How do we reconcile the first three points with the final one? The answer is that AI use that boosts individual performance does not naturally translate to improving organizational performance. To get organizational gains requires organizational innovation, rethinking incentives, processes, and even the nature of work. But the muscles for organizational innovation inside companies have atrophied. For decades, companies have outsourced this to consultants or enterprise software vendors who develop generalized approaches that address the issues of many companies at once. That won’t work here, at least for a while. Nobody has special information about how to best use AI at your company, or a playbook for how to integrate it into your organization. .
Today we are excited to launch Galileo Learn™, a revolutionary new platform for corporate learning and professional development.
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How do we leverage AI to revolutionize this model, doing away with the dated “publishing” model of training?
The answer is Galileo Learn, a radically new and different approach to corporate training and professional development.
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What Exactly is Galileo Learn™? Galileo Learn is an AI-native learning platform which is tightly integrated into the Galileo agent. It takes content in any form (PDF, word, audio, video, SCORM courses, and more) and automatically (with your guidance) builds courses, assessments, learning programs, polls, exercises, simulations, and a variety of other instructional formats.
Centering Public Understanding in AI Education
In a recent talk titled “Designing an Ecosystem of Resources to Foster AI Literacy,” Duri Long, Assistant Professor at Northwestern University, highlighted the growing need for accessible, engaging learning experiences that empower the public to make informed decisions about artificial intelligence. Long emphasized that as AI technologies increasingly influence everyday life, fostering public understanding is not just beneficial—it’s essential. Her work seeks to develop a framework for AI literacy across varying audiences, from middle school students to adult learners and journalists.
A Design-Driven, Multi-Context Approach
Drawing from design research, cognitive science, and the learning sciences, Long presented a range of educational tools aimed at demystifying AI. Her team has created hands-on museum exhibits, such as Data Bites, where learners build physical datasets to explore how computers learn. These interactive experiences, along with web-based tools and support resources, are part of a broader initiative to bridge AI knowledge gaps using the 4As framework: Ask, Adapt, Author, and Analyze. Central to her approach is the belief that familiar, tangible interactions and interfaces reduce intimidation and promote deeper engagement with complex AI concepts.
‘What I learned when students walked out of my AI class’ — from timeshighereducation.com by Chris Hogg Chris Hogg found the question of using AI to create art troubled his students deeply. Here’s how the moment led to deeper understanding for both student and educator
Teaching AI can be as thrilling as it is challenging. This became clear one day when three students walked out of my class, visibly upset. They later explained their frustration: after spending years learning their creative skills, they were disheartened to see AI effortlessly outperform them at the blink of an eye.
This moment stuck with me – not because it was unexpected, but because it encapsulates the paradoxical relationship we all seem to have with AI. As both an educator and a creative, I find myself asking: how do we engage with this powerful tool without losing ourselves in the process? This is the story of how I turned moments of resistance into opportunities for deeper understanding.
In the AI era, how do we battle cognitive laziness in students? — from timeshighereducation.com by Sean McMinn With the latest AI technology now able to handle complex problem-solving processes, will students risk losing their own cognitive engagement? Metacognitive scaffolding could be the answer, writes Sean McMinn
The concern about cognitive laziness seems to be backed by Anthropic’s report that students use AI tools like Claude primarily for creating (39.8 per cent) and analysing (30.2 per cent) tasks, both considered higher-order cognitive functions according to Bloom’s Taxonomy. While these tasks align well with advanced educational objectives, they also pose a risk: students may increasingly delegate critical thinking and complex cognitive processes directly to AI, risking a reduction in their own cognitive engagement and skill development.
Make Instructional Design Fun Again with AI Agents— from drphilippahardman.substack.com by Dr. Philippa Hardman A special edition practical guide to selecting & building AI agents for instructional design and L&D
Exactly how we do this has been less clear, but — fuelled by the rise of so-called “Agentic AI” — more and more instructional designers ask me: “What exactly can I delegate to AI agents, and how do I start?”
In this week’s post, I share my thoughts on exactly what instructional design tasks can be delegated to AI agents, and provide a step-by-step approach to building and testing your first AI agent.
After providing Claude with several prompts of context about my creative writing project, I requested feedback on one of my novel chapters. The AI provided thoughtful analysis with pros and cons, as expected. But then I noticed what wasn’t there: the customary offer to rewrite my chapter.
… Without Claude’s prompting, I found myself in an unexpected moment of metacognition. When faced with improvement suggestions but no offer to implement them, I had to consciously ask myself:“Do I actually want AI to rewrite this section?” The answer surprised me – no, I wanted to revise it myself, incorporating the insights while maintaining my voice and process.
