Highly Complementary Capabilities Will Create a Leading Technology Platform, Redefining Skills Discovery, Development, and Mastery for Learners and Organizations at Scale
Unites Udemy’s Dynamic AI-Powered Skills Development Marketplace with World-Class University and Industry Brands Under the Coursera Ecosystem, Expanding Value, Impact, and Choice Globally
Strengthens Combined Company’s Financial Profile with Pro Forma Annual Revenue of More Than $1.5 Billion and Anticipated Annual Run-Rate Cost Synergies of $115 Million Within 24 Months
“We’re at a pivotal moment in which AI is rapidly redefining the skills required for every job across every industry. Organizations and individuals around the world need a platform that is as agile as the new and emerging skills learners must master,” said Greg Hart, CEO of Coursera. “By combining the highly complementary strengths of Coursera and Udemy, we will be in an even stronger position to address the global talent transformation opportunity, unlock a faster pace of innovation, and deliver valuable experiences and outcomes for our learners and customers. Together, we will ensure our millions of learners, thousands of enterprise, university, and government customers, and expert instructors have a platform to keep pace with technology acceleration.”
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.
At Adobe MAX 2025 in Los Angeles, the company dropped an entire creative AI ecosystem that touches every single part of the creative workflow. In our opinion, all these new features aren’t about replacing creators; it’s about empowering them with superpowers they can actually control.
Adobe’s new plan is to put an AI co-pilot in every single app.
For professionals, the game-changer is Firefly Custom Models. Start training one now to create a consistent, on-brand look for all your assets.
For everyday creators, the AI Assistants in Photoshop and Express will drastically speed up your workflow.
The best place to start is the Photoshop AI Assistant (currently in private beta), which offers a powerful glimpse into the future of creative software—a future where you’re less of a button-pusher and more of a creative director.
Adobe MAX Day 2: The Storyteller Is Still King, But AI Is Their New Superpower — from theneuron.ai by Grant Harvey Adobe’s Day 2 keynote showcased a suite of AI-powered creative tools designed to accelerate workflows, but the real message from creators like Mark Rober and James Gunn was clear: technology serves the story, not the other way around.
On the second day of its annual MAX conference, Adobe drove home a message that has been echoing through the creative industry for the past year: AI is not a replacement, but a partner. The keynote stage featured a powerful trio of modern storytellers—YouTube creator Brandon Baum, science educator and viral video wizard Mark Rober, and Hollywood director James Gunn—who each offered a unique perspective on a shared theme: technology is a powerful tool, but human instinct, hard work, and the timeless art of storytelling remain paramount.
From DSC: As Grant mentioned, the demos dealt with ideation, image generation, video generation, audio generation, and editing.
The creative software giant is launching new generative AI tools that make digital voiceovers and custom soundtracks for videos, and adding AI assistants to Express and Photoshop for web that edit entire projects using descriptive prompts. And that’s just the start, because Adobe is planning to eventually bring AI assistants to all of its design apps.
My take is this: in all of the anxiety lies a crucial and long-overdue opportunity to deliver better learning experiences. Precisely because Atlas perceives the same context in the same moment as you, it can transform learning into a process aligned with core neuro-scientific principles—including active retrieval, guided attention, adaptive feedback and context-dependent memory formation.
Perhaps in Atlas we have a browser that for the first time isn’t just a portal to information, but one which can become a co-participant in active cognitive engagement—enabling iterative practice, reflective thinking, and real-time scaffolding as you move through challenges and ideas online.
With this in mind, I put together 10 use cases for Atlas for you to try for yourself.
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6. Retrieval Practice
What: Pulling information from memory drives retention better than re-reading. Why: Practice testing delivers medium-to-large effects (Adesope et al., 2017). Try: Open a document with your previous notes. Ask Atlas for a mixed activity set: “Quiz me on the Krebs cycle—give me a near-miss, high-stretch MCQ, then a fill-in-the-blank, then ask me to explain it to a teen.” Atlas uses its browser memory to generate targeted questions from your actual study materials, supporting spaced, varied retrieval.
