Institutions can use AI to make skills, pathways, and job outcomes visible to students and employers in ways traditional transcripts cannot.
Academic affairs, workforce development, career services, and employers need a shared definition of readiness and competency before tools can deliver meaningful value.
The second is portable competency records. Learning and employment records — AI-enabled documentation of what a student knows and can do, expressed in language employers recognize — are the infrastructure that makes credentials legible across the education-to-employment continuum. When a student can show an employer not just “completed Supply Chain Management 101” but “demonstrated proficiency in inventory optimization, route planning, and logistics software at the industry-recognized level,” the credential stops being abstract. It becomes evidence. Building these records requires investment in tools, yes — but more importantly, it requires faculty, workforce development staff, and employer partners to agree on what competency actually looks like before the technology is ever purchased.
The reason that I claim I am not well-suited to thrive as an instructor in the age of AI is because both AI Enthusiasts and AI Resisters put a lot of thought and energy into completely redesigning their classes in response to AI. This is the one takeaway that I don’t think the Exhausted Majority has fully accepted yet—to excel as a teacher in this AI era, you need to totally revise how you teach and how you assess what students learn in your classes.
I can say this much—whatever solution our industry comes up with, it’s likely to emerge from teaching and learning centers. Contrary to what Paul Schofield wrote in the Chronicle of Higher Education, pedagogy experts are the best hope we have to equip today’s faculty with the tools required to succeed in this uncertain educational environment. As I always tell my students, “I was trained for 7 years to become a researcher and 2 days to become a teacher.” The idea that only disciplinary experts know how to teach and have nothing to learn from so-called “nonscholars” is so laughable that one has to wonder whether an AI agent jokingly wrote that sad opinion piece to troll the whole academe.
Last week, one of the top law schools in the United States, the University of California, Berkeley School of Law, released its final policy on artificial intelligence, effective summer 2026. In the span of a breezy 1.5 pages, the school outlined the challenge AI poses to legal education and how it plans to address this problem. Despite these intentions, this AI policy is, in my estimation, the worst AI policy in higher education I have seen.
From AI Tutors to AI Study Mates— from drphilippahardman.substack.com by Dr Philippa Hardman New research reveals how AI can enable real learning — not just productivity gains
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The point isn’t that AI is inherently bad for learning — it’s that the meta-analyses showing that LLMs improve assignment and performance scores are measuring the wrong thing. They’re measuring performance with the AI present, not learning that persists once it’s gone.
. From DSC: Notice that when an AI-based learning system can remember what you’ve worked on and how you are doing — where you are struggling or doing well — it can have a positive impact on your longer-term learning. That, to me, is where long-term based learner profiles come in.
Later in the article, Dr. Hardman points out that “if we want to deliver AI tooling which supports substantive learning, we need to intentionally create a new category of AI tool for ‘learning at work’ which prioritises learning and development over productivity.” While I agree with that, I do wonder if businesses will care, so long as the work gets done and gets done well. But this calls into mind the word “experience” — something that traditionally has been hard fought to get in the corporate world. But the corporate realm often doesn’t like to pay for experience (beyond key AI-based jobs) when they perceive it’s getting too expensive. Ask all those 50 and over who had or have a target on their backs.
This $10K AI School Promises to Future-Proof Your Career — from builtin.com by Matthew Urwin Khan Academy, TED and ETS are starting a new program to equip students and professionals with the skills to thrive in an increasingly AI-driven economy. Here’s what you need to know.
Summary: The Khan TED Institute is a higher-education program that will teach students and workers how to use AI through interactive learning. The program’s AI-centric curriculum is an unproven approach, though, casting doubt on whether it will actually improve learning outcomes and career prospects.
Higher education might be on the verge of a radical overhaul to bring it up to speed in the age of artificial intelligence. At the TED2026 conference, Khan Academy, TED and ETS announced that they’re partnering to establish the Khan TED Institute — a new program that reorients the college curriculum around AI. By joining forces, the education technology trio aims to develop an alternative to traditional universities that better tracks student progress, teaches more relevant skills and provides a more personalized learning experience.
Accessibility is another major tenet of the Khan TED Institute. Its virtual nature allows anyone with an internet connection to participate in the program and makes it easier for students to move at their preferred pace. And because its curriculum prioritizes competency over course credits, advanced learners can complete the program in a shorter period. Time isn’t the only thing students can save on, either: The Institute promises a bachelor’s degree for less than $10,000, offering a much more affordable alternative to the typical four-year degree.
