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.

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

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

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

Talking to yourself feels deeply human. Inner speech helps you plan, reflect, and solve problems without saying a word.

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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.