We’re excited to announce and share our new Parents Guide to Microschools, a clear and approachable introduction to one of the fastest growing learning models in the country. The guide unpacks what microschools are, how they work and why families are increasingly drawn to intimate, relationship centered environments. It highlights features like flexible schedules, small cohorts, personalized pathways and hands-on learning so parents can picture what these settings actually look and feel like.
It also equips families with practical tools to navigate the decision making process: key questions to ask during visits, indicators of strong culture and instruction, considerations around cost and accreditation and how to assess overall fit for each learner. Whether parents are simply curious or actively exploring new options, this guide offers clarity, confidence and a starting point for imagining what learning could look like next.
While it’s true that Nano Banana generates better infographics than other AI models, the conversation has so far massively under-sold what’s actually different and valuable about this tool for those of us who design learning experiences.
What this means for our workflow:
Instead of the traditional “commission ? wait ? tweak ? approve ? repeat” cycle, Nano Banana enables an iterative, rapid-cycle design process where you can:
Sketch an idea and see it refined in minutes.
Test multiple visual metaphors for the same concept without re-briefing a designer.
Build 10-image storyboards with perfect consistency by specifying the constraints once, not manually editing each frame.
Implement evidence-based strategies (contrasting cases, worked examples, observational learning) that are usually too labour-intensive to produce at scale.
This shift—from “image generation as decoration” to “image generation as instructional scaffolding”—is what makes Nano Banana uniquely useful for the 10 evidence-based strategies below.
Arnett’s experience spending decades in a job she intended as a first step is common among CNAs, medical assistants, and other entry-level healthcare workers, many of them women of color from low-income backgrounds. Amid a nationwide nursing shortage, elevating those workers seems like an obvious solution, but the path from CNA to nurse isn’t so much a ladder as it is a huge leap.
And obstacle after obstacle is strewn in the way. The high cost of nursing school, lengthy prerequisite requirements, rigid schedules, and unpaid clinical hours make it difficult for many CNAs to advance in their careers, despite their willingness and ability and the dire need of healthcare facilities.
While there are no national statistics about the number of entry-level healthcare workers who move on to higher-paid positions, a study of federal grants for CNA training showed that only 3% of those who completed the training went on to pursue further education to become an LPN or RN. Only 1% obtained an associate degree or above. A similar study in California showed that 22% of people who completed CNA certificate programs at community colleges went on to get a higher-level educational credential in health, but only 13% became registered nurses within six years.
That reality perpetuates chronic shortages in nursing, and it also keeps hundreds of thousands of healthcare workers locked below a living wage, often for decades.
So this year, I’ve been exploring new ways that AI can help support students with disabilities—students on IEPs, learning plans, or 504s—and, honestly, it’s changing the way I think about differentiation in general.
As a quick note, a lot of what I’m finding applies just as well to English language learners or really to any students. One of the big ideas behind Universal Design for Learning (UDL) is that accommodations and strategies designed for students with disabilities are often just good teaching practices. When we plan instruction that’s accessible to the widest possible range of learners, everyone benefits. For example, UDL encourages explaining things in multiple modes—written, visual, auditory, kinesthetic—because people access information differently. I hear students say they’re “visual learners,” but I think everyone is a visual learner, and an auditory learner, and a kinesthetic learner. The more ways we present information, the more likely it is to stick.
So, with that in mind, here are four ways I’ve been using AI to differentiate instruction for students with disabilities (and, really, everyone else too):
What I’ve tried to do is bring together genuinely useful AI tools that I know are already making a difference.
For colleagues wanting to explore further, I’m sharing the list exactly as it appears in the table, including website links, grouped by category below. Please do check it out, as along with links to all of the resources, I’ve also written a brief summary explaining what each of the different tools do and how they can help.
Last week, I wrapped up Dr Philippa Hardman’s intensive bootcamp on AI in learning design. Four conversations, countless iterations, and more than a few humbling moments later – here’s what I am left thinking about.
