Experts say higher education is an industry in turmoil. The collateral damage is widespread — from hechingerreport.org by Jon Marcus
Colleges are closing at a pace equivalent to about one every three weeks, with more projected

AMHERST, Mass. — The parking lots are mostly vacant. Locked and dark, many of the buildings are lifeless but for dying plants in dusty pots. One of the few people visible is a white-bearded man on a riding mower, tending to an empty lawn as if by habit.

This is not a factory or office park abandoned by its tenants. It’s Hampshire College, a once-celebrated liberal arts school that will shut down in December. That makes it the latest in a steady stream of colleges that are closing or merging, at a rate that last year reached the equivalent of nearly one every three weeks.

“Everyone is shocked to hear that more than a quarter of our private colleges could be gone in the next decade,” said Peter Stokes, a managing director at Huron Consulting Group, which has projected that 442 of the nation’s 1,700 still-operating private, nonprofit universities and colleges are at risk of having to close or merge in the next 10 years. “We’re now talking about hundreds of other institutions being in a similar situation.”


Also see:


The State of Higher Ed 2026, in 4 Charts — from linked.com by Jeff Selingo

For nearly 60 years, higher ed had one answer to almost every strategic question: get bigger. Add programs, schools, enrollment, research labs, campuses, athletic teams, international students, online degrees.

That era is ending.

That’s the central argument running through this year’s State of Higher Education, my annual briefing for presidents, trustees, cabinets, and faculty leaders. Like last year’s inaugural deck, it borrows from Mary Meeker’s “Internet Trends”: heavy on charts, quick to scan, built for leadership teams to work through together.

The 2026 edition covers six areas: the business model, federal policy, research, AI, athletics, and governance. The question across all of them is what’s temporary and what’s structural. Each section ends with a page of opportunities for leaders.

Here are four charts from the briefing and what they tell us about what’s next…

 

AI can improve performance, but did anyone learn? — from chieflearningofficer.com by Alexandre Rodrigues
Better AI-assisted performance does not always mean better learning. Here’s what L&D leaders can do about it.

A randomized field experiment involving thousands of knowledge workers found measurable productivity benefits from generative AI, including faster document completion. But productivity and learning are not the same outcome.

As organizations integrate AI more deeply into everyday work, L&D may need to confront an uncomfortable possibility: Employees can become better at producing results without becoming proportionally better at producing those results independently. Performance is visible. Learning is harder to see.

L&D researchers have long distinguished between performance during practice and learning that persists after practice. As Nicholas Soderstrom and Robert Bjork observed, what people do successfully while training is not always a reliable indicator of what they will retain or transfer later.

That distinction becomes particularly consequential in AI-enabled workplaces.

 

We’re Waiting Too Long to Talk to Kids About Their Futures (Julie Lammers, Britebound) — from thehumanist.xyz by Julie Lammers and Allison Dulin Salisbury
“By eighth grade, kids are already ruling out careers they’ve never had a chance to explore.”

Per Allison:

Julie Lammers, who leads Britebound, makes a compelling case for starting career exploration in middle school for precisely this reason. The goal isn’t to get a 12-year-old to choose a profession, but to help them discover possibilities they might otherwise never consider, while they’re still forming their sense of who they are and what they’re capable of.

I love the implication of this argument. If we care about preserving choice, we should start career exploration earlier, not later. Done well, it could be one of the most powerful ways to expand opportunity for young people whose circumstances might otherwise define the limits of their ambition.

Julie and I talked about what this looks like in practice, why career navigation should be a skill we develop rather than only a service we deliver, and what young people themselves are telling Britebound about the support they want. It’s a conversation that challenged some of my assumptions about when and how we should prepare kids for their futures.


Also from Allison, check out:


[Special Report] AI Could Radically Accelerate How We Learn About Learning — from thehumanist.xyz by Allison Dulin Salisbury and Mandy McLean
AI is making a virtual laboratory for human learning possible, opening the door to testing ideas we could never afford or safely attempt with actual students.

Recent advances in AI raise the possibility of building something education has never really had: a virtual laboratory for learning.

At the center of this possibility are synthetic students—AI models designed to simulate aspects of how real students behave and, eventually, how they learn. Researchers could test hundreds of ways to teach a concept, give feedback, or sequence a curriculum and identify the most promising approaches for different kinds of learners. Developers could try out a new assessment or AI tutor on thousands of simulated students before putting it in front of real ones. Districts could stress-test AI products for safety and begin to understand which tools might work for whom and under what circumstances.