The contrast was striking. With ChatGPT, accepting its offer to rewrite felt like a passive, almost innocent act – as if I were just saying “yes” to a helpful assistant. But with Claude, requesting a rewrite required deliberate action. Typing out the request felt like a more conscious surrender of creative agency.
Also re: metacognition and AI, see:
In the AI era, how do we battle cognitive laziness in students? — from timeshighereducation.com by Sean McMinn With the latest AI technology now able to handle complex problem-solving processes, will students risk losing their own cognitive engagement? Metacognitive scaffolding could be the answer, writes Sean McMinn
The concern about cognitive laziness seems to be backed by Anthropic’s report that students use AI tools like Claude primarily for creating (39.8 per cent) and analysing (30.2 per cent) tasks, both considered higher-order cognitive functions according to Bloom’s Taxonomy. While these tasks align well with advanced educational objectives, they also pose a risk: students may increasingly delegate critical thinking and complex cognitive processes directly to AI, risking a reduction in their own cognitive engagement and skill development.
By prompting students to articulate their cognitive processes, such tools reinforce the internalisation of self-regulated learning strategies essential for navigating AI-augmented environments.
EDUCAUSE Panel Highlights Practical Uses for AI in Higher Ed — from govtech.com by Abby Sourwine A webinar this week featuring panelists from the education, private and nonprofit sectors attested to how institutions are applying generative artificial intelligence to advising, admissions, research and IT.
Many higher education leaders have expressed hope about the potential of artificial intelligence but uncertainty about where to implement it safely and effectively. According to a webinar Tuesday hosted by EDUCAUSE, “Unlocking AI’s Potential in Higher Education,” their answer may be “almost everywhere.”
Panelists at the event, including Kaskaskia College CIO George Kriss, Canyon GBS founder and CEO Joe Licata and Austin Laird, a senior program officer at the Gates Foundation, said generative AI can help colleges and universities meet increasing demands for personalization, timely communication and human-to-human connections throughout an institution, from advising to research to IT support.
Here are the predictions, our votes, and some commentary:
“By 2028, at least half of large universities will embed an AI ‘copilot’ inside their LMS that can draft content, quizzes, and rubrics on demand.” The group leaned toward yes on this one, in part because it was easy to see LMS vendors building this feature in as a default.
“Discipline-specific ‘digital tutors’ (LLM chatbots trained on course materials) will handle at least 30% of routine student questions in gateway courses.” We learned toward yes on this one, too, which is why some of us are exploring these tools today. We would like to be ready how to use them well (or avoid their use) when they are commonly available.
“Adaptive e-texts whose examples, difficulty, and media personalize in real time via AI will outsell static digital textbooks in the U.S. market.” We leaned toward no on this one, in part because the textbook market and what students want from textbooks has historically been slow to change. I remember offering my students a digital version of my statistics textbook maybe 6-7 years ago, and most students opted to print the whole thing out on paper like it was 1983.
“AI text detectors will be largely abandoned as unreliable, shifting assessment design toward oral, studio, or project-based ‘AI-resilient’ tasks.” We leaned toward yes on this. I have some concerns about oral assessments (they certainly privilege some students over others), but more authentic assignments seems like what higher ed needs in the face of AI. Ted Underwood recently suggested a version of this: “projects that attempt genuinely new things, which remain hard even with AI assistance.” See his post and the replies for some good discussion on this idea.
“AI will produce multimodal accessibility layers (live translation, alt-text, sign-language avatars) for most lecture videos without human editing.” We leaned toward yes on this one, too. This seems like another case where something will be provided by default, although my podcast transcripts are AI-generated and still need editing from me, so we’re not there quite yet.
Description: I honestly don’t know how I should be educating my kids. A.I. has raised a lot of questions for schools. Teachers have had to adapt to the most ingenious cheating technology ever devised. But for me, the deeper question is: What should schools be teaching at all? A.I. is going to make the future look very different. How do you prepare kids for a world you can’t predict?
And if we can offload more and more tasks to generative A.I., what’s left for the human mind to do?
Rebecca Winthrop is the director of the Center for Universal Education at the Brookings Institution. She is also an author, with Jenny Anderson, of “The Disengaged Teen: Helping Kids Learn Better, Feel Better, and Live Better.” We discuss how A.I. is transforming what it means to work and be educated, and how our use of A.I. could revive — or undermine — American schools.