From DSC: A quick comment. I appreciate these ideas and approaches from Katarzyna and Rita. I do think that someone is going to want to be sure that the AI models/platforms/tools are given up-to-date information and updated instructions — i.e., any new procedures, steps to take, etc. Perhaps I’m missing the boat here, but an internal AI platform is going to need to have access to up-to-date information and instructions.
That gap creates compliance risk and wasted investment. It leaves HR leaders with a critical question: How do you measure and validate real learning when AI is doing the work for employees?
Designing Training That AI Can’t Fake
Employees often find static slide decks and multiple-choice quizzes tedious, while AI can breeze through them. If employees would rather let AI take training for them, it’s a red flag about the content itself.
One of the biggest risks with agentic AI is disengagement. When AI can complete a task for employees, their incentive to engage disappears unless they understand why the skill matters, Rashid explains. Personalization and context are critical. Training should clearly connect to what employees value most – career mobility, advancement, and staying relevant in a fast-changing market.
Nearly half of executives believe today’s skills will expire within two years, making continuous learning essential for job security and growth. To make training engaging, Rashid recommends:
Delivering content in formats employees already consume – short videos, mobile-first modules, interactive simulations, or micro-podcasts that fit naturally into workflows. For frontline workers, this might mean replacing traditional desktop training with mobile content that integrates into their workday.
Aligning learning with tangible outcomes, like career opportunities or new responsibilities.
Layering in recognition, such as digital badges, leaderboards, or team shout-outs, to reinforce motivation and progress
Microsoft is pitching a recent shift of AI agents in Microsoft Teams as more than just smarter assistance. Instead, these agents are built to behave like human teammates inside familiar apps such as Teams, SharePoint, and Viva Engage. They can set up meeting agendas, keep files in order, and even step in to guide community discussions when things drift off track.
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Unlike tools such as ChatGPT or Claude, which mostly wait for prompts, Microsoft’s agents are designed to take initiative. They can chase up unfinished work, highlight items that still need decisions, and keep projects moving forward. By drawing on Microsoft Graph, they also bring in the right files, past decisions, and context to make their suggestions more useful.
As an advisor to Aibrary, I am impressed with their educational philosophy, which is based both on theory and on empirical research findings. Aibrary is an innovative approach to self-directed learning that complements academic resources. Expanding our historic conceptions of books, libraries, and lifelong learning to new models enabled by emerging technologies is central to empowering all of us to shape our future. .
Why AI literacy must come before policy — from timeshighereducation.com by Kathryn MacCallum and David Parsons When developing rules and guidelines around the uses of artificial intelligence, the first question to ask is whether the university policymakers and staff responsible for implementing them truly understand how learners can meet the expectations they set
Literacy first, guidelines second, policy third
For students to respond appropriately to policies, they need to be given supportive guidelines that enact these policies. Further, to apply these guidelines, they need a level of AI literacy that gives them the knowledge, skills and understanding required to support responsible use of AI. Therefore, if we want AI to enhance education rather than undermine it, we must build literacy first, then create supportive guidelines. Good policy can then follow.
Sept 22 (Reuters) – At orientation last month, 375 new Fordham Law students were handed two summaries of rapper Drake’s defamation lawsuit against his rival Kendrick Lamar’s record label — one written by a law professor, the other by ChatGPT.
The students guessed which was which, then dissected the artificial intelligence chatbot’s version for accuracy and nuance, finding that it included some irrelevant facts.
The exercise was part of the first-ever AI session for incoming students at the Manhattan law school, one of at least eight law schools now incorporating AI training for first-year students in orientation, legal research and writing courses, or through mandatory standalone classes.
