#next-generation-science-standards

#next-generation-science-standards

The first event is a roundtable on "Zhuangzi: Fate, Desires, Transformation" on April 6th at 9:00am Beijing time.

Modern scientific societies are increasingly vulnerable due to their dependence on membership fees and journal subscriptions, which are being challenged by the rise of virtual networking and open-access publishing.

Computer programs that check mathematical arguments have existed for decades, but translating a human-written proof into the strict programming language of a computer is extremely time-consuming, often taking months or even years.

The core of the argument is that agentic AI will replace human labor in most white-collar industries and will do so with dizzying speed. The consequent abrupt and massive job displacements will lead to crashes in property values and local tax bases, with devastating impacts on communities and much of the public sector.

Bias risks: AI can amplify inequalities, like mislabeling non-native English writing as AI-generated. Privacy concerns: Schools face rising cyberattacks, and data misuse risks are high. Accountability: Human oversight is crucial to prevent over-reliance on AI.

What happened over the last year [was] a rupture. We're not going back. It's not possible. Too much damage has been done. Too much has changed,

Collective learning is how a group or system creates, improves, and keeps knowledge. This knowledge lasts beyond any one person or cohort. That is the most practical collective learning definition, because it shifts the focus away from individuals and toward the learning system itself.

This is a striking decision at a moment when public confidence in higher education is eroding. It is also puzzling because rigorous research and evaluation have demonstrated, over and over, the value of the work of centers for teaching and learning, including positive impacts on student learning outcomes, institutional effectiveness and faculty development.

For nearly 100 years, the United States has been the world's leader in a wide variety of scientific fields. No other country has: invested as much in fundamental scientific research, has made more scientific breakthroughs and scientific advances, has attracted more scientific researchers to move there to conduct their research, or has conducted more projects and been home to more scientists that have won Nobel Prizes.

When we look more closely at how and why organizations actually invest in these systems, we can see that the popularity of adaptive learning has far less to do with pedagogical ambition and far more to do with operational pressure. Understanding this gap between how adaptive learning is marketed and how it is used in practice is critical for organizations trying to decide whether it is the right approach for their learning needs.

We don't need more courses. We need better ones. Everywhere I look, someone is launching a "Learn Figma in 5 Days" crash course or a "Top 10 AI Hacks for Beginners" tutorial. And don't get me wrong - those courses aren't useless. They scratch an itch, they help you pick up a tool, and sometimes they even get you to a quick win.