Anthropic’s “Prompt Engineering Overview” is a free masterclass that’s worth its weight in gold. Their “constitutional AI prompting” section helped us create a content filter that actually works—unlike the one that kept flagging our coffee bean reviews as “inappropriate.” Apparently “rich body” triggered something…
OpenAI’s “Cookbook” is like having a Michelin-star chef explain cooking—simple for beginners, but packed with pro techniques. Their JSON formatting examples saved us 3 hours of debugging last week…
Google’s “Prompt Design Strategies” breaks down complex concepts with clear examples. Their before/after gallery showing how slight prompt tweaks improve results made us rethink everything we knew about getting quality outputs.
Pro tip: Save these guides as PDFs before they disappear behind paywalls. The best AI users keep libraries of these resources for quick reference. .
“To address this, organizations should consider building a sustainable AI governance model, prioritizing transparency, and tackling the complex challenge of AI-fueled imposter syndrome through reinvention. Employers who fail to approach innovation with empathy and provide employees with autonomy run the risk of losing valuable staff and negatively impacting employee productivity.”
Key findings from the report include the following:
Employees are keeping their productivity gains a secret from their employers. …
In-office employees may still log in remotely after hours. …
Younger workers are more likely to switch jobs to gain more flexibility.
AI discovers new math algorithms— from by Zach Mink & Rowan Cheung PLUS: Anthropic reportedly set to launch new Sonnet, Opus models
The Rundown: Google just debuted AlphaEvolve, a coding agent that harnesses Gemini and evolutionary strategies to craft algorithms for scientific and computational challenges — driving efficiency inside Google and solving historic math problems.
… Why it matters: Yesterday, we had OpenAI’s Jakub Pachocki saying AI has shown “significant evidence” of being capable of novel insights, and today Google has taken that a step further. Math plays a role in nearly every aspect of life, and AI’s pattern and algorithmic strengths look ready to uncover a whole new world of scientific discovery.
At the recent HR Executive and Future Talent Council event at Bentley University near Boston, I talked with Top 100 HR Tech Influencer Joey Price about what he’s hearing from HR leaders. Price is president and CEO of Jumpstart HR and executive analyst at Aspect43, Jumpstart HR’s HR?tech research division, and author of a valuable new book, The Power of HR: How to Make an Organizational Impact as a People?Professional.
This puts him solidly at the center of HR’s most relevant conversations. Price described the curiosity he’s hearing from many HR leaders about AI agents, which have become increasingly prominent in recent months.
Live experiential learning: ILT as usual?
Is live experiential learning, or LEL, just a surface rebranding of traditional instructor-led training?
Absolutely not. In fact, LEL is as distant from traditional ILT as Sleep No More is from traditional theater.
Instead of sitting politely, nodding along — or nodding off — as an instructor carefully reads aloud from their slide deck, learners roam about, get their hands dirty and focus on the things that matter to them (yes, even if that means they don’t get to every topic or encounter them in the way we would have liked).
In short, LEL has the ability to shake up your learners, in a good way. And when they realize that this isn’t learning as usual, they land in a mental space that makes them more curious and receptive.
So what does this look like, really? And how does it work?
As learning and development leaders, you can create fun, engaging and challenging exercises for teams that develop these important characteristics and improve numerous markers of team efficacy. Exercises to improve team performance should be focused on four themes: negotiation, agreement, coordination and output. In this article, I’ll discuss each type of exercise briefly, then how I use a framework to create challenging and engaging exercises to improve collaborative problem-solving and performance on my teams.
Marketers have spent billions of dollars testing what works—and their insights can revolutionize microlearning. By borrowing from marketing’s best strategies, L&D professionals can create microlearning that cuts through the noise, engages learners, and drives real behavior change.
If marketing can make people remember a product, L&D can make people remember a skill.
.Get the 2025 Student Guide to Artificial Intelligence — from studentguidetoai.org This guide is made available under a Creative Commons license by Elon University and the American Association of Colleges and Universities (AAC&U). .
Agentic AI is taking these already huge strides even further. Rather than simply asking a question and receiving an answer, an AI agent can assess your current level of understanding and tailor a reply to help you learn. They can also help you come up with a timetable and personalized lesson plan to make you feel as though you have a one-on-one instructor walking you through the process. If your goal is to learn to speak a new language, for example, an agent might map out a plan starting with basic vocabulary and pronunciation exercises, then progress to simple conversations, grammar rules and finally, real-world listening and speaking practice.
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For instance, if you’re an entrepreneur looking to sharpen your leadership skills, an AI agent might suggest a mix of foundational books, insightful TED Talks and case studies on high-performing executives. If you’re aiming to master data analysis, it might point you toward hands-on coding exercises, interactive tutorials and real-world datasets to practice with.