In this episode, we explore why digital accessibility can be so important to the student experience. My guest is Amy Lomellini, director of accessibility at Anthology, the company that makes the learning management system Blackboard. Amy teaches educational technology as an adjunct at Boise State University, and she facilitates courses on digital accessibility for the Online Learning Consortium. In our conversation, we talk about the importance of digital accessibility to students, moving away from the traditional disclosure-accommodation paradigm, AI as an assistive technology, and lots more.
Miro and GenAI as drivers of online student engagement — from timeshighereducation.com by Jaime Eduardo Moncada Garibay A set of practical strategies for transforming passive online student participation into visible, measurable and purposeful engagement through the use of Miro, enhanced by GenAI
To address this challenge, I shifted my focus from requesting participation to designing it. This strategic change led me to integrate Miro, a visual digital workspace, into my classes. Miro enables real-time visualisation and co-creation of ideas, whether individually or in teams.
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The transition from passive attendance to active engagement in online classes requires deliberate instructional design. Tools such as Miro, enhanced by GenAI, enable educators to create structured, visually rich learning environments in which participation is both expected and documented.
While technology provides templates, frames, timers and voting features, its real pedagogical value emerges through intentional facilitation, where the educator’s role shifts from delivering content to orchestrating collaborative, purposeful learning experiences.
In the past, it was typical for faculty to teach online courses as an “overload” of some kind, but BOnES data show that 92% of online programs feature courses taught as part of faculty member’s standard teaching responsibilities. Online teaching has become one of multiple modalities in which faculty teach regularly.
Three-quarters of chief online officers surveyed said they plan to have a great market share of online enrollments in the future, but only 23% said their current marketing is better than their competitors. The rising tide of online enrollments won’t lift all boats–some institutions will fare better than others.
Staffing at online education units is growing, with the median staff size increasing from 15 last year to 20 this year. Julie pointed out that successful online education requires investment of resources. You might need as many buildings as onsite education does, but you need people and you need technology.
SINGAPORE Sept. 3, 2025 /PRNewswire/ — Today, Midoo AIproudly announces the launch of the world’s first AI language learning agent, a groundbreaking innovation set to transform language education forever.
For decades, language learning has pursued one ultimate goal: true personalization. Traditional tools offered smart recommendations, gamified challenges, and pre-written role-play scripts—but real personalization remained out of reach. Midoo AI changes that. Here is the >launch video of Midoo AI.
Imagine a learning experience that evolves with you in real time. A system that doesn’t rely on static courses or scripts but creates a dynamic, one-of-a-kind language world tailored entirely to your needs. This is the power of Midoo’s Dynamic Generation technology.
“Midoo is not just a language-learning tool,” said Yvonne, co-founder of Midoo AI. “It’s a living agent that senses your needs, adapts instantly, and shapes an experience that’s warm, personal, and alive. Learning is no longer one-size-fits-all—now, it’s yours and yours alone.”
Language learning apps have traditionally focused on exercises, quizzes, and progress tracking. Midoo AI introduces a different approach. Instead of presenting itself as a course provider, it acts as an intelligent learning agent that builds, adapts, and sustains a learner’s journey.
This review examines how Midoo AI operates, its feature set, and what makes it distinct from other AI-powered tutors.
Midoo AI in Context: Purpose and Position
Midoo AI is not structured around distributing lessons or modules. Its core purpose is to provide an agent-like partner that adapts in real time. Where many platforms ask learners to select a “level” or “topic,”
Midoo instead begins by analyzing goals, usage context, and error patterns. The result is less about consuming predesigned units and more about co-constructing a pathway.
Turning Time Saved Into Better Learning
AI can save teachers time, but what can that time be used for (besides taking a breath)? For most of us, it means redirecting energy into the parts of teaching that made us want to pursue this profession in the first place: connecting with our students and helping them grow academically.