True, this could make running education studies faster and cheaper. But change the cost of experimentation enough and you change what gets experimented on. The promise of synthetic students is that researchers could ask more questions, test more ambitious ideas, explore edge cases, and discard failures before spending years and millions of dollars studying them with actual students.

 

The Faculty Bargain Of 2040: What Should The University And Its Faculty Owe One Another? — from forbes.com by David Rosowsky

What if we stopped reforming the existing faculty career for a moment? What if we started over?

Suppose we were designing the faculty career today for the university of 2040, starting with a blank sheet of paper. There would be no rigid departmental affiliations, ranks, tenure systems, promotion policies, or inherited assumptions. We could keep anything that still made sense. But nothing would be presumed necessary simply because it has been around for decades.

What would we build?

When machines can increasingly perform parts of the work that once consumed enormous amounts of human time, the scarce attributes may shift toward judgment, curiosity, creativity, disciplinary depth, the ability to frame consequential questions, the capacity to connect ideas across fields, and the ability to know when a machine is wrong.

That is not an argument for fewer faculty. It may be an argument for different faculty – and for a faculty employment system capable of recognizing those important (even desirable) differences.

 

 

The State of AI Report 2026 — from press.airstreet.com by Nathan Benaich
AI is building AI, physical AI, booming inference, and escalating cyber risks.

For nearly a decade, this report has been a labor of love. My interest in AI began with my PhD in cancer research and computational biology in the early 2010s. Since 2013, I’ve invested in companies building and applying AI to accelerate technological progress. The report is my way of sharing what I’m learning and helping more people understand the research shaping our world.

Every day brings new papers, model releases, and developments in industry and politics. Much of the work goes into deciding what deserves your attention, checking what the evidence supports, debating it with researchers and builders, and explaining why it matters.

 
 

Exceptional Videos of Authors Reading Their Books Aloud for Young Students — from edutopia.org by Kristin Rydholm
These 14 books can help teachers celebrate student writing by weaving literacy and social and emotional learning into the school day.

This classroom scenario is an example of how finishing a piece of writing is a dual celebration—an ending and a beginning. A published piece of writing means crossing a finish line as the culminating event within the writing process, but it is also a starting line, where a story begins a journey of its own—being read, seen, heard, and shared by others.

Who better to assist with modeling these transitional and celebratory moments than 14 professional authors of picture books?

Where to start
Featured below are links to exceptional authors and author/illustrators reading their picture books. Watching these videos, a child can see and hear the diverse faces and voices of authors who have prioritized stories about kids like them.


4 Ways to Encourage Great Questions in the Science Classroom — from edutopia.org by Katie Budrow
Here’s how a middle school teacher guides students to proactively ask and answer questions like ‘why does that happen?’

Narrow, prescribed tasks are known as convergent tasks. Define the parts of a cell has a specific answer. Sometimes, convergent tasks make the most sense. But whenever possible, I prefer to use broader divergent tasks, which allow for multiple ways to approach the learning. Describe the different ways that cell parts can work together to form a system is an example of a divergent task.

Divergent tasks inherently drive more student questions, since the answers aren’t just one-and-done. Divergence promotes “deep thinking, requiring students to analyze and evaluate concepts,” according to a study on the subject. Students are not just trying to find the “best” answer—they’re working toward a more complex concept.


A First-Week Routine That Gets Middle School Students Thinking Like Scientists — from edutopia.org by Bexaida Buzzie
Here’s a day-by-day plan for guiding your students to start the year by asking questions. Just resist the urge to answer them.

I realized I was spending so much of my time going over my classroom rules that I forgot that the students were there to learn how to learn.

Once I took the pressure off myself, everything changed. Instead of focusing on perfection, I started to focus on curiosity and fun. Instead of cramming information and reaping frustration, I slowed down.

Here’s what my first five days look like, and how they build a foundation for scientific thinking.


15 Effective Ways to Manage Dysregulated Students — from edutopia.org by Andrew Boryga
Teachers are seeing more big emotions and disruptive behavior in early elementary classrooms. These practical strategies can help youngsters reflect, reset, and repair.