The beauty of AI-driven learning is that it’s adaptive. As you gain proficiency, your AI coach can shift its recommendations, challenge you with new concepts and even simulate real-world scenarios to deepen your understanding.
Ironically, the very technology feared by workers can also be leveraged to help them. Rather than requiring expensive external training programs or lengthy in-person workshops, AI agents can deliver personalized, on-demand learning paths tailored to each employee’s role, skill level, and career aspirations. Given that 68% of employees find today’s workplace training to be overly “one-size-fits-all,” an AI-driven approach will not only cut costs and save time but will be more effective.
This is one reason why I don’t see AI-embedded classrooms and AI-free classrooms as opposite poles. The bone of contention, here, is not whether we can cultivate AI-free moments in the classroom, but for how long those moments are actually sustainable.
Can we sustain those AI-free moments for an hour? A class session? Longer?
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Here’s what I think will happen. As AI becomes embedded in society at large, the sustainability of imposed AI-free learning spaces will get tested. Hard. I think it’ll become more and more difficult (though maybe not impossible) to impose AI-free learning spaces on students.
However, consensual and hybrid AI-free learning spaces will continue to have a lot of value. I can imagine classes where students opt into an AI-free space. Or they’ll even create and maintain those spaces.
Duolingo’s AI Revolution — from drphilippahardman.substack.com by Dr. Philippa Hardman What 148 AI-Generated Courses Tell Us About the Future of Instructional Design & Human Learning
Last week, Duolingo announced an unprecedented expansion: 148 new language courses created using generative AI, effectively doubling their content library in just one year. This represents a seismic shift in how learning content is created — a process that previously took the company 12 years for their first 100 courses.
As CEO Luis von Ahn stated in the announcement, “This is a great example of how generative AI can directly benefit our learners… allowing us to scale at unprecedented speed and quality.”
In this week’s blog, I’ll dissect exactly how Duolingo has reimagined instructional design through AI, what this means for the learner experience, and most importantly, what it tells us about the future of our profession.
Medical education is experiencing a quiet revolution—one that’s not taking place in lecture theatres or textbooks, but with headsets and holograms. At the heart of this revolution are Mixed Reality (MR) AI Agents, a new generation of devices that combine the immersive depth of mixed reality with the flexibility of artificial intelligence. These technologies are not mere flashy gadgets; they’re revolutionising the way medical students interact with complicated content, rehearse clinical skills, and prepare for real-world situations. By combining digital simulations with the physical world, MR AI Agents are redefining what it means to learn medicine in the 21st century.
4 Reasons To Use Claude AI to Teach — from techlearning.com by Erik Ofgang Features that make Claude AI appealing to educators include a focus on privacy and conversational style.
After experimenting using Claude AI on various teaching exercises, from generating quizzes to tutoring and offering writing suggestions, I found that it’s not perfect, but I think it behaves favorably compared to other AI tools in general, with an easy-to-use interface and some unique features that make it particularly suited for use in education.
DC: THIS could unfortunately be the ROI companies will get from large investments in #AI — reduced headcount/employees/contract workers. https://t.co/zEWlqCSWzI
Duolingo will “gradually stop using contractors to do work that AI can handle,” according to an all-hands email sent by cofounder and CEO Luis von Ahn announcing that the company will be “AI-first.” The email was posted on Duolingo’s LinkedIn account.
According to von Ahn, being “AI-first” means the company will “need to rethink much of how we work” and that “making minor tweaks to systems designed for humans won’t get us there.” As part of the shift, the company will roll out “a few constructive constraints,” including the changes to how it works with contractors, looking for AI use in hiring and in performance reviews, and that “headcount will only be given if a team cannot automate more of their work.”
Something strange, and potentially alarming, is happening to the job market for young, educated workers.
According to the New York Federal Reserve, labor conditions for recent college graduates have “deteriorated noticeably” in the past few months, and the unemployment rate now stands at an unusually high 5.8 percent. Even newly minted M.B.A.s from elite programs are struggling to find work. Meanwhile, law-school applications are surging—an ominous echo of when young people used graduate school to bunker down during the great financial crisis.
What’s going on? I see three plausible explanations, and each might be a little bit true.
The new workplace trend is not employee friendly. Artificial intelligence and automation technologies are advancing at blazing speed. A growing number of companies are using AI to streamline operations, cut costs, and boost productivity. Consequently, human workers are facing facing layoffs, replaced by AI. Like it or not, companies need to make tough decisions, including layoffs to remain competitive.
