the University of Osaka have turned to artificial intelligence (AI) to tackle that challenge. Their AI system provides a unified way to compare different methods of describing the structure of supercooled water, helping identify which ones capture the most important features. The research was published in Communications Chemistry.

Why Supercooled Water Behaves So Strangely

For liquid water to become ice, its molecules must arrange themselves into an orderly crystal lattice. That process begins at a nucleation site, a surface where ice crystals can start forming. Tiny impurities in the water or even microscopic scratches inside a container can provide those starting points.

If those nucleation sites are absent, water can remain liquid even after it has been cooled below its normal freezing point. This unusual state is known as supercooled water.

Water's unusual properties become even more pronounced under these conditions. Scientists believe these behaviors are linked to a balance between two competing forms of liquid water: a high density liquid (HDL) and a low density liquid (LDL).
New AI model reveals how neutron star mergers forge heavy elements

Researchers at GSI/FAIR developed an AI-powered model called RHINE that can simulate the nuclear reactions in neutron star mergers much more efficiently than traditional methods. The work could improve understanding of how heavy elements form in the universe and make future astrophysics simulations faster and more detailed.

Researchers have developed a new artificial intelligence powered simulation that could significantly improve our understanding of how the universe creates many of its heaviest elements. Created by an international team at GSI/FAIR, the machine learning model allows scientists to simulate the complex nuclear reactions that occur during neutron star mergers and other violent stellar events far more efficiently than before. Their findings were published in the journal Physical Review D.

AI Improves Simulations of Heavy Element Formation

Many of the chemical elements found throughout the universe are forged during extreme cosmic events, including supernova explosions and neutron star mergers. These enormous explosions generate the energy needed to produce heavy atomic nuclei through a process known as rapid neutron capture, or the r-process.

During the r-process, atomic nuclei rapidly absorb free neutrons. Some of those neutrons then transform into protons, allowing the nuclei to grow larger and eventually form many of the heavy elements found in nature.

Simulating these reactions is one of the biggest challenges in nuclear astrophysics because the calculations require tremendous computing power.

"Researchers around the world strive to make these complex reactions understandable through theoretical simulations. However, modeling all parameters requires incredible computing power, which is why the models often have to be simplified," said Dr. Oliver Just, first author of the study and a researcher in the "Nuclear Astrophysics & Structure" department at GSI/FAIR. "Our new model RHINE, which uses artificial intelligence, offers an efficient alternative."

Computer Science

The Trailblazing School on the Frontier of Artificial Intelligence

Washington Leadership Academy is using AI in classrooms, assessments, and school operations to support learning and save teachers time. The article says teachers are also showing students AI’s limits, so they learn to use it critically rather than depend on it blindly.

In the Classroom

WLA teachers have deployed AI in instruction in a variety of innovative ways.

In Giani Clarkson’s AP Government class, for example, students studied imperialism by designing a country’s resources to resist being conquered by “Clarksonia,” a fictional nation Clarkson controls. They input their country into a chatbot to see whether they could survive a three-year simulated war against Clarksonia, allocating its population across soldiers, scientists, artists, and educators, and selecting a key natural resource. The bot delivered a verdict on each simulated country, revealing Clarksonia’s resources for comparison but not explaining why a country won or lost; this forced students to think through the causes themselves. “It’s not good enough to just tell them, ‘This is how it happened,’” Clarkson said. “They have to see it in real time and kick the tires themselves.”

Beyond teaching students how AI works and how to use it, WLA is exposing students to AI’s limitations.

Niyesha Coleman, the school’s math instructional coach, built a gamified chatbot that walked students through practice problems, offering hints and feedback in a voice trained to sound like hers. Students had to explain their reasoning for every answer, and sometimes, Coleman noted, the bot got the answer wrong, and students had to defend their thinking against the incorrect response. It was a lesson in both mathematical reasoning and the limits of AI.

Mathematics

Physics

Computer Science

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