An aside: Google is working on a new vision for textbooks that can be easily differentiated based on the beautiful success for NotebookLM. You can get on the waiting list for that tool by going to LearnYourWay.withgoogle.com.
… Nano Banana Pro
Sticking with the Google tools for now, Nano Banana Pro (which you can use for free on Google’s AI Studio), is doing something that everyone has been waiting a long time for: it adds correct text to images.
The simple act of remembering is the crux of how we navigate the world: it shapes our experiences, informs our decisions, and helps us anticipate what comes next. For AI agents like Comet Assistant, that continuity leads to a more powerful, personalized experience.
Today we are announcing new personalization features to remember your preferences, interests, and conversations. Perplexity now synthesizes them automatically like memory, for valuable context on relevant tasks. Answers are smarter, faster, and more personalized, no matter how you work.
From DSC : This should be important as we look at learning-related applications for AI.
For the last three days, my Substack has been in the top “Rising in Education” list. I realize this is based on a hugely flawed metric, but it still feels good. ?
You’ll hear me briefly describe five recent op-eds on teaching and learning in higher ed. For each op-ed, I’ll ask each of our panelists if they “take it,” that is, generally agree with the main thesis of the essay, or “leave it.” This is an artificial binary that I’ve found to generate rich discussion of the issues at hand.
Three years ago, we were impressed that a machine could write a poem about otters. Less than 1,000 days later, I am debating statistical methodology with an agent that built its own research environment. The era of the chatbot is turning into the era of the digital coworker. To be very clear, Gemini 3 isn’t perfect, and it still needs a manager who can guide and check it. But it suggests that “human in the loop” is evolving from “human who fixes AI mistakes” to “human who directs AI work.” And that may be the biggest change since the release of ChatGPT.
Results May Vary — from aiedusimplified.substack.com by Lance Eaton, PhD On Custom Instructions with GenAI Tools….
I’m sharing today about custom instructions and my use of them across several AI tools (paid versions of ChatGPT, Gemini, and Claude). I want to highlight what I’m doing, how it’s going, and solicit from readers to share in the comments some of their custom instructions that they find helpful.
I’ve been in a few conversations lately that remind me that not everyone knows about them, even some of the seasoned folks around GenAI and how you might set them up to better support your work. And, of course, they are, like all things GenAI, highly imperfect!
I’ll include and discuss each one below, but if you want to keep abreast of my custom instructions, I’ll be placing them here as I adjust and update them so folks can see the changes over time.
I just completed nearly 60,000 miles of travel across Europe, Asia, and the Middle East meeting with hundred of companies to discuss their AI strategies. While every company’s maturity is different, one thing is clear: AI as a business tool has arrived: it’s real and the use-cases are growing.
A new survey by Wharton shows that 46% of business leaders use Gen AI daily and 80% use it weekly. And among these users, 72% are measuring ROI and 74% report a positive return. HR, by the way, is the #3 department in use cases, only slightly behind IT and Finance.
What are companies getting out of all this? Productivity. The #1 use case, by far, is what we call “stage 1” usage – individual productivity.
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From DSC: Josh writes: “Many of our large clients are now implementing AI-native learning systems and seeing 30-40% reduction in staff with vast improvements in workforce enablement.”
While I get the appeal (and ROI) from management’s and shareholders’ perspective, this represents a growing concern for employment and people’s ability to earn a living.
And while I highly respect Josh and his work through the years, I disagree that we’re over the problems with AI and how people are using it:
Two years ago the NYT was trying to frighten us with stories of AI acting as a romance partner. Well those stories are over, and thanks to a $Trillion (literally) of capital investment in infrastructure, engineering, and power plants, this stuff is reasonably safe.
Those stories are just beginning…they’re not close to being over.
So let’s imagine a world where there’s no separation between learning and assessment: it’s ongoing. There’s always assessment, always learning, and they’re tied together. Then we can ask: what is the role of the human in that world? What is it that AI can’t do?
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Imagine something like that in higher ed. There could be tutoring or skill-based work happening outside of class, and then relationship-based work happening inside of class, whether online, in person, or some hybrid mix.
