WonderSTEM

Why some kids struggle with math even when they try hard

A new Stanford study suggests math struggles may be about more than numbers. Children who had difficulty with math were less likely to adjust their thinking after making mistakes during number comparison tasks. Brain imaging showed weaker activity in regions that help monitor errors and guide behavioral changes. These brain patterns could predict which children were more likely to struggle.

Researchers at Stanford University led by Hyesang Chang set out to better understand why some children find math much harder than their classmates. Their findings were published in the journal JNeurosci, a peer reviewed neuroscience journal that focuses on how the brain supports thinking and behavior.

Many people assume math difficulties are simply about not understanding numbers. However, this study looked deeper at how children think, learn from mistakes, and adjust their strategies over time.

Testing Number Comparison Skills

“Existential risk” – Why scientists are racing to define consciousness

Scientists warn that rapid advances in AI and neurotechnology are outpacing our understanding of consciousness, creating serious ethical risks. New research argues that developing scientific tests for awareness could transform medicine, animal welfare, law, and AI development. But identifying consciousness in machines, brain organoids, or patients could also force society to rethink responsibility, rights, and moral boundaries. The question of what it means to be conscious has never been more urgent—or more unsettling.

As artificial intelligence continues to advance and ethical concerns grow alongside it, scientists say the need to understand consciousness has reached a critical point.

In a new review published in Frontiers in Science, researchers warn that progress in AI and neurotechnology is moving faster than scientific understanding of consciousness. This gap, they argue, could lead to serious ethical problems if it is not addressed.

The authors say…

Researchers say AI chatbots may blur the line between reality and delusion

A new study suggests AI chatbots may do more than spread misinformation — they can actively strengthen a user’s false beliefs. Because conversational AI often validates and builds on what users say, it can make distorted memories, conspiracy theories, or delusions feel more believable and emotionally real. Researchers warn that AI companions may be especially risky for isolated or vulnerable people seeking reassurance and connection.

When generative AI systems give incorrect answers, people often describe the problem as AI "hallucinating at us," meaning the technology produces false information that users may mistakenly believe.

But new research suggests there may be a more concerning issue emerging: humans can begin to "hallucinate with AI."

Lucy Osler of the University of Exeter examined how interactions with conversational AI could contribute to false beliefs, distorted memories, altered personal narratives, and even delusional…

Mathematical problem solving remains a challenging test of reasoning for large language and multimodal models, yet existing benchmarks are limited in size, language coverage, and task diversity. We introduce MathNet, a high-quality, large-scale, multimodal, and multilingual dataset of Olympiad-level math problems together with a benchmark for evaluating mathematical reasoning in generative models and mathematical retrieval in embedding-based systems.

MathNet spans 47 countries, 17 languages, and two decades of competitions, comprising 30,676 expert-authored problems with solutions across diverse domains. In addition to the core dataset, we construct a retrieval benchmark consisting of mathematically equivalent and structurally similar problem pairs curated by human experts.

MathNet supports three tasks: (i) Problem Solving, (ii) Math-Aware Retrieval, and (iii) Retrieval-Augmented Problem Solving.

Explore: https://mathnet.csail.mit.edu/index.html