Why universities must become flexible lifelong partners, not one-time providers — from timeshighereducation.com by Sankar Sivarajah
As careers become increasingly non-linear and shaped by rapid change, universities must evolve beyond traditional degree provision, says Sankar Sivarajah. Here, he outlines strategies

From programmes to learning ecosystems
These pressures point towards a broader redefinition of higher education. Rather than viewing education as a one-time experience culminating in a degree, universities increasingly need to see themselves as partners in professional development across an entire career.

This means moving from a model centred on programmes to one focused on learning ecosystems that allow individuals to enter, leave and re-engage with higher education as their needs evolve.

Business schools may be particularly well placed to lead this shift because of their close engagement with employers and their long tradition of educating professionals at different stages of their careers.

But success will depend on more than introducing new modules or certificates. Universities must confront a fundamental question. Are the systems, structures and cultures that define higher education capable of supporting genuinely flexible learning?

The sector has already embraced the language of lifelong learning – the next step is ensuring that universities themselves are built to deliver it.


From DSC:
Long-time readers of this blog have seen this graphic of mine posted over the last 12+ years:
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Also relevant/see:

What if the undergraduate journey were a four-year internship? — from timeshighereducation.com by Michelle Seref
Treating work placements and co-curricular programmes as optional or supplementary misses deeper questions about whether traditional degrees prepare students for careers. Michelle Seref explains

Attending workshops or polishing a résumé in their final semester does not make students career-ready. They need to practise how to work – how to collaborate, navigate ambiguity, manage projects and apply knowledge in context – throughout their academic experience. The reality is that career readiness is not a co-curricular programme; it is an essential part of an integrated curriculum.

To be clear, employers do not expect classrooms to become training centres. What they are asking for – implicitly and explicitly – is graduates who can function in complex environments from day one. That means graduates who can work in teams, communicate professionally with stakeholders, adapt when plans change, apply theory to real constraints and learn continuously on the job.

These capabilities do not develop through passive learning. But experiential learning is often misunderstood as a single, high-impact activity: an internship, a capstone project or study abroad. In reality, its power comes from repetition and progression. One experience introduces exposure. A sequence of experiences builds competence.

We are proposing a paradigm shift: repositioning the undergraduate journey as a four-year professional internship rather than a continuation of the K-12 classroom environment. 

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From DSC:

The problem with this innovative idea is that faculty often are not out in the “real world.” The best chance higher ed has to deliver on this idea is via the adjunct faculty members out there. Often, they are the ones practicing what they are teaching. They are constantly pulse-checking — and actively involved with — their industries and have more up-to-date, practical knowledge.

But this is a problem for traditional institutions of higher education, which have treated their adjunct faculty members poorly through the years. Adjunct faculty members hardly make minimum wage, have no benefits, no retirement plans, etc. — plus they have little to no say in faculty senates. 

Organizational change would be a requirement.

 

Mapping the Structural Divide — from kylesaunders.com by Kyle Saunders
Institutional Resilience, Post-College Market Position, and Artificial Intelligence Exposure Across 1,556 U.S. Colleges and Universities

Where does your institution stand?
U.S. four-year colleges and universities face compounding pressures — demographic decline, fiscal stress, and artificial intelligence — that will reshape the sector over the next decade. This project maps where 1,556 institutions are structurally positioned across two dimensions, using federal data anyone can verify.

X-axis: Institutional Resilience
Can this institution absorb financial and enrollment shocks?
Endowment per student · Revenue diversification · Enrollment trend · Admissions selectivity

Y-axis: Post-College Market Position
How well does this institution position graduates for the labor market ahead?
Completion rate · Earnings-to-debt ratio · AI exposure (inverted) · Demographic trajectory

 

I Was a University AI Czar. I’m Not Equipped to Teach in the Age of AI. — from jgellers.substack.com by Josh Gellers, PhD

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.

Also from Dr. Gellers, see:

The Worst AI Policy in Higher Ed
How Berkeley Law Boalt-ed From Expertise in Favor of Abstinence

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.

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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.

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“The sad fact is that we don’t teach learners how to be good at learning. Whether K12, higher ed, or organizations, it’s just not there.”

 

from Clark Quinn’s posting entitled, Thoughts on meta-coaching!

 

From DSC:
I agree. We could do a much better job at this.

 

LinkedIn Grad’s Guide 2026: Starting your career in the AI era — from linkedin.com by Gianna Prudente
To help you head off in the right direction, we’ve identified where those starting their careers are finding opportunity, based on data from millions of LinkedIn member profiles.

