From DSC:
The vision that I’ve been tracking for well over a decade begins with this graphic:
.
Could LearnVector be this next-generation company/platform? Perhaps. Time will tell.
Per Matt Tower via The EdSheet Vol. 38 dated 7/31/26:
What China’s Calligraphy Lessons Can Teach Us About AI — from linkedin.com by Rebecca Winthrop
He recalled visiting a primary school classroom in China several years ago where students were practicing calligraphy—the centuries-old art of writing Chinese characters with brush and ink. While the scene appeared entirely traditional, there was something innovative happening beneath the surface. Embedded in students’ desks was an AI system capable of providing immediate feedback on their brushwork, noting misplaced strokes and suggesting corrections.
The image is striking. Students were learning in much the same way generations before them had learned—brush in hand, immersed in a deeply human and cultural practice. Yet AI was quietly enhancing the experience by providing immediate, personalized feedback that would have been difficult for any teacher to provide to her whole class.
That example captures something important for me. The most promising uses of AI may not be the applications that place technology front and center, but those that support learners to do what they have always done: connect with educators, practice with concentration, and learn and grow.
AI Can Build Your Course, but Can it Design the Learning? — from drphilippahardman.substack.com by Dr. Philippa Hardman
Aka, the current state of AI’s instructional design ability & what it means for L&D
So these models have learned design artefacts in extraordinary volume and design method barely at all. They know what e-learning looks like. They have very little access to why any of it looks that way and whether it actually works or not.
That produces a completely consistent behaviour: ask an LLM for an e-learning module and it generates the statistical centre of every e-learning module it has ever seen. What AI produces isn’t the output of a design process. It’s the output of a sampling process — the statistical centre of every e-learning module it has ever seen.
…
So the honest conclusion isn’t that AI can design learning; it’s that AI executes design well when someone who already knows what to ask for is doing the asking and the checking.
This finding gives us a clearer division of labour than we’ve had before and with it a clearer picture of what the AI-enhanced workflow might look like:
The Pedagogy of Trading Places: Lessons from an Unexpected Role Reversal with AI — from facultyfocus.com by Sherrie Myers Bartell, PhD
These disruptions matter. They remind us that teaching is not a static identity but a dynamic posture. They show us that our pedagogical selves are not fixed; they are responsive to context, energy, and attention. They reveal that the qualities we value most in our teaching—curiosity, metaphor, play, and expansiveness—are not automatic. They require care and cultivation.
The AI didn’t “teach” me in the traditional sense. But it did something pedagogically adjacent: it surprised me back into myself.
AI & Accessibility: 5 Practical Ways to Use LLMs to Support Inclusive Learning — from learningguild.com by Dr. Athena Joyce Stanley (B.A., M.A.E., Ph.D.)
The following strategies focus on how instructional designers can design for accessibility by embedding or enabling AI-supported tools within learning experiences. These approaches move beyond access to content and begin to support access to understanding, expression, and participation.
…
Personalized Clarification Through AI Copilots
Learners often need additional explanation, but may hesitate to ask questions in live sessions or require more individualized support.
Instructional designers can integrate AI copilots, LLM-powered conversational interfaces, into learning environments to provide on-demand clarification. These tools can act as digital coaches, helping learners understand concepts through simplified explanations and relevant examples.
This approach supports learners who benefit from additional processing time and personalized guidance.
Sample Prompt (for IDs Configuring Copilot Behavior)
You are a workplace learning coach.
Explain the following concept in a clear, simple way:
Then ask one follow-up question to check for understanding.
Teaching AI Literacy with Susan Ray — from intentionalteaching.buzzsprout.com by Derek Bruff and Susan Ray
Per Derek Bruff:
In my conversation with Susan, we talk about that syllabus activity, as well as the AI transparency journals she asks her students to keep during her courses. We also talk about how her personal background prepared her to navigate the challenges and opportunities that AI has posed to her teaching, how she approaches assessing student learning in this age of AI—especially in her online asynchronous courses—and much more.