The aspects of learning that don’t require relational context could be handled by AI, while the human parts remain intact. For example, I teach strategy and strategic management. I teach people how to talk with one another about the operation and function of a business. I can help students learn to be open to new ideas, recognize when someone pushes back out of fear of losing power, or draw from my own experience in leading a business and making future-oriented decisions.
But the technical parts such as the frameworks like SWOT analysis, the mechanics of comparing alternative viewpoints in a boardroom—those could be managed through simulations or reports that receive immediate feedback from AI. The relational aspects, the human mentoring, would still happen with me as their instructor.
A New AI Career Ladder — from ssir.org (Stanford Social Innovation Review) by Bruno V. Manno; via Matt Tower The changing nature of jobs means workers need new education and training infrastructure to match.
AI has cannibalized the routine, low-risk work tasks that used to teach newcomers how to operate in complex organizations. Without those task rungs, the climb up the opportunity ladder into better employment options becomes steeper—and for many, impossible. This is not a temporary glitch. AI is reorganizing work, reshaping what knowledge and skills matter, and redefining how people are expected to acquire them.
The consequences ripple from individual career starts to the broader American promise of economic and social mobility, which includes both financial wealth and social wealth that comes from the networks and relationships we build. Yet the same technology that complicates the first job can help us reinvent how experience is earned, validated, and scaled. If we use AI to widen—not narrow—access to education, training, and proof of knowledge and skill, we can build a stronger career ladder to the middle class and beyond. A key part of doing this is a redesign of education, training, and hiring infrastructure.
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What’s needed is a redesigned model that treats work as a primary venue for learning, validates capability with evidence, and helps people keep climbing after their first job. Here are ten design principles for a reinvented education and training infrastructure for the AI era.
Create hybrid institutions that erase boundaries. …
Make work-based learning the default, not the exception. …
Create skill adjacencies to speed transitions. …
Place performance-based hiring at the core. …
Ongoing supports and post-placement mobility. …
Portable, machine-readable credentials with proof attached. …
It begins with a basic reversal of mindset: Stop treating AI as a threat to be policed. Start treating it as the accelerant that finally forces us to build the education we should have created decades ago.
A serious institutional response would demand — at minimum — six structural commitments:
Make high-intensity human learning the norm. …
Put active learning at the center, not the margins. …
Replace content transmission with a focus on process. …
Mainstream high-impact practices — stop hoarding them for honors students. …
Redesign assessment to make learning undeniable. …
And above all: Instructional design can no longer be a private hobby.
How to Integrate AI Developmentally into Your Courses
Lower-Level Courses: Focus on building foundational skills, which includes guided instruction on how to use AI responsibly. This moves the strategy beyond mere prohibition.
Mid-Level Courses: Use AI as a scaffold where faculty provide specific guidelines on when and how to use the tool, preparing students for greater independence.
Upper-Level/Graduate Courses: Empower students to evaluate AI’s role in their learning. This enables them to become self-regulated learners who make informed decisions about their tools.
Balanced Approach: Make decisions about AI use based on the content being learned and students’ developmental needs.
Now that you have a framework for how to conceptualize including AI into your courses here are a few ideas on scaffolding AI to allow students to practice using technology and develop cognitive skills.
What was encouraging, though, is that students aren’t just passively accepting this new reality. They are actively asking for help. Almost half want their teachers to help them figure out what AI-generated content is trustworthy, and over half want clearer guidelines on when it’s appropriate to use AI in their work. This isn’t a story about students trying to cheat the system; it’s a story about a generation grappling with a powerful new technology and looking to their educators for guidance. It echoes a sentiment I heard at the recent AI Pioneers’ Conference – the issue of AI in education is fundamentally pedagogical and ethical, not just technological.
This blog explores how custom AI development accelerates the evolution from static AI to dynamic learning agents and why this transformation is critical for driving innovation, efficiency, and competitive advantage.