While all of this is happening, colleges are still catching up. Many students are graduating without having spent much time learning how AI actually fits into day-to-day work — even as employers seek out those exact skills.

“Colleges are moving into an era of, we’ll let the faculty decide, which leads to a very uneven experience for students because some faculty are really into AI and other faculty are not,” says Jeff Selingo, a higher education strategist. “Employers are the same; they don’t really know how to act around early careers.”

Taken together, new grads are entering a uniquely challenging environment: fewer traditional entry points, slower turnover and a workplace that’s evolving faster than the systems preparing people for it.

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I knew my writing students were using AI. Their confessions led to a powerful teaching moment — from theguardian.com by Micah Nathan
The problem wasn’t just the perfectly polished, yet mediocre prose. It’s what’s lost when we surrender the struggle to translate thought into words

For a few moments, all was quiet except the classroom’s ticking radiators. Then, a teary-eyed confession: one of the ostensible authors said she only used AI because she was scared of looking stupid, of being criticized for bad writing. She said she loved writing stories and hated having used AI. But she couldn’t stop herself, recounting a sequence similar to an addict’s descent: at first she fed her story into AI for a grammar check, it suggested line edits and she accepted, then it asked if she wanted structural edits, then it offered to rewrite the entire piece.

The other would-be author admitted he had never written a short story before and he had an idea but didn’t know where to start. I asked him why he didn’t reach out to me for help. He shrugged.

One of the other students raised her hand, saying she didn’t understand why it was bad for AI to write stories as long as the stories are based on their ideas. More students spoke: one wanted to know how using AI was any different from using a human editor. Another wanted me to answer why, at a university that launched one of the world’s first AI research programs in 1959, were we even having this debate? Isn’t AI meant to make everyone’s life easier? Less stressful? Isn’t the point of AI to free humans from the tedium of rote tasks?

The conversation that followed their confessions was one of the most productive teaching moments of my eight years at MIT. Writing, I told them, isn’t supposed to be easy, and of course it can be tedious but that doesn’t make it rote. Writing isn’t just the production of sentences – it’s the training of endurance by way of sustained attention. It’s a way of learning what one thinks by attempting to say it. 


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.


 

From DSC:
The types of postings/articles (such as the one below) make me ask, are we not shooting ourselves in the foot with AI and recent college graduates? If the bottom rungs continue to disappear, internships and apprenticeships can only go so far. There aren’t enough of them — especially valuable ones. So as this article points out, there will be threats to the long-term health of our talent pipelines unless we can take steps to thwart those impacts — and to do so fairly soon.

To me…vocational training and jobs are looking better all the time — i.e., plumbers, carpenters, electricians, mechanics, and more.


Can New Graduates Compete With AI? — from builtin.combyRichard Johnson
The increasing adoption of AI automation is compressing early-career jobs. How should new graduates get a foothold in the economy now?

Summary: AI is hollowing out entry-level roles by automating routine tasks, eliminating a rung on the career ladder. New graduates face intense competition and a rising skill floor. While firms gain short-term productivity, they risk a long-term talent shortage by eliminating junior training grounds.

Conversations about AI have covered all grounds: hype, fear and slop. But while some roll their eyes at yet another automation headline, soon?to?be graduates are watching the labor market with a very different level of urgency. They’re entering a world where the old paradox of needing experience to get experience is colliding with a new reality: AI is absorbing the standardized, routine tasks that once defined entry?level work. The result isn’t just a shift in job descriptions or skill-requirements, but rather a structural reshaping of the career pipeline.

Entry-level workers face an outsized disruption to their long-term career trajectories. They have the least buffer to adapt given their lack of relevant job market experience and heightened financial pressure to secure a job quickly with the student-debt repayment periods for recent graduates looming.

Momentum early in one’s career matters, and the first job on a resume shapes future compensation bands and opportunities. It also serves as a signal for perceived specialization or, at minimum, interest. Losing that foothold has compounding effects to one’s career ladder.


Also relevant/see:

New Anthropic Institute to Study Risks and Economic Effects of Advanced AI — from campustechnology.com by John K. Waters

Key Takeaways

  • Anthropic has launched the Anthropic Institute, a new research effort focused on the biggest societal challenges posed by more powerful AI systems.
  • The institute will study how advanced AI could affect the economy, the legal system, public safety, and broader social outcomes.
  • Anthropic co-founder Jack Clark will lead the institute in a new role as the company’s head of public benefit.
  • The new unit brings together Anthropic’s existing red-teaming, societal impacts, and economic research work, while adding new hires and new research areas.
 

