The Campus AI Crisis — by Jeffrey Selingo; via Ryan Craig Young graduates can’t find jobs. Colleges know they have to do something. But what?
Only now are colleges realizing that the implications of AI are much greater and are already outrunning their institutional ability to respond. As schools struggle to update their curricula and classroom policies, they also confront a deeper problem: the suddenly enormous gap between what they say a degree is for and what the labor market now demands.In that mismatch, students are left to absorb the risk. Alina McMahon and millions of other Gen-Zers like her are caught in a muddled in-between moment: colleges only just beginning to think about how to adapt and redefine their mission in the post-AI world, and a job market that’s changing much, much faster.
“Colleges and universities face an existential issue before them,” said Ryan Craig, author of Apprentice Nation and managing director of a firm that invests in new educational models. “They need to figure out how to integrate relevant, in-field, and hopefully paid work experience for every student, and hopefully multiple experiences before they graduate.”
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Kling 3.0: Everyone a Director
Kling just dropped version 3.0, and it’s a legitimate leap forward for AI video production (Kling is the GOAT). After spending early access time testing the new capabilities, I can confirm this is the most significant update to video generation tools I’ve seen in months.
Jim VandeHei’s note to his kids: Blunt AI talk — from axios.com by CEO Jim VandeHei Axios CEO Jim VandeHei wrote this note to his wife, Autumn, and their three kids. She suggested sharing it more broadly since so many families are wrestling with how to think and talk about AI. So here it is …
Dear Family: I want to put to words what I’m hearing, seeing, thinking and writing about AI.
Simply put, I’m now certain it will upend your work and life in ways more profound than the internet or possibly electricity. This will hit in months, not years.
The changes will be fast, wide, radical, disorienting and scary. No one will avoid its reach.
I’m not trying to frighten you. And I know your opinions range from wonderment to worry. That’s natural and OK. Our species isn’t wired for change of this speed or scale.
My conversations with the CEOs and builders of these LLMs, as well as my own deep experimentation with AI, have shaken and stirred me in ways I never imagined.
All of you must figure out how to master AI for any specific job or internship you hold or take. You’d be jeopardizing your future careers by not figuring out how to use AI to amplify and improve your work. You’d be wise to replace social media scrolling with LLM testing.
Some gatherings change not just in size, but in meaning. What started as a small, intentional space to celebrate partners has grown into a moment that reflects how an entire ecosystem has matured. Each year, the room fills with more leaders, more relationships, and more shared language about what learning can look like when people are genuinely connected. It is less about an event on the calendar and more about what it represents: an education community that knows each other, trusts each other, and keeps showing up.
That kind of connection did not happen by accident. Through efforts like Get on the Bus, hosted by the Ewing Marion Kauffman Foundation, networking for education leaders has shifted from transactional to relational. Students lead. Stories anchor the work. Conversations happen across tables, sectors, and roles. System leaders, intermediaries, industry partners, and civic organizations are not passing business cards. They are building shared understanding and social capital that lasts long after the room clears.
This week’s newsletter carries that same energy. You will find examples of learning that travels beyond buildings, leadership conversations grounded in real tensions, and models that reflect what becomes possible when ecosystems are aligned. When people feel connected to one another and to a common purpose, the work gets clearer, stronger, and more human. That sense of belonging is not just powerful. It is foundational to what comes next.
As we enter 2026, the Getting Smart team is diving deep into the convergence of human potential and technological opportunity. Our annual Town Hall isn’t just a forecast—it’s a roadmap for the year ahead. We will explore how human-centered AI is reshaping pedagogy, the power of participation, and the new realities of educational leadership. Join us as we define the new dispositions for future-ready educators and discover how to build meaningful, personalized pathways for every student.
What if the biggest change in education isn’t a new app… but the end of the university monopoly on credibility?
Jensen Huang has framed AI as a platform shift—an industrial revolution that turns intelligence into infrastructure. And when intelligence becomes cheap, personal, and always available, education stops being a place you go… and becomes a system that follows you. The question isn’t whether universities will disappear. The question is whether the old model—high cost, slow updates, one-size-fits-all—can survive a world where every student can have a private tutor, a lab partner, and a curriculum designer on demand.
This video explores what AI has in store for education—and why traditional universities may need to reinvent themselves fast.
