Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks.

Axiom says its AI found solutions to several long-standing math problems, a sign of the technology’s steadily advancing reasoning capabilities.

Four simple strategies—beginning with an image, previewing vocabulary, omitting the numbers, and offering number sets—can have a big impact on learning.

Abstract: The “Automated Math Equation Recognition and Problem Solving with Computer Vision” research work is to develop a framework that utilizes computer vision methods to consequently recognize ...

These student-constructed problems foster collaboration, communication, and a sense of ownership over learning.

An analysis of data from 200,000 students using a computer-assisted math program supports an optimistic view of skill-focused ...

OpenAI’s GPT-5.2 Pro does better at solving sophisticated math problems than older versions of the company’s top large ...

Abstract: This complete work on innovative practices, presents the proposal of a hybrid model between Problem-Based Learning (PBL) and the SCRUM agile project management framework, which seeks to ...

Some math problems are designed in ways that reward simplicity rather than analytical depth. Research shows that highly intelligent individuals are more likely to overthink these problems, leading to ...