Corporations including Klarna, UPS, Duolingo, Intuit and Cisco are replacing laid-off workers with AI and automation. While these technologies enhance productivity, they raise serious concerns about future job security. For many workers, there is a big concern over whether or not their jobs will be impacted.
Key takeaway: Career navigation has remained largely unchanged for decades, relying on personal networks and static job boards. The advent of AI is changing this, offering personalised career pathways, better job matching, democratised job application support, democratised access to career advice/coaching, and tailored skill development to help you get to where you need to be.Hundreds of millions of people start new jobs every year, this transformation opens up a multi-billion dollar opportunity for innovation in the global career navigation market.
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A.4 How will AI disrupt this segment? Personalised recommendations: AI can consume a vast amount of information (skills, education, career history, even youtube history, and x/twitter feeds), standardise this data at scale, and then use data models to match candidate characteristics to relevant careers and jobs. In theory, solutions could then go layers deeper, helping you position yourself for those future roles. Currently based in Amsterdam, and working in Strategy at Uber and want to work in a Product role in the future? Here are X,Y,Z specific things YOU can do in your role today to align yourself perfectly. E.g. find opportunities to manage cross functional projects in your current remit, reach out to Joe Bloggs also at Uber in Amsterdam who did Strategy and moved to Product, etc.
No matter the school, no matter the location, when I deliver an AI workshop to a group of teachers, there are always at least a few colleagues thinking (and sometimes voicing), “Do I really need to use AI?”
Nearly three years after ChatGPT 3.5 landed in our lives and disrupted workflows in ways we’re still unpacking, most schools are swiftly catching up. Training sessions, like the ones I lead, are springing up everywhere, with principals and administrators trying to answer the same questions: Which tools should we use? How do we use them responsibly? How do we design learning in this new landscape?
But here’s what surprises me most: despite all the advances in AI technology, the questions and concerns from teachers remain strikingly consistent.
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In this article, I want to pull back the curtain on those conversations. These concerns aren’t signs of reluctance – they reflect sincere feelings. And they deserve thoughtful, honest answers.
This week, in advance of major announcements from us and other vendors, I give you a good overview of the AI Agent market, and discuss the new role of AI governance platforms, AI agent development tools, AI agent vendors, and how AI agents will actually manifest and redefine what we call an “application.”
I discuss ServiceNow, Microsoft, SAP, Workday, Paradox, Maki People, and other vendors. My goal today is to “demystify” this space and explain the market, the trends, and why and how your IT department is going to be building a lot of the agents you need. And prepare for our announcements next week!
DeepSeek has quietly launched Prover V2, an open-source model built to solve math problems using Lean 4 assistant, which ensures every step of a proof is rigorously verified.
What’s impressive about it?
Massive scale: Based on DeepSeek-V3 with 671B parameters using a mixture-of-experts (MoE) architecture, which activates only parts of the model at a time to reduce compute costs.
Theorem solving: Uses long context windows (32K+ tokens) to generate detailed, step-by-step formal proofs for a wide range of math problems — from basic algebra to advanced calculus theorems.
Research grade: Assists mathematicians in testing new theorems automatically and helps students understand formal logic by generating both Lean 4 code and readable explanations.
New benchmark: Introduces ProverBench, a new 325-question benchmark set featuring problems from recent AIME exams and curated academic sources to evaluate mathematical reasoning.
The need for deep student engagement became clear at Dartmouth Geisel School of Medicine when a potential academic-integrity issue revealed gaps in its initial approach to artificial intelligence use in the classroom, leading to significant revisions to ensure equitable learning and assessment.
From George Siemens “SAIL: Transmutation, Assessment, Robots e-newsletter on 5/2/25
All indications are that AI, even if it stops advancing, has the capacity to dramatically change knowledge work. Knowing things matters less than being able to navigate and make sense of complex environments. Put another way, sensemaking, meaningmaking, and wayfinding (with their yet to be defined subelements) will be the foundation for being knowledgeable going forward.
That will require being able to personalize learning to each individual learner so that who they are (not what our content is) forms the pedagogical entry point to learning.(DSC: And I would add WHAT THEY WANT to ACHIEVE.)LLMs are particularly good and transmutation. Want to explain AI to a farmer? A sentence or two in a system prompt achieves that. Know that a learner has ADHD? A few small prompt changes and it’s reflected in the way the LLM engages with learning. Talk like a pirate. Speak in the language of Shakespeare. Language changes. All a matter of a small meta comment send to the LLM. I’m convinced that this capability to change, transmute, information will become a central part of how LLMS and AI are adopted in education.