From DSC: Faculty senates don’t do well with this pace of change. But to their credit, few organizations can begin to deal with this pace of change.
AI & the Future of Learning Summit brings industry, education leaders together to discuss higher education’s opportunity to lead, what students need, and what partnerships are possible
As artificial intelligence rapidly reshapes the nature of work and learning, speakers at the University of Michigan’s AI & the Future of Learning Summit delivered a clear message: higher education must take a leading role in defining what comes next.
One CEO of a leading educational technology company put it like this: “The only bad thing would be universities standing still.”
Universities must embrace their roles as providers of continuous, lifelong learning that evolves alongside technological change.
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This shift is already affecting early-career pathways. Employers are placing greater emphasis on experience, while traditional entry-level roles are becoming less accessible. There is often a gap between what a credential represents and the expectations of employers.
That gap is particularly evident in access to internships. Chris Parrish, co-founder and president of Podium, noted that millions of students compete for a limited number of internships each year, making it increasingly difficult to gain the experience employers demand.
“If you miss out on an internship, you’re twice as likely to be unemployed,” Parrish said.
The quest to build a better AI tutor— from hechingerreport.org by Jill Barshay Researchers make progress with an older ed tech idea: personalized practice
One promising idea has less to do with how an AI tutor explains concepts and more with what it asks students to practice next.
A team at the University of Pennsylvania, which included some AI skeptics, recently tested this approach in a study of close to 800 Taiwanese high school students learning Python programming. All the students used the same AI tutor, which was designed not to give away answers.
But there was one key difference. Half the students were randomly assigned to a fixed sequence of practice problems, progressing from easy to hard. The other half received a personalized sequence with the AI tutor continuously adjusting the difficulty of each problem based on how the student was performing and interacting with the chatbot.
The idea is based on what educators call the “zone of proximal development.” When problems are too easy, students get bored. When they’re too hard, students get frustrated. The goal is to keep students in a sweet spot: challenged, but not overwhelmed.
The researchers found that students in the personalized group did better on a final exam than students in the fixed problem group. The difference was characterized as the equivalent of 6 to 9 months of additional schooling, an eye-catching claim for an after-school online course that lasted only five months. … To address this, Chung’s team combined a large language model with a separate machine-learning algorithm that analyzes how students interact with the online course platform — how they answer the practice questions, how many times they revise or edit their coding, and the quality of their conversations with the chatbot — and uses that information to decide which problem to serve up next.
From DSC: I have been proposing that the AI-based learning platform of the future will be constantly doing this — every single day. It will know what the in-demand skills are — at any given moment in time. It will then be able to direct you to resources that will help you gain those skills. Though in my vision, the system is querying actual/open job descriptions, not analyzing learning data from enterprise learners. Perhaps I should add that to the vision.
The Job Skills Report 2026 analyzes learning data from more than 6 million enterprise learners to identify the future job skills organizations need most. It’s designed for HR and L&D leaders; data, IT, and software & product development leaders; higher education administrators; and government agencies seeking actionable insights on workforce skills trends and AI-driven transformation.
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Drawing on data from 6 million enterprise learners across nearly 7,000 organizations, the Job Skills Report 2026 guides you through the skills reshaping the global economy. This year’s analysis spans Data, IT, and Software & Product Development—and the Generative AI skills becoming essential for every role.
The answers were frequently indistinguishable across different models by different companies that have different architectures and use different training data. The metaphors, imagery, word choices, sentence structures — even punctuation — often converged. Jiang’s team called this phenomenon “inter-model homogeneity” and quantified the overlaps and similarities. To drive the point home, Jiang titled her paper, the “Artificial Hivemind.” The study won a best paper award at the annual conference on Neural Information Processing Systems in December 2025, one of the premier gatherings for AI research.
AI Has No Moral Compass. Do You? — from michelleweise.substack.com by Michelle Weise & Dana Walsh Why the Age of AI Demands We Take Character Formation Seriously
Here’s something to chew on:
Anthropic, the company behind Claude — a chatbot used by 30 million users per month — has exactly one person (whom we know of) working on AI ethics. One. A young Scottish philosopher is doing the vital work of training a large language model to discern right from wrong.
I don’t say this to shame Anthropic. In fact, Anthropic appears to be the only company (that we know of) being explicit about the moral foundations and reasoning of its chatbot. Hundreds of millions of users worldwide are leveraging tools from other LLMs that do not appear to have an explicit moral compass being cultivated from within.