… Dynamic Learning Agents: The Next Generation Dynamic learning agents, sometimes referred to as adaptive or agentic AI, represent a leap forward. They combine continuous learning, autonomous action, and context-aware adaptability.
Custom AI development plays a crucial role here: it ensures that these agents are designed specifically for an enterprise’s unique needs rather than relying on generic, one-size-fits-all AI platforms. Tailored dynamic agents can:
Continuously learn from incoming data streams
Make autonomous, goal-directed decisions aligned with business objectives
Adapt behavior in real time based on context and feedback
Collaborate with other AI agents and human teams to solve complex challenges
The result is an AI ecosystem that evolves with the business, providing sustained competitive advantage.
Perception: The Foundation of Intelligent Agents Perception is the first step in building AI agents. It involves capturing and interpreting data from multiple modalities, including text, images, audio, and structured inputs. A multimodal AI agent relies on this comprehensive understanding to make informed decisions.
For example, in healthcare, an AI agent may process electronic health records (text), MRI scans (vision), and patient audio consultations (speech) to build a complete understanding of a patient’s condition. Similarly, in retail, AI agents can analyze purchase histories (structured data), product images (vision), and customer reviews (text) to inform recommendations and marketing strategies.
Effective perception ensures that AI agents have contextual awareness, which is essential for accurate reasoning and appropriate action.
Your New ChatGPT Guide— from wondertools.substack.com by Jeremy Caplan and The PyCoach 25 AI Tips & Tricks from a guest expert
ChatGPT can make you more productive or dumber. An MIT study found that while AI can significantly boost productivity, it may also weaken your critical thinking. Use it as an assistant, not a substitute for your brain.
If you’re a student, use study mode in ChatGPT, Gemini, or Claude. When this feature is enabled, the chatbots will guide you through problems rather than just giving full answers, so you’ll be doing the critical thinking.
ChatGPT and other chatbots can confidently make stuff up (aka AI hallucinations). If you suspect something isn’t right, double-check its answers.
NotebookLM hallucinates less than most AI tools, but it requires you to upload sources (PDFs, audio, video) and won’t answer questions beyond those materials. That said, it’s great for students and anyone with materials to upload.
Probably the most underrated AI feature is deep research. It automates web searching for you and returns a fully cited report with minimal hallucinations in five to 30 minutes. It’s available in ChatGPT, Perplexity, and Gemini, so give it a try.
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.
While confidence in higher education has eroded and more Americans are questioning the importance of a degree, the demand for internships among college students is skyrocketing and the odds of getting an internship at a major company are now lower than getting into the Ivy League. This begs the question: are we at a point where an internship is as valuable – or perhaps more so – than a degree itself?
While concerns about degree ROI were on the rise, the value of internships and other work-integrated learning opportunities was becoming increasingly apparent. New research and analysis have shown us how valuable it is for a student to have an internship during college: it doubles the odds they have a good job waiting for them upon graduation and doubles their odds of being engaged in their work over their lifetime. Although there are some variations in those outcomes by choice of college or academic major, those variations pale in comparison to the impact of having an internship. In short, a collegiate internship experience is a more important indicator of these outcomes than alma mater or major.
In that spirit, in this post I examine a report from Virginia’s Joint Legislative Audit and Review Commission (JLARC) on Virginia’s Community Colleges and the changing higher-education landscape. The report offers a rich view of how several major issues are evolving at the institutional level over time, an instructive case study in big changes and their implications.
Its empirical depth also prompts broader questions we should ask across higher education.
What does the shift toward career education and short-term training mean for institutional costs and funding?
How do we deliver effective student supports as enrollment moves online?
As demand shifts away from on-campus learning, do physical campuses need to get smaller?
Are we seeing a generalizable movement from academic programs to CTE to short-term options? If so, what does that imply for how community colleges are staffed and funded?
As online learning becomes a larger, permanent share of enrollment, do student services need a true bimodal redesign, built to serve both online and on-campus students effectively? Evidence suggests this urgent question is not being addressed, especially in cash-strapped community colleges.