In this video you’ll discover:
How AI tutors could deliver personalized learning at scale
Why credentials may shift from “degrees” to proof-of-skill portfolios
What happens when the “middle” of studying becomes automated
How universities could evolve: research hubs, networks, and high-trust credentialing
The risks: cheating, dependency, bias, and widening inequality
The 3 skills that become priceless when information is everywhere: judgment, curiosity, and responsibility
From DSC:
There appears to be another, similar video, but with a different date and length of the video. So I’m including this other recording as well here:
What if universities don’t “disappear”… but lose their monopoly on learning, credentials, and opportunity?
AI is turning education into something radically different: personal, instant, adaptive, and always available. When every student can have a 24/7 tutor, a writing coach, a coding partner, and a study plan designed specifically for them, the old model—one professor, one curriculum, one pace for everyone—starts to look outdated. And the biggest disruption isn’t the classroom. It’s the credential. Because in an AI world, proof of skill can become more valuable than a piece of paper.
This video explores the end of universities as we know them: what AI is bringing, what will break, what will survive, and what replaces the traditional path.
In this video you’ll discover:
Why AI tutoring could outperform one-size-fits-all lectures
How “degrees” may shift into skill proof: portfolios, projects, and verified competency
What happens when the “middle” of studying becomes automated
How universities may evolve: research hubs, networks, high-trust credentialing
The dark side: cheating, dependency, inequality, and biased evaluation
The new advantage: judgment, creativity, and responsibility in a world of instant answers
I’m a firm believer in conferences. This isn’t just because I have chaired the Learning Technologies Conference in London since 2000. It’s because they are invaluable in sustaining our community. So many in Learning and Development work alone or in small teams, that building and maintaining personal contacts is crucial.For a number of years, I have kept a personal list of the Learning and Development conferences running internationally. This year, I thought it would be helpful to share it.
Penelope Adams Moon suggested that instead [of] framing a workshop around “How can we integrate AI into the work of teaching?” we should ask “Given what we know about learning, how might AI be useful?” I love that reframing, and I think it connects to the students’ requests for more AI knowhow. Students have a lot of options for learning: working with their instructor, collaborating with peers, surfing YouTube for explainer videos, university-provided social annotation platforms, and, yes, using AI as a kind of tutor. I think our job (collectively) isn’t just to teach students how to use AI (as they’re requesting) but also to help them figure out when and how AI is helpful for their learning. That’s highly dependent on the student and the learning task! I wrote about this kind of metacognition on my blog.
In the same way, when I approach any kind of educational technology, I’m looking for tools that can be responsive to my pedagogical aims. The pedagogy should drive the technology use, not the other way around.
There were 15% fewer entry-level and internship job postings in 2025 than the year before, according to Handshake, a job-search platform popular with college students; meanwhile, applications per posting rose 26%.
How much AI is to blame for the fragile entry-level job market is unclear. Several research studies show AI is hitting young college-educated workers disproportionately, but broader economic forces are part of the story, too.
As Christine Y. Cruzvergara, Handshake’s chief education strategy officer, told me, AI isn’t “taking” jobs so much as employers are “choosing” to replace parts of jobs with automation rather than redesign roles around workers. “They’re replacing people instead of enabling their workforce,” she said.
Today’s graduates are stuck in an in-between moment. Many started college before AI mattered and graduated into a labor market reshaped almost overnight, where entry-level roles are disappearing faster than students can adapt.
The real story isn’t what AI can produce — it’s how it changes the decisions we make at every stage of instructional design.
After working with thousands of instructional designers on my bootcamp, I’ve learned something counterintuitive: the best teams aren’t the ones with the fanciest AI tools — they’re the ones who know when to use which mode—and when to use none at all.
Once you recognise that, you start to see instructional design differently — not as a linear process, but as a series of decision loops where AI plays distinct roles.
In this post, I show you the 3 modes of AI that actually matter in instructional design — and map them across every phase of ADDIE so you know exactly when to let AI run, and when to slow down and think.
In higher education, developing strong multiple-choice questions can be a time-intensive part of the course design process. Developing such items requires subject-matter expertise and assessment literacy, and for faculty and designers who are creating and producing online courses, it can be difficult to find the capacity to craft quality multiple-choice questions.
At the University of Michigan Center for Academic Innovation, learning experience designers are using generative artificial intelligence to streamline the multiple-choice question development process and help ameliorate this issue. In this article, I summarize one of our projects that explored effective prompting strategies to develop multiple-choice questions with ChatGPT for our open course portfolio. We examined how structured prompting can improve the quality of AI-generated assessments, producing relevant comprehension and recall items and options that include plausible distractors.
Achieving this goal enables us to develop several ungraded practice opportunities, preparing learners for their graded assessments while also freeing up more time for course instructors and designers.
