Do you stare at a math word problem and feel completely stuck? You're not alone. These problems mix reading comprehension ...

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

AI systems are beginning to produce proof ideas that experts take seriously, even when final acceptance is still pending.

Engineers at the University of California San Diego have developed a new way to train artificial intelligence systems to ...

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

Frustrated by the AI industry’s claims of proving math results without offering transparency, a team of leading academics has ...

Engineers at the University of California San Diego have developed a new way to train artificial intelligence systems to solve complex problems more ...

The race is on to develop an artificial intelligence that can do pure mathematics, and top mathematicians just threw down the gauntlet with an exam of actual, unsolved problems that are relevant to ...

Print Join the Discussion View in the ACM Digital Library The mathematical reasoning performed by LLMs is fundamentally different from the rule-based symbolic methods in traditional formal reasoning.

These low-floor, high-ceiling problems support differentiation, challenging all students by encouraging flexible thinking and allowing for multiple solution paths.

You’ve checked for understanding—now you can use this framework to understand what students’ confusion is telling you, and how you can adjust course.