Differentiation Every classroom has students with different readiness levels, language needs, and learning preferences. AI tools like Diffit or MagicSchool can instantly create multiple versions of a passage or assignment, differentiated by grade level, complexity, or language. This allows every student to engage with the same core concept, moving together as one cohesive class. Instead of spending an evening retyping and rephrasing, teachers can review and tweak AI drafts in minutes, ready for the next lesson.
Mass Intelligence — from oneusefulthing.org by Ethan Mollick From GPT-5 to nano banana: everyone is getting access to powerful AI
When a billion people have access to advanced AI, we’ve entered what we might call the era of Mass Intelligence. Every institution we have — schools, hospitals, courts, companies, governments — was built for a world where intelligence was scarce and expensive. Now every profession, every institution, every community has to figure out how to thrive with Mass Intelligence. How do we harness a billion people using AI while managing the chaos that comes with it? How do we rebuild trust when anyone can fabricate anything? How do we preserve what’s valuable about human expertise while democratizing access to knowledge?
By the time today’s 9th graders and college freshman enter the workforce, the most disruptive waves of AGI and robotics may already be embedded into part society.
What replaces the old system will not simply be a more digital version of the same thing. Structurally, schools may move away from rigid age-groupings, fixed schedules, and subject silos. Instead, learning could become more fluid, personalized, and interdisciplinary—organized around problems, projects, and human development rather than discrete facts or standardized assessments.
AI tutors and mentors will allow for pacing that adapts to each student, freeing teachers to focus more on guidance, relationships, and high-level facilitation. Classrooms may feel less like miniature factories and more like collaborative studios, labs, or even homes—spaces for exploring meaning and building capacity, not just delivering content.
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If students are no longer the default source of action, then we need to teach them to:
Design agents,
Collaborate with agents,
Align agentic systems with human values,
And most of all, retain moral and civic agency in a world where machines act on our behalf.
We are no longer educating students to be just doers.
We must now educate them to be judges, designers, and stewards of agency.
Meet Your New AI Tutor — from wondertools.substack.com by Jeremy Caplan Try new learning modes in ChatGPT, Claude, and Gemini
AI assistants are now more than simple answer machines. ChatGPT’s new Study Mode, Claude’s Learning Mode, and Gemini’s Guided Learningrepresent a significant shift. Instead of just providing answers, these free tools act as adaptive, 24/7 personal tutors.
That’s why, in preparation for my next bootcamp which kicks off September 8th 2025, I’ve just completed a full refresh of my list of the most powerful & popular AI tools for Instructional Designers, complete with tips on how to get the most from each tool.
The list has been created using my own experience + the experience of hundreds of Instructional Designers who I work with every week.
It contains the 50 most powerful AI tools for instructional design available right now, along with tips on how to optimise their benefits while mitigating their risks.
Addendums on 9/4/25:
AI Companies Roll Out Educational Tools — from insidehighered.com by Ray Schroeder This fall, Google, Anthropic and OpenAI are rolling out powerful new AI tools for students and educators, each taking a different path to shape the future of learning.
So here’s the new list of essential skills I think my students will need when they are employed to work with AI five years from now:
They can follow directions, analyze outcomes, and adapt to change when needed.
They can write or edit AI to capture a unique voice and appropriate tone in sync with an audience’s needs
They have a deep understanding of one or more content areas of a particular profession, business, or industry, so they can easily identify factual errors.
They have a strong commitment to exploration, a flexible mindset, and a broad understanding of AI literacy.
They are resilient and critical thinkers, ready to question results and demand better answers.
They are problem solvers.
And, of course, here is a new rubric built on those skills:
The Online Education Marketplace Is Increasingly Competitive: …
Alternative Credentials Take Center Stage: …
AI Integration Lacks Strategic Coordination: …
Just 28% of faculty are considered fully prepared for online course design, and 45% for teaching. Alarmingly, only 28% of institutions report having fully developed academic continuity plans for future emergency pivots to online.