… Speaking of Duolingo– it took them 12 years to develop 100 courses. In the last year, they developed an additional 148. AI is an accelerant with an impact in education that is hard to overstate. “Instead of taking years to build a single course with humans the company now builds a base course and uses AI to quickly customize it for dozens of different languages.”
FutureHouse is launching our platform, bringing the first publicly available superintelligent scientific agents to scientists everywhere via a web interface and API. Try it out for free at https://platform.futurehouse.org.
Encouraging Students’ Curiosity With Animal Observations — from edutopia.org by Shelby Guthrie Watching animals—either outdoors or via an online live cam—is an engaging way for students to build their critical thinking skills.
A moment of stillness can spark a lifetime of curiosity. Watching animals—whether it’s a bird outside or a zoo cam online—helps learners slow down, notice patterns, and ask questions. Structured observation builds patience, critical thinking, and a stronger connection to nature.
More than just engaging, these small moments contribute to bigger ideas—like understanding ecosystems, animal behavior, and the ever-changing world. Observation teaches learners to care, question, and conserve. These skills are foundational not only for scientific thinking but also for fostering empathy and awareness. When students observe closely, they begin to notice patterns, ask better questions, and connect deeply with the natural world. This kind of curiosity-driven learning empowers them to take informed action, whether that means advocating for a local habitat, participating in citizen science, or simply seeing their environment through a more thoughtful lens.
I set out to create some helpful tools and strategies for my students, who have diverse learning profiles, including processing delays, anxiety, attention challenges, and autism. I knew going in that whatever I came up with needed to be flexible. The goal was never perfection; it was access and agency.
What’s resulted are three classroom strategies that don’t feel like extra work for students. Instead, they help students own their attention. I named the strategies “The Listening Gym,” “The Noise Diet,” and “Focus GPS.”
Keeping Students Engaged During Long Class Periods — from edutopia.org by Maggie Espinola By chunking class time using gradual release of responsibility, teachers can vary their teaching strategies to help students maintain focus.
For a new teacher, figuring out how to manage a long class period can feel particularly daunting. In order to maximize the attention of your students during longer class periods, consider the following tips.
Creating a Student Leadership Program — from edutopia.org by Danica Derksen These strategies for building leadership skills can be implemented as an elective or by creating other opportunities for students.
Running an effective student leadership program takes structure and vision from all levels of a school. When we create opportunities for students to lead, we are building competencies that they will take with them for the rest of their lives.
I currently co-teach a middle school student leadership elective each semester. I am passionate about teaching my students to lead by example, find solutions to problems, and make their school a place where all students know they belong. Here are four ideas to create a strong student leadership program or other student leadership opportunities.
A market report from Validated Insights released this month notes that fewer colleges and universities hire external online program management (OPM) companies to develop their courses.
For 2024, higher education institutions launched only 81 new partnerships with OPMs — a drop of 42% and the lowest number since 2016.
The report showed that institutions increasingly pay OPMs a fee-for-service instead of following a revenue-sharing model with big service bundles and profit splits.
Experts say revenue-sharing models, which critics denounce as predatory arrangements, incentivize service providers to use aggressive recruiting tactics to increase enrollments and maximize tuition revenue.
According to the report, fee-for-service has become the dominant business model for OPMs.
While school-led professional development can be helpful, there are online professional learning communities on various edtech websites that can be leveraged. Also, some of these community spaces offer the chance to monetize your work.
Here is a summary of six online edtech professional learning spaces.
Over the course of the next 10 years, AI-powered institutions will rise in the rankings. US News & World Report will factor a college’s AI capabilities into its calculations. Accrediting agencies will assess the degree of AI integration into pedagogy, research, and student life. Corporations will want to partner with universities that have demonstrated AI prowess. In short, we will see the emergence of the AI haves and have-nots.
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What’s happening in higher education today has a name: creative destruction. The economist Joseph Schumpeter coined the term in 1942 to describe how innovation can transform industries. That typically happens when an industry has both a dysfunctional cost structure and a declining value proposition. Both are true of higher education.
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Out of the gate, professors will work with technologists to get AI up to speed on specific disciplines and pedagogy. For example, AI could be “fed” course material on Greek history or finance and then, guided by human professors as they sort through the material, help AI understand the structure of the discipline, and then develop lectures, videos, supporting documentation, and assessments.