While many experts cite the pandemic’s role in creating skill deficiencies, it’s only a part of the story. Educators also point to major changes in the culture around child rearing, pointing to issues like rising screen time, less play, and fewer opportunities for face-to-face interaction—“developmental needs that no digital program, however sophisticated, can fully replicate,” writes elementary school teacher Cara Zelas.

Serious behavioral issues and developmental concerns require broad support from administrators, specialists, and families. But for everyday moments that derail lessons—a child melting down, refusing directions, or struggling to rejoin the group—many teachers find that simple practices, consistently applied, can help turn big feelings into moments of growth instead of lost instructional time.

 
 

How to Design Around Cognitive Offloading — from drphilippahardman.substack.com by Dr Philippa Hardman
Three new studies shed light on when offloading to AI harms learning, when it helps — and how to design for the difference

A cluster of new studies — most published in the last few weeks in the International Journal of Educational Technology in Higher Education — lets us answer two questions with more precision than ever before:

  1. When does cognitive offloading actually happen?
  2. When does using AI improve cognition?

The answers in turn help us to start to design learning which intentionally uses AI to drive – rather than diminish – learning.
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A Writer’s Toolkit — from wondertools.substack.com by Mallary Tenore Tarpley and Jeremy Caplan
Useful tools for every stage

Per Jeremy Caplan: My friend and colleague Mallary Tenore Tarpley writes an excellent newsletter, Write at the Edge, with helpful writing tips and best practices. She’s a professor, journalist, and author who has written for The New York Times, The Washington Post, and other outlets. She recently published an important debut book, Slip: Life in the Middle of Eating Disorder Recovery.

Mallary recently interviewed me about writing tools. She wrote up a summary of our conversation, highlighting the tools I talked about. I’m turning the rest of this post over to her summary.

 

Microsoft Discovery Platform Brings Agentic AI to Scientific Research — from campustechnology.com by Chris Paoli

Key Takeaways

  • Microsoft Discovery reaches general availability, bringing agentic AI to scientific research and development workflows.
  • AI agents support hypothesis generation, experimentation, data analysis, and knowledge management at scale.
  • New Discovery app preview enables researchers to explore AI-driven scientific discovery with lower adoption barriers.
 

Flipped Classrooms and Academic Achievement — from learningscientists.org by Megan Sumeracki

There are actually many, many ways to design a flipped classroom, and it has been fascinating to learn about the ways my colleague typically structures her hybrid, flipped-classroom courses. As a result, we’ve been able to engage in really interesting conversations about the best approach for this particular course, and why. As a result of some of these discussions, I came across a few recent meta-analyses related to the effects of flipped classrooms, the results of which I thought were worth sharing here (1, 2, 3).

 

 

Anthropic, NVIDIA Move AI Agents Deeper into Scientific Workflows — from campustechnology.com by John K. Waters

Key Takeaways

  • Anthropic’s Claude Science beta and NVIDIA’s BioNeMo Agent Toolkit show AI agents moving beyond general productivity into specialized scientific research workflows.
  • The companies are emphasizing auditability and reproducibility, including preserved code, environments, message history, reviewer agents, and integration with existing research tools.
  • For enterprises and research teams, the key test will be whether agentic AI can produce traceable, reliable results while keeping humans in control of sensitive data, compute, and scientific judgment.
 

Scan More than 60 Million Stars in the Most Detailed Photo of the Milky Way Ever Taken — from thisiscolossal.com by Kate Mothes and The European Space Agency (ESA)

In March 2025, the Euclid mission led by the The European Space Agency (ESA) enabled scientists to capture the highest resolution image ever taken of the dense, glowing center of the Milky Way galaxy.
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Stanford Online Launches Immersive Learning Studio — from campustechnology.com by Matt Jones

Key Takeaways

  • Stanford Online celebrated its 30th anniversary by launching a new immersive learning studio that combines VR, AR, and AI technologies to create more engaging and personalized educational experiences.
  • The studio provides faculty with advanced production tools — including a 4K LED wall, cinematic cameras, AI-enabled workflows, and extensive editing and storage infrastructure — to develop innovative learning content at scale.
  • University leaders see the studio as a major step toward expanding faculty-led, research-based education globally, leveraging AI and immersive technologies to reach learners in ways previously not possible.
 
© 2025 | Daniel Christian