Cultural resistance remains strong. Many [Chief Online Learning Officers] COLOs say faculty and deans still believe in-person learning is “just better,” creating headwinds even for modest online growth. As one respondent at a four-year institution with a large online presence put it:
Supportive departments [that] see the value in online may have very different levels of responsiveness compared to academic departments [that] are begrudgingly online. There is definitely a growing belief that students “should” be on-ground and are only choosing online because it’s easy/ convenient. Never mind the very real and growing population of nontraditional learners who can only take online classes, and the very real and growing population of traditional-aged learners who prefer online classes; many faculty/deans take a paternalistic, “we know what’s best” approach.
… Ultimately, what we need is not just more ambition but better ambition. Ambition rooted in a realistic understanding of institutional capacity, a shared strategic vision, investments in policy and infrastructure, and a culture that supports online learning as a core part of the academic mission, not an auxiliary one. It’s time we talked about what it really takes to grow online learning , and where ambition needs to be matched by structure.
From DSC: Yup. Culture is at the breakfast table again…boy, those strategies taste good.
I’d like to take some of this report — like the graphic below — and share it with former faculty members and members of a couple of my past job families’ leadership. They strongly didn’t agree with us when we tried to advocate for the development of online-based learning/programs at our organizations…but we were right. We were right all along. And we were LEADING all along. No doubt about it — even if the leadership at the time said that we weren’t leading.
The cultures of those organizations hurt us at the time. But our cultivating work eventually led to the development of online programs — unfortunately, after our groups were disbanded, they had to outsource those programs to OPMs.
Arizona State University — with its dramatic growth in online-based enrollments.
Millions of college students around the world are getting ready to start classes. To help make the school year even better, we’re making our most advanced AI tools available to them for free, including our new Guided Learning mode. We’re also providing $1 billion to support AI education and job training programs and research in the U.S. This includes making our AI and career training free for every college student in America through our AI for Education Accelerator — over 100 colleges and universities have already signed up.
… Guided Learning: from answers to understanding
AI can broaden knowledge and expand access to it in powerful ways, helping anyone, anywhere learn anything in the way that works best for them. It’s not about just getting an answer, but deepening understanding and building critical thinking skills along the way. That opportunity is why we built Guided Learning, a new mode in Gemini that acts as a learning companion guiding you with questions and step-by-step support instead of just giving you the answer. We worked closely with students, educators, researchers and learning experts to make sure it’s helpful for understanding new concepts and is backed by learning science.
In this post, part of the UsableNet 25th anniversary series, I’m taking a look at where things stand in 2025. I’ll discuss the areas that have improved—such as online shopping, banking, and social media—and the ones that still make it challenging to perform basic tasks, including travel, healthcare, and mobile apps. I hope that by sharing what works and what doesn’t, I can help paint a clearer picture of the digital world as it stands today.
On June 28, 2025, the European Accessibility Act (EAA) officially became enforceable across the European Union. This law requires digital products and services—including websites, mobile apps, e-commerce platforms, and software to meet the defined accessibility standards outlined in EN 301 549, which aligns with the WCAG 2.1 Level AA.
Companies that serve EU consumers must be able to demonstrate that accessibility is built into the design, development, testing, and maintenance of their digital products and services.
This milestone also arrives as UsableNet celebrates 25 years of accessibility leadership—a moment to reflect on how far we’ve come and what digital teams must do next.
Building a learning ecosystem that drives business results — from chieflearningofficer.com by Nick Romanowski How SAX combined adaptive e-learning and experiential workshops to accelerate capability development and impact the bottom line.
At SAX, we know that to succeed in today’s market, we need professionals who can learn quickly, apply that learning effectively and continuously adapt as client needs evolve.
Yet traditional training methods were no longer enough. Our firm faced familiar challenges: helping staff meet continuing professional education requirements efficiently, uncovering knowledge gaps to guide development and building a more capable, more client-ready workforce.