As online learning grows, what happens to physical campuses? Improving space utilization likely means downsizing, which carries other implications. Campuses are community anchors, even for online students—so finding the right balance deserves serious debate.
Entrepreneurship education fosters resilience, creativity, and financial literacy—skills critical for success in an unpredictable, tech-driven world.
Programs like NFTE, Junior Achievement, and Uncharted Learning empower students by offering real-world entrepreneurial experiences and mentorship.
“Entrepreneurship is the job of the future.”
— Charles Fadel, Education for the Age of AI
This shift requires a radical re-evaluation of what we teach. Education leaders across the country are realizing that the most valuable skill we can impart is not accounting or marketing, but the entrepreneurial mindset. This mindset—built on resilience, creative problem-solving, comfort with ambiguity, and the ability to pivot—is essential in startups, as an intrapreuer in big organizations, or as a citizen working for the common good.
From DSC: Stephen has some solid reflections and asks some excellent questions in this posting, including:
The question is: how do we optimize an AI to support learning? Will one model be enough? Or do we need different models for different learners in different scenarios?
A More Human University: The Role of AI in Learning — from er.educause.edu by Robert Placido Far from heralding the collapse of higher education, artificial intelligence offers a transformative opportunity to scale meaningful, individualized learning experiences across diverse classrooms.
The narrative surrounding artificial intelligence (AI) in higher education is often grim. We hear dire predictions of an “impending collapse,” fueled by fears of rampant cheating, the erosion of critical thinking, and the obsolescence of the human educator.Footnote1 This dystopian view, however, is a failure of imagination. It mistakes the death rattle of an outdated pedagogical model for the death of learning itself. The truth is far more hopeful: AI is not an asteroid coming for higher education. It is a catalyst that can finally empower us to solve our oldest, most intractable problem: the inability to scale deep, engaged, and truly personalized learning.
Increasing the rate of scientific progress is a core part of Anthropic’s public benefit mission.
We are focused on building the tools to allow researchers to make new discoveries – and eventually, to allow AI models to make these discoveries autonomously.
Until recently, scientists typically used Claude for individual tasks, like writing code for statistical analysis or summarizing papers. Pharmaceutical companies and others in industry also use it for tasks across the rest of their business, like sales, to fund new research. Now, our goal is to make Claude capable of supporting the entire process, from early discovery through to translation and commercialization.
To do this, we’re rolling out several improvements that aim to make Claude a better partner for those who work in the life sciences, including researchers, clinical coordinators, and regulatory affairs managers.
AI as an access tool for neurodiverse and international staff— from timeshighereducation.com by Vanessa Mar-Molinero Used transparently and ethically, GenAI can level the playing field and lower the cognitive load of repetitive tasks for admin staff, student support and teachers
Where AI helps without cutting academic corners When framed as accessibility and quality enhancement, AI can support staff to complete standard tasks with less friction. However, while it supports clarity, consistency and inclusion, generative AI (GenAI) does not replace disciplinary expertise, ethical judgement or the teacher–student relationship. These are ways it can be put to effective use:
The Sleep of Liberal Arts Produces AI — from aiedusimplified.substack.com by Lance Eaton, Ph.D. A keynote at the AI and the Liberal Arts Symposium Conference
This past weekend, I had the honor to be the keynote speaker at a really fantstistic conferece, AI and the Liberal Arts Symposium at Connecticut College. I had shared a bit about this before with my interview with Lori Looney. It was an incredible conference, thoughtfully composed with a lot of things to chew on and think about.
It was also an entirely brand new talk in a slightly different context from many of my other talks and workshops. It was something I had to build entirely from the ground up. It reminded me in some ways of last year’s “What If GenAI Is a Nothingburger”.
It was a real challenge and one I’ve been working on and off for months, trying to figure out the right balance. It’s a work I feel proud of because of the balancing act I try to navigate. So, as always, it’s here for others to read and engage with. And, of course, here is the slide deck as well (with CC license).