Building the AI-Native University: Student Success and Lifelong Learning — from coursera.org
In this episode, you’ll discover:
We’re Raising the First AI Generation. They’re Pushing Back. (Rebecca Winthrop, Brookings) — from humanistxyz.substack.com by Allison Dulin Salisbury
“Young people harbor real anger about AI because they’re already experiencing its consequences in their schools, relationships, job prospects, and feeds.”
As Rebecca told me:
“Kids harbor real anger. They’re pissed about climate impacts. They’re upset about job prospects. They’re outraged about AI being used to plagiarize writing and produce counterfeit artwork. Their relationships are being affected, and they’re seeing deepfakes in their feeds.”
Our conversation explores what follows from taking those concerns seriously. We discuss cognitive stunting, the case for small, purpose-built AI models over frontier models in education, why students should spend far more time in explorer mode, and how schools can cultivate agency, curiosity, and independent thinking in an age of AI.
The interview ends with one of Rebecca’s most practical recommendations: every school should have a student AI council, not as a symbolic gesture, but with real influence over the tools their schools adopt, the ways they’re used, and the data students are asked to hand over.
Every generation of technology seems to relearn the same lesson: build with people, not for them. AI should be no exception.
The image model race just split into four lanes –> find which model fits your work — from heatherbcooper.substack.com by Heather B. Cooper
In today’s edition:
Cybersecurity Researchers Identify First Fully Autonomous AI-Driven Ransomware Attack — from campustechnology.com by John K. Waters
Key Takeaways
Researchers hid a prompt injection inside a PNG, and AI fell for it — from digitaltrends.com by Shimul Sood
A team of security researchers (professor Sudipta Chattopadhyay and researcher Murali Ediga) has demonstrated an unusual attack that doesn’t target the AI model directly. Instead, it targets what the AI doesn’t pay enough attention to during code reviews. Rather than hiding malicious instructions in lines of code, the researchers tucked them inside an image file. Since many AI review tools treat images as decorative assets rather than as something worth inspecting, the pull request can appear perfectly harmless and sail through the review.
They argue that AI review tools need to become “multimodal” in the truest sense — treating images, documentation, configuration files, and other non-code assets with the same level of scrutiny as source code. If an AI can read a picture, it also needs to understand that the picture could be trying to manipulate it. For developers, this is another reminder that AI coding tools still need supervision. They can dramatically speed up software development, but they also open entirely new attack surfaces that didn’t exist before. The next security risk might not be hidden in thousands of lines of code — it could be sitting inside an image that nobody thought was worth opening.
AI has already fallen into the wrong hands and they’re using it to make bombs — from digitaltrends.com by Shimul Sood
Artificial intelligence has quickly become the go-to tool for everything from writing emails and summarizing meetings to helping students study or developers debug code. But the same technology that saves people time can also be misused, and a new report suggests that terrorist organizations are finding ways to do exactly that.
According to a research paper shared with The New York Times ahead of its publication, researchers found evidence that members of Boko Haram have been using popular AI chatbots to support both day-to-day activities and combat-related tasks. Interviews with 27 former members conducted in Nigeria over the past two years suggest that tools such as ChatGPT, Gemini, Claude, Grok, Meta AI, and DeepSeek were used to gather technical information, troubleshoot weapons, and even assist with planning attacks.
This wasn’t just a few bad actors messing around
What makes the findings especially concerning is that this wasn’t described as the work of a few individuals experimenting with AI. The report claims the group’s use of AI had become organized, with dedicated teams, internal training, and knowledge shared between members. Researchers also say some users managed to bypass built-in safety protections designed to prevent AI from responding to requests related to violence.
Microsoft Discovery Platform Brings Agentic AI to Scientific Research — from campustechnology.com by Chris Paoli
Key Takeaways
“Teachers ban it. Employers demand it.”
Also relevant/see:
The Shifting Career Ladder — from nafez.substack.com by Nafez Dakkak
AI is changing how work works and quietly removing the pathways through which young people learn to become experts.