We found our solution in a flipped learning model that blends adaptive e-learning with live, experiential workshops. The results were transformative. We accelerated CPE credit completion by more than 50 percent, reclaimed 173 billable hours and equipped our people with deeper capabilities.
Here’s how we did it, and what we learned along the way.
Blend technology and human touch: Adaptive e-learning addresses individual knowledge gaps efficiently. Live workshops enable skill development through practice and feedback. Together, they drive both learning efficiency and behavior change.
I also pondered what functions blogging has provided for me over the years.
Continuity – as an individual you persist across multiple organisations, roles and jobs. Although I stayed in one institution, I had many roles and the blog wasn’t associated with one specific project. Now I have left it continues.
Holistic – you can blog about one topic, but over time I think some personality will creep in. You are not just one thing, you have a personal life, tastes, interests etc which will all feed into what you do. A blog allows this more rounded representation.
Experimentation – there is relatively low cost and risk for much of it (this may not be the case for many people online, we need to acknowledge), so you can try things, and if they don’t work, so what? Also you can try formats that conventional outlets might not be appropriate for.
Development – the blog has been both an intentional and unintentional vehicle for working up ideas, documenting the process and getting feedback, which have led to more substantial outputs, such as books, project proposals and papers. Most importantly though it has been the means through which I have continually developed writing.
Connecting – particularly in those halcyon early days, it was a good way of finding others, working on ideas together, sharing something of yourself. A lot of my career related personal friendships have resulted from blogging.
Publicity – I became at one point (the OU crisis of 2018) something of a public voice of the OU, and have often used the blog for projects such as GO-GN
That’s not a bad return for a lil’ ol’ blog. I couldn’t say the same for academic journals.
Intellectual rigor comes from the journey: the dead ends, the uncertainty, and the internal debate. Skip that, and you might still get the insight–but you’ll have lost the infrastructure for meaningful understanding. Learning by reading LLM output is cheap. Real exercise for your mind comes from building the output yourself.
The irony is that I now know more than I ever would have before AI. But I feel slightly dumber. A bit more dull. LLMs give me finished thoughts, polished and convincing, but none of the intellectual growth that comes from developing them myself.
Every few months I put together a guide on which AI system to use. Since I last wrote my guide, however, there has been a subtle but important shift in how the major AI products work. Increasingly, it isn’t about the best model, it is about the best overall system for most people. The good news is that picking an AI is easier than ever and you have three excellent choices. The challenge is that these systems are getting really complex to understand. I am going to try and help a bit with both.
First, the easy stuff.
Which AI to Use For most people who want to use AI seriously, you should pick one of three systems: Claude from Anthropic, Google’s Gemini, and OpenAI’s ChatGPT.
This summer, I tried something new in my fully online, asynchronous college writing course. These classes have no Zoom sessions. No in-person check-ins. Just students, Canvas, and a lot of thoughtful design behind the scenes.
One activity I created was called QuoteWeaver—a PlayLab bot that helps students do more than just insert a quote into their writing.
It’s a structured, reflective activity that mimics something closer to an in-person 1:1 conference or a small group quote workshop—but in an asynchronous format, available anytime. In other words, it’s using AI not to speed students up, but to slow them down.
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The bot begins with a single quote that the student has found through their own research. From there, it acts like a patient writing coach, asking open-ended, Socratic questions such as:
What made this quote stand out to you?
How would you explain it in your own words?
What assumptions or values does the author seem to hold?
How does this quote deepen your understanding of your topic?
It doesn’t move on too quickly. In fact, it often rephrases and repeats, nudging the student to go a layer deeper.
On [6/13/25], UNESCO published a piece I co-authored with Victoria Livingstone at Johns Hopkins University Press. It’s called The Disappearance of the Unclear Question, and it’s part of the ongoing UNESCO Education Futures series – an initiative I appreciate for its thoughtfulness and depth on questions of generative AI and the future of learning.