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In the near future, if a student misses class, they will be able watch a recording that an AI bot captured. Or the AI bot will find a similar lecture from another professor at another accredited university. If you need tutoring, an AI bot will be ready to help any time, day or night. Similarly, if you are going on a trip and wish to take an exam on the plane, a student will be able to log on and complete the AI-designed and administered exam. Students will no longer be bound by a rigid class schedule. Instead, they will set the schedule that works for them.
Early and mid-career professors who hope to survive will need to adapt and learn how to work with AI. They will need to immerse themselves in research on AI and pedagogy and understand its effect on the classroom.
From DSC: I had a very difficult time deciding which excerpts to include. There were so many more excerpts for us to think about with this solid article. While I don’t agree with several things in it, EVERY professor, president, dean, and administrator working within higher education today needs to read this article and seriously consider what Scott Latham is saying.
Change is already here, but according to Scott, we haven’t seen anything yet. I agree with him and, as a futurist, one has to consider the potential scenarios that Scott lays out for AI’s creative destruction of what higher education may look like. Scott asserts that some significant and upcoming impacts will be experienced by faculty members, doctoral students, and graduate/teaching assistants (and Teaching & Learning Centers and IT Departments, I would add). But he doesn’t stop there. He brings in presidents, deans, and other members of the leadership teams out there.
There are a few places where Scott and I differ.
The foremost one is the importance of the human element — i.e., the human faculty member and students’ learning preferences. I think many (most?) students and lifelong learners will want to learn from a human being. IBM abandoned their 5-year, $100M ed push last year and one of the key conclusions was that people want to learn from — and with — other people:
To be sure, AI can do sophisticated things such as generating quizzes from a class reading and editing student writing. But the idea that a machine or a chatbot can actually teach as a human can, he said, represents “a profound misunderstanding of what AI is actually capable of.”
Nitta, who still holds deep respect for the Watson lab, admits, “We missed something important. At the heart of education, at the heart of any learning, is engagement. And that’s kind of the Holy Grail.”
— Satya Nitta, a longtime computer researcher at
IBM’s Watson Research Center in Yorktown Heights, NY .
By the way, it isn’t easy for me to write this. As I wanted AI and other related technologies to be able to do just what IBM was hoping that it would be able to do.
Also, I would use the term learning preferences where Scott uses the term learning styles.
Scott also mentions:
“In addition, faculty members will need to become technologists as much as scholars. They will need to train AI in how to help them build lectures, assessments, and fine-tune their classroom materials. Further training will be needed when AI first delivers a course.”
It has been my experience from working with faculty members for over 20 years that not all faculty members want to become technologists. They may not have the time, interest, and/or aptitude to become one (and vice versa for technologists who likely won’t become faculty members).
That all said, Scott relays many things that I have reflected upon and relayed for years now via this Learning Ecosystems blog and also via The Learning from the Living [AI-Based Class] Room vision — the use of AI to offer personalized and job-relevant learning, the rising costs of higher education, the development of new learning-related offerings and credentials at far less expensive prices, the need to provide new business models and emerging technologies that are devoted more to lifelong learning, plus several other things.
So this article is definitely worth your time to read, especially if you are working in higher education or are considering a career therein!
Google Public Sector and the University of Michigan’s Ross School of Business have launched an advanced Virtual Teaching Assistant pilot program aimed at improving personalized learning and enlightening educators on artificial intelligence in the classroom.
The AI technology, aided by Google’s Gemini chatbot, provides students with all-hours access to support and self-directed learning. The Virtual TA represents the next generation of educational chatbots, serving as a sophisticated AI learning assistant that instructors can use to modify their specific lessons and teaching styles.
The Virtual TA facilitates self-paced learning for students, provides on-demand explanations of complex course concepts, guides them through problem-solving, and acts as a practice partner. It’s designed to foster critical thinking by never giving away answers, ensuring students actively work toward solutions.
AI in Education Survey: What UK and US Educators Think in 2025 — from twinkl.com As artificial intelligence (AI) continues to shape the world around us, Twinkl conducted a large-scale survey between January 15th and January 22nd to explore its impact on the education sector, as well as the work lives of teachers across the UK and the USA.
Teachers’ use of AI for work continues to rise Twinkl’s survey asked teachers whether they were currently using AI for work purposes. Comparing these findings to similar surveys over recent years shows the use of AI tools by teachers has seen a significant increase across both the UK and USA.
According to two UK surveys by the National Literacy Trust – 30% of teachers used generative AI in 2023 and nearly half (47.7%) in 2024. Twinkl’s survey indicates that AI adoption continues to rise rapidly, with 60% of UK educators currently integrating it into their work lives in 2025.