AI is reshaping how people build skills, enter professions, and move along the career ladder and through the labour market.
In this conversation, I sit down with Matt Sigelmen founder of LightCast and now the President of Burning Glass Institute. Matt has dedicated his career to understanding the labor market and helping society improve the connections within in it.
Matt and I explore why people and opportunities are often only “a few skills apart,” why entry-level work may be losing its traditional role as the first rung of expertise, and why schools, universities, and employers now need to rethink the pathways that turn potential into mastery.
Educators need to be deeply aligned with what these changes are, and they need to shift the AI discourse from “how” questions to “what” questions. What do we need to teach? What do we need to keep in the curriculum?
The Law School Deans Driving AI Innovation in Legal Education — from natlawreview.com by Shivani Vedhere, AI & the Law Newsletter; via Colin S. Levy
Artificial intelligence is no longer a peripheral issue for legal education. It is quickly becoming one of the central questions facing law schools: how to prepare future lawyers for a profession in which AI will affect research, client counseling, litigation strategy, access to justice, and the business of law.
For decades, law schools treated legal technology as an elective or a niche interest for students already inclined toward innovation. That era is ending. Law firms are adopting AI tools at scale and even investing in developing their own tools. Clients are asking harder questions about efficiency, cost, and competence. Courts are sanctioning lawyers and litigants for AI-generated hallucinations, with the number of identified cases in the United States now exceeding 1,000. Students entering the profession will be expected to keep up with this rapidly changing landscape.
The most forward-looking law schools are responding accordingly. That transformation is being driven in large part by a group of innovative law school deans who are treating AI not as a passing trend, but as a structural change in legal education.
These initiatives signal a broader shift in legal academia where law schools are no longer merely debating whether AI belongs in the curriculum. The more pressing question is how deeply, how early, and how responsibly AI should be integrated into legal education.
A Comprehensive Report on Teens, Tweens, and AI — from commonsensemedia.org
To find out what that actually looks like day-to-day, we surveyed more than a thousand 9- to 17-year-olds across the country. We asked them how they use AI, how often, and for what.
The Common Sense Media Census: AI Use by Tweens and Teens (2026) is the first in a series we’ll repeat every year to learn how this generation’s relationship with AI evolves over time.
A few things stood out:
Instructional Design Trends: What’s Shaping The Future Of Learning? — from elearningindustry.com by Christopher Pappas
Table of contents
1. Why Instructional Design Is Entering A New Era
2. The State Of Instructional Design Today
3. Top Instructional Design Trends Shaping 2026
4. The Future Of Instructional Design And Technology
Also from elearningindustry.com, see:
The Future Of Personalized Learning And The Leaders Being Trained To Deliver It — by Ryan Ayers
Table of contents
1. Personalized Learning For Future Leaders
2. Where Personalized Learning Is Heading
3. What Implementing Personalized Learning At Scale Actually Requires
4. The Educational Leaders Being Trained To Deliver This Future
5. Conclusion
NotebookLM’s 60-second videos turned my doomscrolling curse into something useful — from digitaltrends.com by Shimul Sood
Google has announced Short Video Overviews for NotebookLM, a feature that turns dense documents and complicated sources into 60-second vertical videos that explain key ideas. Instead of staring at pages of notes, you get a quick visual walkthrough of the concept you’re trying to understand.
Doom scrolling but make it educational ?
Introducing Short Video Overviews in NotebookLM! Turn your most complex sources into 60-second, vertical videos that deep dive into any concept.