Our piece raises a small but important red flag. Generative AI is changing how students approach academic questions, and one unexpected side effect is that unclear questions – for centuries a trademark of deep thinking – may be beginning to disappear. Not because they lack value, but because they don’t always work well with generative AI. Quietly and unintentionally, students (and teachers) may find themselves gradually avoiding them altogether.
Of course, that would be a mistake.
We’re not arguing against using generative AI in education. Quite the opposite. But we do propose that higher education needs a two-phase mindset when working with this technology: one that recognizes what AI is good at, and one that insists on preserving the ambiguity and friction that learning actually requires to be successful.
By leveraging generative artificial intelligence to convert lengthy instructional videos into micro-lectures, educators can enhance efficiency while delivering more engaging and personalized learning experiences.
Researchers at Massachusetts Institute of Technology (MIT) have now devised a way for LLMs to keep improving by tweaking their own parameters in response to useful new information.
The work is a step toward building artificial intelligence models that learn continually—a long-standing goal of the field and something that will be crucial if machines are to ever more faithfully mimic human intelligence. In the meantime, it could give us chatbots and other AI tools that are better able to incorporate new information including a user’s interests and preferences.
The MIT scheme, called Self Adapting Language Models (SEAL), involves having an LLM learn to generate its own synthetic training data and update procedure based on the input it receives.
Edu-Snippets — from scienceoflearning.substack.com by Nidhi Sachdeva and Jim Hewitt Why knowledge matters in the age of AI; What happens to learners’ neural activity with prolonged use of LLMs for writing
Highlights:
Offloading knowledge to Artificial Intelligence (AI) weakens memory, disrupts memory formation, and erodes the deep thinking our brains need to learn.
Prolonged use of ChatGPT in writing lowers neural engagement, impairs memory recall, and accumulates cognitive debt that isn’t easily reversed.
Here are some incredibly powerful numbers from Mary Meeker’s AI Trends report, which showcase how artificial intelligence as a tech is unlike any other the world has ever seen.
AI took only three years to reach 50% user adoption in the US; mobile internet took six years, desktop internet took 12 years, while PCs took 20 years.
ChatGPT reached 800 million users in 17 months and 100 million in only two months, vis-à-vis Netflix’s 100 million (10 years), Instagram (2.5 years) and TikTok (nine months).
ChatGPT hit 365 billion annual searches in two years (2024) vs. Google’s 11 years (2009)—ChatGPT 5.5x faster than Google.
Above via Mary Meeker’s AI Trend-Analysis — from getsuperintel.com by Kim “Chubby” Isenberg How AI’s rapid rise, efficiency race, and talent shifts are reshaping the future.
The TLDR
Mary Meeker’s new AI trends report highlights an explosive rise in global AI usage, surging model efficiency, and mounting pressure on infrastructure and talent. The shift is clear: AI is no longer experimental—it’s becoming foundational, and those who optimize for speed, scale, and specialization will lead the next wave of innovation.
The Rundown: Meta aims to release tools that eliminate humans from the advertising process by 2026, according to a report from the WSJ — developing an AI that can create ads for Facebook and Instagram using just a product image and budget.
The details:
Companies would submit product images and budgets, letting AI craft the text and visuals, select target audiences, and manage campaign placement.
The system will be able to create personalized ads that can adapt in real-time, like a car spot featuring mountains vs. an urban street based on user location.
The push would target smaller companies lacking dedicated marketing staff, promising professional-grade advertising without agency fees or skillset.
Advertising is a core part of Mark Zuckerberg’s AI strategy and already accounts for 97% of Meta’s annual revenue.
Why it matters: We’re already seeing AI transform advertising through image, video, and text, but Zuck’s vision takes the process entirely out of human hands. With so much marketing flowing through FB and IG, a successful system would be a major disruptor — particularly for small brands that just want results without the hassle.
