In an MIT classroom, a professor lectures while students diligently write down notes they will reread later to study and internalize key information ahead of an exam.

What can we learn about human intelligence by studying how machines “think?” Can we better understand ourselves if we better understand the artificial intelligence systems that are becoming a more significant part of our everyday lives?

Hal Abelson, MIT Class of 1922 Professor and CSAIL principal investigator, has received the 2025 Lifetime Achievement Award for Excellence from Open Education Global for helping make information technology more accessible worldwide. “Hal Abelson’s work promotes knowledge of all forms as a public good,” notes the organization in a public statement. “Hal’s work has focused on communities working together to advance and support knowledge.”

The degree to which someone trusts the information depicted in a chart can depend on their assumptions about who made the data visualization, according to a pair of studies by MIT researchers.

In the months leading up to the 2024 U.S. presidential election, a team of researchers at MIT CSAIL, MIT Sloan, MIT LIDS, set out to answer a question no one had fully explored: how do large language models (LLMs) respond to questions about the election? Over four months, from July through November, the team ran nearly daily queries across 12 state-of-the-art models on more than 12,000 carefully constructed prompts, generating a dataset with over 16 million responses from LLMs, to help answer this question.