Small-group discussions designed to help elementary students engage in conversations that promote critical analytic thinking, reasoning and deeper understanding of the content they read increased critical thinking over time for fourth- and fifth-grade students, according to a new study by a team that includes researchers from the Penn State College of Education. It’s the latest evidence in support of Quality Talk, the “deliberate approach to discussion that transforms student engagement” developed by P. Karen Murphy, associate dean for research and outreach in the Penn State College of Education.

In a paper recently published in Science Advances, researchers from the lab of University of Chicago Pritzker School of Molecular Engineering Asst. Prof. Po-Chun Hsu developed a distributed carbon nanofiber direct air capture (DAC) filter that could potentially turn every home, office, school or other building into a small carbon-capture system working toward the global problem of airborne CO2. A life-cycle analysis shows that – even after factoring extra CO2 released by everything from manufacture and transportation to maintenance and disposal – the new filter is 92.1% efficient in removing carbon dioxide from the air.

Principal Investigator Maryam Parsa, Assistant Professor of Electrical and Computer Engineering, College of Engineering and Computing (CEC), and co-Principal Investigator Giorgio Ascoli, Distinguished Professor of Bioengineering and Neuroscience, College of Science, received funding from the U.S. Department of Energy for the project: “GAINS: Generalizable, Analog, Izhikevich-Based Neuromorphic Spintronics for Next-Generation Computing.” PI-Parsa is leading this collaborative project with two other co-PIs from University of Wisconsin–Madison (Prof. Akhilesh Jaiswal) and Northwestern University (Prof. Pedram Khalili). 

Consumers with lower AI literacy are more likely to adopt AI tools because they view AI as magical and awe-inspiring.

Humans have the cognitive capacity to infer and reason about the minds and thoughts of other people. Our brains are very good at it—much better than the Large Language Models or LLMs.  Although LLMs were inspired by some concepts from neuroscience and cognitive science, they aren’t exact mimics of the human brain. Now, two scientists team up in a multi-disciplinary effort to tune the LLMs to make fewer redundant computations, so they operate closer to how a human brain does.