Across the divide: reimagining faculty-staff collaboration in higher education — from timeshighereducation.com by Saskia van de Gevel
Academic units do best when they harness different viewpoints – from field scientists and curriculum designers to extension professionals – to drive innovation and relevance. Saskia van de Gevel offers proactive advice

Universities are not sustained by individual leaders or isolated units. They are sustained by teams of people who bring different kinds of expertise to a shared mission. When faculty and professional staff collaborate as genuine partners – aligned around outcomes, clear about roles and committed to mutual respect – institutions become more resilient, innovative and effective.

Also from timeshighereducation.com, see:

Again, we don’t send them 200 CVs. We might send 20, but they’re meticulously shortlisted. The employer saves time, the student feels they are being taken seriously and trust builds quickly on both sides.

And because we work closely with employers, we learn something universities often struggle to find out early enough: what the market is asking for now.

What academics need to know: we can’t do this without you
If I could say one thing to academic colleagues anywhere, it’s that employability can’t sit next to the curriculum. It has to live with it.

 

The Future of College in an AI World — from linkedin.com by Jeff Selingo
In today’s issue: The tension over AI in higher ed; application inflation continues and testing is back; what’s the future of the original classroom technology, the learning management system. 


Hundreds of higher ed and industry leaders gathered Tuesday for a summit
on AI and the future of learning at the University of Michigan.
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Conversations like the one we had at Michigan this week are necessary, but the action rarely matches the ambition.

  • We say the humanities are the operating system of an AI world, yet students and parents don’t believe it. They’re voting with their feet toward STEM, business, and narrowly tailored majors they believe will lead to a job.
  • Meanwhile, colleges are quietly eliminating the very humanities degrees the panelists were championing, employers are cutting the entry rungs off the career ladder for new graduates, and as Podium Education co-founder Christopher Parrish reminded us yesterday, there’s a yawning gap between demand for experience and the internships that actually exist.


AI Music Generators: Teaching With These Catchy AI Tools — from techlearning.com by Erik Ofgang
AI music generators are getting better and better, and there are more applications in the classroom as a result.

Are All AI Music Generators More Or Less The Same?
No. After experimenting with a few various free ones, I found a wide range of quality with the same prompts.

Gemini is the only one I’d currently recommend. It’s user-friendly but limited and only creates 30-second clips. Other music generators could potentially outperform Gemini with prompt adjustments. The ones I tried did better with the instrumentals but struggled more with the lyrics, and that kind of defeated the purpose of the tool for me.


ChatDOC: Teaching With The AI Summarizing Tool — from techlearning.com by Erik Ofgang
ChatDOC lets users turn any PDF into an AI chatbot that can summarize the text, answer questions, and generate quizzes.

What Is ChatDOC?
ChatDOC is an AI designed to help users interact with PDFs of various types, be it research papers, short stories, or chapters from larger works. Users upload a PDF and then have the opportunity to “chat” with that document, that is speak with a chatbot that bases its answers off of the uploaded text.

ChatDOC can perform tasks such as provide a short summary, search for specific terms, explain the overall theme if it’s a work of literature, or unpack the science in a research paper.

Other similar tools are out there, but ChatDOC is definitely one of the better PDF readers I’ve used. Its free version is quick and easy-to-use, and delivers on its promise of providing an AI that can discuss a given document with users and even quiz them on it.


From AI access to workforce readiness — from chieflearningofficer.com by Johnny Hamilton, Amy Stratbucker, & Brad Bigelow
Is your workforce using the right tool with an outdated mindset and playbook? Why old playbooks fall short — and what learning leaders must do next.

The leadership opportunity
Organizations do not need to predict every future AI capability. They need systems that allow people to explore with curiosity, practice safely, reflect deeply and adapt continuously — starting with what they already have and extending as capabilities evolve.

For CLOs, this is a moment to lead from the center of change — designing workforce readiness that keeps pace with accelerating technology while making work more rewarding for employees and more valuable for the organization. That is how AI moves from the promise of transformation to demonstrated readiness and, ultimately, from promise to performance.


Addendums on 3/19/26:
How to Build Practice-Based Learning Activities with AI — from drphilippahardman.substack.com by Dr Philippa Hardman
Four evidence-based methods for designing, building & deploying active learning activities with your favourite LLM

Most L&D teams are using AI to make content faster. The real opportunity is using it as a practice engine.