Rolling out now to Google AI Ultra and Pro subscribers on mobile & web (free users soon!) pic.twitter.com/vaOa4ELbw4
— NotebookLM (@NotebookLM) June 30, 2026
Stanford Online Launches Immersive Learning Studio — from campustechnology.com by Matt Jones
Key Takeaways
The Current State of Play: AI in Higher Education and the Road Ahead — from er.educause.edu by Tanya Gamby, David Kil, Rachel Koblic, Paul LeBlanc, Mihnea Moldoveanu and George Siemens
The conventional explanation for this strategic vacuum points to the speed of technological change; it is moving too fast for institutions built for deliberation. That is true. . . and incomplete. The deeper issue is cultural. In fairness to higher education, many industries are struggling to keep up with the pace of AI advances. Higher education, however, moves even more slowly and is not built for the kind of transformational speed now underway. Getting institutional stakeholders to engage, rethink the work, and move faster may be the central challenge facing presidents and chancellors today, and that’s saying a lot in such volatile times.
From DSC:
I highlighted this paragraph because it hits upon the key item involved here — culture. “The deeper issue is cultural.” I think that’s a very true statement.
Part of the culture and setup of many institutions includes giving faculty members full rein of their classes and their departments. Faculty members have a great deal of leeway and power in how they do things. So trying to get X faculty members to get on board — including the Department Chairs — is not an easy task.
Another part of culture involves being willing — or not — to change in the first place. Some institutions are like Google and are used to making changes and being more innovative. But those institutions are not the norm, at least in my experience. And this doesn’t even address another topic the article mentioned — the pace of these changes. As the authors point out, most institutions of traditional higher education are not equipped to deal with the current pace of change (nor are most of our other types of institutions and our corporations as well).
I’m going to end this posting with another brief excerpt from the article:
Institutions rooted in human relationships, committed to truth-seeking, and oriented toward the full development of persons play a central role. AI cannot manufacture the experience of mattering to another human being. It cannot model intellectual courage or ethical discernment. It cannot build the kind of community in which students discover who they are and what they believe.
These are not small things. They are, in fact, the things most worth doing. At their best, colleges and universities are not only preparing better workers but shaping individuals and strengthening society.
From DSC:
I used to be able to bring up Firefly on the web and use it “free” of charge — I didn’t have to go purchase tokens or credits. (I was actually paying for the Adobe Creative Cloud Pro suite of tools…so it wasn’t really free.)
But the other day I was trying to figure out what the latest pricing is at Adobe with that suite of tools and the use of credits for AI-based features. They say Adobe Creative Cloud Pro users get 4000 credits a month. Well, I have that suite and I’m still getting prompted to purchase credits. Firefly for individuals runs from $9.99 (2,000 credits/month) to $139.91 per month (50,000 credits per month). Not inexpensive, right? Below are other items along these lines.
The Era of Affordable AI Is Over. What Comes Next? — from builtin.com by Ameya Kanitkar
AI providers are shifting to usage-based billing for their services. AI fluency is more important now than ever to make the most of your tools to avoid unnecessary spending.
Summary: The era of cheap, flat-rate AI is ending as providers shift to usage-based billing. Every prompt now carries a direct cost, turning casual use into major budget risks, as seen when Uber depleted its 2026 AI budget in four months. Leaders must now track real-time value and token efficiency.
For a brief window, companies had access to the most transformative technology in a generation at the cost of a streaming subscription. Tools like ChatGPT put AI within reach of anyone with a browser and time for experimentation, while GitHub Copilot came in at just $10 a month, with token costs remaining relatively low. In the beginning, experimentation felt cost-effective, easy and relatively low-risk.
But that era is ending, and the bill is coming due faster than a lot of enterprise leaders anticipated.
The Fable of AI in Education — from downes.ca by Stephen Downes
Marc Watkins, Rhetorica, Jun 17, 2026
Tokenomics will be a hot topic of discussion on university campuses because, as Marc Watkins notes in this article, there is no realistic path forward to providing all students with access to advanced AI.
From this posting on LinkedIn.com from Dr. Nick Jackson:
And now there is a third layer emerging. Institutions are waking up to a systems-level question they are likely not remotely prepared for. Who pays for AI? How are budgets managed when there are unclear token consumption pricing models? How is AI procured? Who decides what tools get used and by whom and who gets access and at what level?
.
