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.
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
Teach Smarter with AI — from wondertools.substack.com by Jeremy Caplan and Lance Eaton 10 tested strategies from two educators who actually use them
I recently talked with Lance Eaton, Senior Associate Director of AI and Teaching & Learning at Northeastern University and writer of AI + Education = Simplified. We traded ideas about what’s actually working. We came up with 10 specific, practical ways anyone who teaches, coaches, or leads can put AI to work.
Watch the full conversation above, or read highlights below.
Shadow AI Isn’t a Threat: It’s a Signal— from campustechnology.com by Damien Eversmann Unofficial AI use on campus reveals more about institutional gaps than misbehavior.
Key Takeaways
Shadow AI is widespread in higher education: Faculty, researchers, students, and staff are using AI tools outside official IT channels, including consumer platforms and public cloud services that may involve sensitive data.
Unauthorized AI use creates data, compliance, and cost risks: Consumer AI tools may store or reuse user data, while uncoordinated adoption drives redundant licenses, unpredictable cloud costs, and weaker security oversight.
Institutions are shifting from restriction to enablement: Some campuses are making approved paths easier by offering ready-to-use research environments, campus-managed AI tools, clear guidance on data and vendors, and streamlined approval processes.
How to lead even when your company doesn’t allow AI Even if your corporation isn’t ready for AI, you can still research tools personally to stay ahead of the curve, so when organizational restrictions lift, you are ready to use AI for learning right away. Here are some tools you can test at home if they’re restricted in your workplace:
Content generation – Start testing text-based tools to get a taste of how AI can accelerate content creation. Then take it to the next level by exploring tools that generate voices, music, and sound effects.
AI coaching tools – Have AI pose as a customer co-worker or customer to get a taste of what it’s like to use it as a conversation coach. Next, use the voice and video capabilities in an app like ChatGPT to explore how AI can coach someone through tasks.
In-the-flow learning assistants – Test turning documents into a conversational avatar and interacting with it to see how it feels. Then think about how the technology could potentially transform static content into dynamic learning experiences for employees.
Vibe-coded simulations – Experiment with this technology by creating a simple, fun game. Afterwards, brainstorm some ideas on how it could quickly create simulations for your learners in the future.
Affordable AI adoption focuses on evolving existing systems: Universities are embedding AI into current devices, workflows, and legacy systems rather than rebuilding infrastructure or investing in new data centers.
Edge AI reduces costs and improves access: Running AI models on local devices or networks lowers cloud processing costs, enhances security, and supports learning use cases such as tutoring, translation, transcription, and adaptive learning.
Enterprise integration and governance drive impact: Institutions are applying AI across admissions, advising, facilities, and research workflows, supported by shared resource hubs, data governance, AI literacy, and outcome-driven implementation.
Something Big Is Happening— from shumer.dev by Matt Shumer; see below from the BIG Questions Institute, where I got this article from
I’ve spent six years building an AI startup and investing in the space. I live in this world. And I’m writing this for the people in my life who don’t… my family, my friends, the people I care about who keep asking me “so what’s the deal with AI?” and getting an answer that doesn’t do justice to what’s actually happening. I keep giving them the polite version. The cocktail-party version. Because the honest version sounds like I’ve lost my mind. And for a while, I told myself that was a good enough reason to keep what’s truly happening to myself. But the gap between what I’ve been saying and what is actually happening has gotten far too big. The people I care about deserve to hear what is coming, even if it sounds crazy.
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They’ve now done it. And they’re moving on to everything else.
The experience that tech workers have had over the past year, of watching AI go from “helpful tool” to “does my job better than I do”, is the experience everyone else is about to have. Law, finance, medicine, accounting, consulting, writing, design, analysis, customer service.Not in ten years. The people building these systems say one to five years. Some say less. And given what I’ve seen in just the last couple of months, I think “less” is more likely.
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The models available today are unrecognizable from what existed even six months ago. The debate about whether AI is “really getting better” or “hitting a wall” — which has been going on for over a year — is over. It’s done. Anyone still making that argument either hasn’t used the current models, has an incentive to downplay what’s happening, or is evaluating based on an experience from 2024 that is no longer relevant. I don’t say that to be dismissive. I say it because the gap between public perception and current reality is now enormous, and that gap is dangerous… because it’s preventing people from preparing.
