This opinion piece argues that teaching goals, teacher preparation, and student needs should guide edtech use. He supports AI literacy and assessments that demonstrate problem-solving through projects, discussions, and presentations. A useful takeaway is that engagement alone does not establish whether a tool improves learning. For math teachers, that means examining whether students can explain their reasoning and apply ideas to unfamiliar problems after using it.

What I find useful about this list is the range of approaches, including animation, storytelling, and everyday examples. I could see it helping teachers find a short supplement when a lesson isn’t quite connecting with students. Being able to pause and replay an explanation helps, too. I’d choose a video around one specific learning goal and follow it with a problem students work through themselves. 

DragonBox and Khan Academy Kids are standouts on this list. I like how DragonBox lets children explore mathematical relationships through puzzles before introducing formal notation. Khan Academy is always a winner and Khan Academy Kids offers a useful starting point for younger children, with spoken directions and visual activities for counting, shapes, and early operations, which matters when a child’s reading skills are still developing. With either app, I’d pause occasionally and ask children to explain what they noticed or show how they figured something out. (Caution: Lots of ads!)

What's concerning in this article is how early the barriers begin for Black students. Limited access to Algebra I in middle school can make reaching calculus in high school much harder. Another obstacle is that schools serving large Black and Hispanic populations are less likely to offer advanced math courses at all.

I wonder how many students miss the chance to discover an interest or strength in mathematics simply because those classes aren’t available. Expanding course offerings, along with the teaching and support students need to succeed, seems like an essential place to start.

I take the reported failures to recognize children in crisis seriously, and this article gives those concerns plenty of attention. It leans toward Common Sense Media’s child-safety perspective, although Mashable also includes Google’s objections to the testing. My reservation is that the assessment used selected test prompts, so its failure rates don’t tell readers how often children encounter these problems during everyday searches. That’s a limitation, but it doesn’t make the documented failures less concerning. I see a clear emphasis on risk and I’d want stronger testing and meaningful parental controls before making snap decisions.

A humanoid robot named Sally and an AI avatar from Realbotix, according to New York Focus. The district planned to introduce the tools in high school AI and robotics classes to support teachers and students. The avatar was intended to help with assignments during and after school, including feedback on uploaded homework. District officials said they would judge the pilot’s success mainly through feedback from students and teachers. The rollout hit a pause on July 24, when the district announced it would address student data privacy agreements.

Three potential AI career paths: building systems, researching new methods, and connecting AI tools with organizational needs. Engineering emphasizes coding and reliable systems, while research requires deeper mathematical preparation. Product, strategy, and ethics roles draw on subject expertise, communication, and an understanding of AI’s limitations. The author recommends comparing actual job requirements with existing strengths before committing to training.

This is a great approach. We simply don't know enough about AI to make a decision one way or the other. Calling this a ban, as many news agencies have, is inaccurate.

New York City announced a one-year pause on student use of generative AI through eighth grade for 2026–27, according to The Verge. Mayor Mamdani questions whether AI is necessary in early education, emphasizing human relationships and opportunities for children to solve problems independently. High school students will receive AI literacy lessons and limited supervised access, while companion chatbots are banned across grades. Teachers can still use AI for lesson planning, and exceptions allow certain uses for accessibility, multilingual learners, and specialized programs.

This TeachThought article shows how a wrong answer can reveal a student’s thinking. A wrong answer can tell a teacher quite a bit about how a student is thinking. When errors are handled patiently and discussed openly, students may feel more comfortable sharing an unfinished idea, questioning an approach, and trying again.  A "work backward" problem, in which the student is given the wrong answer to a problem, is also a great way to challenge. Common error problems work because the brain can catch a math mistake before the student even realizes it. A brief electrical signal, known as error-related negativity (ERN), reflects this early recognition that something has gone wrong.

I take the concerns in this article seriously, especially the drop in motivation and the achievement losses among students whose classes had lower prior scores. Overall achievement, though, did not decline.

I’m less convinced by the suggestion that teachers relied on unedited AI materials, since the researchers did not observe lessons or evaluate those materials. Teachers in the comparison group could also use other AI tools, so the study cannot neatly separate teaching with AI from teaching without it. To me, these findings support more careful classroom testing and teacher support, while leaving open the question of which uses of AI actually improve learning.

This Tech News Weekly episode covers Apple’s device-leasing program, AI security concerns, and publicly published Claude chats appearing in Google searches. The leasing discussion offers a practical math activity: students could compare monthly payments, total costs, and whether they would own the device afterward. The privacy segment provides another teaching opportunity—examining how information shared publicly can reach unintended audiences. Together, these topics connect mathematical reasoning and digital literacy to everyday decisions.

ST Math’s 2026 Math Learning Solution of the Year award was well deserved. Its visual puzzles give students room to test an idea, see what happens, and adjust their thinking. I also appreciate the reduced reliance on text, which is fantastic for English language learners. Connecting those visual experiences to mathematical symbols afterward makes sense to me because students have an idea to build on when the notation appears.

Count the Critters and Snack Shop Stories stand out in TCEA’s collection for making early number ideas visible. Children match numerals to groups of animals or move food items to model addition and subtraction. The article recommends pairing these activities with real objects and conversation. Recreating a digital snack story with counters could help teachers see whether children understand how quantities change and can explain that thinking to a partner.

A study of elementary students in China linked abacus training with stronger arithmetic performance. Students practiced with beads before learning to visualize an abacus for mental calculations. Memory and attention advantages were clearest among fourth- and fifth-graders. However, researchers compared existing classrooms without measuring students’ skills before training began, so the study cannot establish that abacus practice caused these differences.

Considerations include different applications, such as LaTeX (a math typesetting language), MATLAB and Mathematica (advanced math systems), and coding tools like Python. Many options suit STEM majors. Everyday details like a readable screen, decent battery life, and something comfortable to carry between classes are important and can be easy to overlook.

Colleges are expanding AI courses beyond computer science to fields such as psychology, business, and music, according to Entrepreneur.

UT Austin and Northwestern are making AI classes more accessible to other majors. Eastern Mennonite University is planning a course combining music and computer science. While one student describes AI skills as a career advantage, the article offers no hiring data to support that claim.

Forbes highlights computer science, mathematics, statistics, and engineering as routes into AI careers. Choosing a degree depends on a student’s interests and intended work. Mathematics provides a theoretical foundation, though students may need programming experience to apply that knowledge.