The Synthesia 2026 AI in L&D Report f2026 AI in L&D Report found that the fastest-growing areas of planned AI adoption aren’t in content creation — they’re in assessments and simulations (36%), adaptive pathways (33%), and AI tutors (29%). In other words: L&D teams are starting to realise that the most powerful use of AI isn’t producing learning materials. It’s creating environments where learners actually practise.

And you can build these right now — no dev team, no custom platform, no code. Each method below includes a prompt you can paste into your preferred AI tool to generate a working interactive prototype: a self-contained practice activity with a briefing screen, a live AI interaction, and a debrief — all running in the browser, ready to share with stakeholders or deploy to learners.

OpenAI Adds Interactive Math and Science Learning Tools to ChatGPT — from campustechnology.com by Rhea Kelly

Key Takeaways

  • ChatGPT adds interactive learning tools: OpenAI introduced interactive math and science visualizations that allow users to explore formulas, variables, and relationships in real time.
  • The tool currently covers over 70 core math and science topics and is aimed initially at high school and college-level learners.
  • Users can adjust variables, manipulate formulas, and immediately see how changes affect graphs and outcomes.
 

“But what’s happening right now is exponential.” — from linkedin.com by Josh Cavalier

Excerpt:

I need to be honest with you. I’ve been running experiments this week with Claude Code and Opus 4.6, and we have reached the precipice in the collapse of time required to produce high-quality text-based ID outputs.

This includes performance consulting reports, learning needs analyses, action mapping, scripts, storyboards, facilitator guides, rubrics, and technical specs.

I just mapped the entire performance consulting process into a multimodal AI integration architecture (diagram image). Every phase. Entry and contracting. Performance analysis. Cause analysis. Solution design. Implementation. Evaluation. Thirty files. System specifications for each. The next step is to vet out each “skill” with an expert performance consultant.

Then I attempted a learning output: an 8-module course built with a cognitive scaffold that moves beyond content delivery to facilitate deliberate practice, meaning-making, and guided reflection within the learner’s own context.

The result:



AI and human-centered learning — from linkedin.com by Patrick Blessinger

Democratizing opportunities

AI adaptive learning can adapt learning in real-time. These tools have the potential to provide a more personalized learning experience, but only if used properly.

The California State University system uses ChatGPT Edu (OpenAI, 2025). Students use it for AI-assisted tutoring, study aids, and writing support. These resources provide 24/7 availability of subject-matter expertise tailored to students’ learning needs. It is not a replacement for professors. Rather, it extends the reach of mentorship by reducing access barriers.

However, we must proceed with intellectual humility and ethical responsibility. Even though AI can customize messages, it cannot replace the encouragement of a teacher or professor, or the social and emotional aspects of learning. It’s at the intersection of humanistic values and knowledge development that education must find its balance.

 

How storytelling can turn international students into the most powerful voices in the room — from timeshighereducation.com by Natalie Cummins
Turning presentations into a visual storytelling task allows international students to demonstrate their learning through elements such as sound, visuals, silence and pacing rather than just language

Rather than changing the assessment, I shared a simple storytelling checklist that emphasised structure – a strong opening, a clear human dilemma at the centre and a purposeful ending – alongside explicit inspiration for communicating meaning through visuals, video, metaphor, sound and pacing rather than language alone.

On presentation day, the group told a moving story grounded in a global extractive firm where one student’s family worked. What stood out was not linguistic fluency but clarity of meaning. Their story allowed a complex organisational problem of unsafe work conditions to unfold, with non-verbal elements carrying the ethical and human weight of the case.

This group delivered one of the most compelling presentations I have ever seen. The room was transfixed.

 

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.

Be the very best at using AI for your gig.

more here.


Also see:


Also relevant/see:

 

The Essential Retrieval Practice Handbook — from edutopia.org
Retrieval practice is one of the most effective ways to strengthen learning. Here’s a collection of our best resources to use in your classroom today.
January 29, 2026


Also see:

What is retrieval practice? — from retrievalpractice.org

When we think about learning, we typically focus on getting information into students’ heads. What if, instead, we focus on getting information out of students’ heads?


 

Farewell to Traditional Universities | What AI Has in Store for Education

Premiered Jan 16, 2026

Description:

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:


The End of Universities as We Know Them: What AI Is Bringing

Premiered Jan 27, 2026

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
 

AI and the Work of Centers for Teaching and Learning — from derekbruff.org by Derek Bruff

  • 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.

 

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 million Copilot 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?”


8 AI Agents Every HR Leader Needs To Know In 2026 — from forbes.com by Bernard Marr

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.

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.

 

 
© 2025 | Daniel Christian