I raise this because this is yet another example of where we are: extraordinary technical power advancing without an equally strong moral infrastructure to support it.
Why do we keep producing people who are skilled but not wise?
From DSC…note these excerpts from Pradnya’s posting:
I’m not manually assigning 400 people anymore ? They’re actually taking relevant courses now Shows me the data ? Suggests courses to fix it ? I look like a strategic genius Completion rates up 34% ? Nobody’s “stuck” anymore
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.
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
How Your Learners *Actually* Learn with AI— from drphilippahardman.substack.com by Dr. Philippa Hardman What 37.5 million AI chats show us about how learners use AI at the end of 2025 — and what this means for how we design & deliver learning experiences in 2026
Last week, Microsoft released a similar analysis of a whopping 37.5 millionCopilot conversations. These conversation took place on the platform from January to September 2025, providing us with a window into if and how AI use in general — and AI use among learners specifically – has evolved in 2025.
Microsoft’s mass behavioural data gives us a detailed, global glimpse into what learners are actually doing across devices, times of day and contexts. The picture that emerges is pretty clear and largely consistent with what OpenAI’s told us back in the summer:
AI isn’t functioning primarily as an “answers machine”: the majority of us use AI as a tool to personalise and differentiate generic learning experiences and – ultimately – to augment human learning.
Let’s dive in!
Learners don’t “decide” to use AI anymore. They assume it’s there, like search, like spellcheck, like calculators. The question has shifted from “should I use this?” to “how do I use this effectively?”
So where do you start? There are many agentic tools and platforms for AI tasks on the market, and the most effective approach is to focus on practical, high-impact workflows. So here, I’ll look at some of the most compelling use cases, as well as provide an overview of the tools that can help you quickly deliver tangible wins.
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Some of the strongest opportunities in HR include:
Workforce management, administering job satisfaction surveys, monitoring and tracking performance targets, scheduling interventions, and managing staff benefits, medical leave, and holiday entitlement.
Recruitment screening, automatically generating and posting job descriptions, filtering candidates, ranking applicants against defined criteria, identifying the strongest matches, and scheduling interviews.
Employee onboarding, issuing new hires with contracts and paperwork, guiding them to onboarding and training resources, tracking compliance and completion rates, answering routine enquiries, and escalating complex cases to human HR specialists.
Training and development, identifying skills gaps, providing self-service access to upskilling and reskilling opportunities, creating personalized learning pathways aligned with roles and career goals, and tracking progress toward completion.
Here’s what’s shaped the AI-education landscape in the last month:
The AI Speed Trap is [still] here: AI adoption in L&D is basically won (87%)—but it’s being used to ship faster, not learn better (84% prioritising speed), scaling “more of the same” at pace.
AI tutors risk a “pedagogy of passivity”: emerging evidence suggests tutoring bots can reduce cognitive friction and pull learners down the ICAP spectrum—away from interactive/constructive learning toward efficient consumption.
Singapore + India are building what the West lacks: they’re treating AI as national learning infrastructure—for resilience (Singapore) and access + language inclusion (India)—while Western systems remain fragmented and reactive.
Agentic AI is the next pivot: early signs show a shift from AI as a content engine to AI as a learning partner—with UConn using agents to remove barriers so learners can participate more fully in shared learning.
Moodle’s AI stance sends two big signals: the traditional learning ecosystem in fragmenting, and the concept of “user sovereignty” over by AI is emerging.
For Cogniti to be taken seriously, it needs to be woven into the structure of your unit and its delivery, both in class and on Canvas, rather than left on the side. This article shares practical strategies for implementing Cogniti in your teaching so that students:
understand the context and purpose of the agent,
know how to interact with it effectively,
perceive its value as a learning tool over any other available AI chatbots, and
engage in reflection and feedback.
In this post, we discuss how to introduce and integrate Cogniti agents into the learning environment so students understand their context, interact effectively, and see their value as customised learning companions.
In this post, we share four strategies to help introduce and integrate Cogniti in your teaching so that students understand their context, interact effectively, and see their value as customised learning companions.
Collection: Teaching with Custom AI Chatbots — from teaching.virginia.edu; via Derek Bruff The default behaviors of popular AI chatbots don’t always align with our teaching goals. This collection explores approaches to designing AI chatbots for particular pedagogical purposes.