What “Something Big Is Happening” Means for Schools — from/by the BIG Questions Institute
Matt Shumer’s newsletter post Something Big is Happening has been read over 80 million times within the week when it was published, on February 9.
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Still, it’s worth reading Shumer’s post. Given the claims and warnings in Something Big Is Happening (and countless other articles), how would you truly, honestly respond to these questions:
What will the purpose of school be in 5 years?
What are we doing now that we must leave behind right away?
What can we leave behind gradually?
What does rigor look like in this AI-powered world?
Does our strategy look like making adjustments at the margins or are we preparing our students for a fundamental shift?
What is our definition of success? How do the the implications of AI and jobs (and other important forces, from geopolitical shifts and climate change, to mental health needs and shifting generational values) impact the outcomes we prioritize? What is the story of success we want to pass on to our students and wider community?
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
At CES 2026, Everything Is AI. What Matters Is How You Use It — from wired.com by Boone Ashworth Integrated chatbots and built-in machine intelligence are no longer standout features in consumer tech. If companies want to win in the AI era, they’ve got to hone the user experience.
Beyond Wearables
Right now, AI is on your face and arms—smart glasses and smart watches—but this year will see it proliferate further into products like earbuds, headphones, and smart clothing.
Health tech will see an influx of AI features too, as companies aim to use AI to monitor biometric data from wearables like rings and wristbands. Heath sensors will also continue to show up in newer places like toilets, bath mats, and brassieres.
The smart home will continue to be bolstered by machine intelligence, with more products that can listen, see, and understand what’s happening in your living space. Familiar candidates for AI-powered upgrades like smart vacuums and security cameras will be joined by surprising AI bedfellows like refrigerators and garage door openers.
After a year of bot battles, one thing stands out: There is no single best AI. The smartest way to use chatbots today is to pick different tools for different jobs — and not assume one bot can do it all.
Some enterprise platforms now support cross-agent communication and integration with ecosystems maintained by companies like Microsoft, NVIDIA, Google, and Oracle. These cross-platform data fabrics break down silos and turn isolated AI pilots into enterprise-wide services. The result is an IT backbone that not only automates but also collaborates for continuous learning, diagnostics, and system optimization in real time.
It’s difficult to think of any single company that had a bigger impact on Wall Street and the AI trade in 2025 than Nvidia (NVDA).
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Nvidia’s revenue soared in 2025, bringing in $187.1 billion, and its market capitalization continued to climb, briefly eclipsing the $5 trillion mark before settling back in the $4 trillion range.
There were plenty of major highs and deep lows throughout the year, but these 15 were among the biggest moments of Nvidia’s 2025.
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.
Compliance As Part Of The Everyday Automation Fabric
According to the Thomson Reuters Future Of Professionalsreport, most experts already expect AI to transform their work within five years, with many viewing it as a positive force. The challenge now is clear: legal and compliance leaders must understand the tools reshaping their field and prepare their teams for a very different way of working in 2026.
Addendum on 12/17/25:
Top 10 legal tech episodes in 2025 — from lawyersweekly.com.au With AI and legal technology at the forefront of the profession’s mind now more than ever, The Lawyers Weekly Show and our special series LawTech Talks delivered in-depth explorations of these game-changing topics. Now, we’re highlighting the 10 most downloaded episodes that sparked conversation across the legal tech community.
While it’s true that Nano Banana generates better infographics than other AI models, the conversation has so far massively under-sold what’s actually different and valuable about this tool for those of us who design learning experiences.
What this means for our workflow:
Instead of the traditional “commission ? wait ? tweak ? approve ? repeat” cycle, Nano Banana enables an iterative, rapid-cycle design process where you can:
Sketch an idea and see it refined in minutes.
Test multiple visual metaphors for the same concept without re-briefing a designer.
Build 10-image storyboards with perfect consistency by specifying the constraints once, not manually editing each frame.
Implement evidence-based strategies (contrasting cases, worked examples, observational learning) that are usually too labour-intensive to produce at scale.
This shift—from “image generation as decoration” to “image generation as instructional scaffolding”—is what makes Nano Banana uniquely useful for the 10 evidence-based strategies below.
You’ll hear me briefly describe five recent op-eds on teaching and learning in higher ed. For each op-ed, I’ll ask each of our panelists if they “take it,” that is, generally agree with the main thesis of the essay, or “leave it.” This is an artificial binary that I’ve found to generate rich discussion of the issues at hand.