Similarly, with 62% of US teachers currently using AI for work, uptake appears to have risen greatly in the past 12 months, with just 25% saying they were leveraging the new technology in the 2023-24 school year according to a RAND report.
Teachers are using AI more for work than in their personal lives: In the UK, personal usage drops to 43% (from 60% at school). In the US, 52% are using AI for non-work purposes (versus 62% in education settings).
60% of UK teachers and 62% of US teachers use AI in their work life in 2025.
The urgent task facing those of us who teach and advise students, whether they be degree program or certificate seeking, is to ensure that they are prepared to enter (or re-enter) the workplace with skills and knowledge that are relevant to 2025 and beyond. One of the first skills to cultivate is an understanding of what kinds of services this emerging technology can provide to enhance the worker’s productivity and value to the institution or corporation.
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Given that short period of time, coupled with the need to cover the scheduled information in the syllabus, I recommend that we consider merging AI use into authentic assignments and assessments, supplementary modules, and other resources to prepare for AI.
Learning Design in the Era of Agentic AI— from drphilippahardman.substack.com by Dr Philippa Hardman Aka, how to design online async learning experiences that learners can’t afford to delegate to AI agents
The point I put forward was that the problem is not AI’s ability to complete online async courses, but that online async courses courses deliver so little value to our learners that they delegate their completion to AI.
The harsh reality is that this is not an AI problem — it is a learning design problem.
However, this realisation presents us with an opportunity which we overall seem keen to embrace. Rather than seeking out ways to block AI agents, we seem largely to agree that we should use this as a moment to reimagine online async learning itself.
While fears of AI replacing educators swirl in the public consciousness, a cohort of pioneering institutions is demonstrating a far more nuanced reality. These eight universities and schools aren’t just experimenting with AI, they’re fundamentally reshaping their educational ecosystems. From personalized learning in K-12 to advanced research in higher education, these institutions are leveraging Google’s AI to empower students, enhance teaching, and streamline operations.
Essential AI tools for better work — from wondertools.substack.com by Jeremy Caplan My favorite tactics for making the most of AI — a podcast conversation
AI tools I consistently rely on (areas covered mentioned below)
Research and analysis
Communication efficiency
Multimedia creation
AI tactics that work surprisingly well
1. Reverse interviews Instead of just querying AI, have it interview you. Get the AI to interview you, rather than interviewing it. Give it a little context and what you’re focusing on and what you’re interested in, and then you ask it to interview you to elicit your own insights.”
This approach helps extract knowledge from yourself, not just from the AI. Sometimes we need that guide to pull ideas out of ourselves.
Blind Spot on AI — from the-job.beehiiv.com by Paul Fain Office tasks are being automated now, but nobody has answers on how education and worker upskilling should change.
Students and workers will need help adjusting to a labor market that appears to be on the verge of a historic disruption as many business processes are automated. Yet job projections and policy ideas are sorely lacking.
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The benefits of agentic AI are already clear for a wide range of organizations, including small nonprofits like CareerVillage. But the ability to automate a broad range of business processes means that education programs and skills training for knowledge workers will need to change. And as Chung writes in a must-read essay, we have a blind spot with predicting the impacts of agentic AI on the labor market.
“Without robust projections,” he writes, “policymakers, businesses, and educators won’t be able to come to terms with how rapidly we need to start this upskilling.”
As learning professionals, we help others grow—but how well are we developing ourselves? And does it really matter? Absolutely! In this article, I’ll explore why mastering the art of learning is crucial for our success and share strategies that go beyond traditional professional development.
Why learning matters for us
We need to be strong learners because our work demands broad expertise. We must understand the learning sciences, instructional design, project management, technology, evaluation, organizational dynamics, and business strategy. We also need to navigate a sea of learning frameworks, approaches, and models.
This shift in how we consume and process information is challenging traditional learning methods, which are finding it increasingly difficult to keep learners’ attention.
Microlearning is a bridge to the attention of today’s learners, delivering complex topics in short, manageable pieces. Whether it’s a five-minute video, a quick quiz, or a short lesson, microlearning makes it easier for students to stay engaged. Microlearning often holds learners’ attention better and for longer compared to standard learning methods.
Typical low completion rates clearly show the need for innovative approaches to content delivery and student engagement. Microlearning offers the answer to this need.
Instructional designers and learning professionals are creative by nature. We are called upon to be creative with technology like Articulate, Camtasia, or Captivate. More often than we would like, organizations, red tape, and clients require us to be creative with timelines and budgets. Being creative is a core qualification and requirement of our work. So, what do we do when we feel like the creative river has run to a trickle or dried up entirely?
