innovation

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We are at a tipping point. In the next 25 years, technologies like AI, clean energy, and bioengineering are poised to reshape society on a scale few can imagine.

Peter Leyden draws on decades of observing technological revolutions and historical patterns to show how old systems collapse, new ones rise, and humanity faces both extraordinary risk and unprecedented opportunity.

0:00 We’re on the cusp of an era of progress
0:37 The Great Progression
1:08 What was the ‘Long Boom?’
4:56 How often do these epoch resets happen?
6:12 3 Tipping points
6:39 Artificial Intelligence
7:13 Clean energy technologies
7:32 Biotechnology
9:00 The 80-year cycle
13:27 The Gilded Age
17:50 The Founding Era
22:46 The new enlightenment
32:18 The clean energy revolution
37:13 Bioengineering the genome
39:43 Industrial production vs biological engineering
47:40 What will the future think?

“Using AI to create the practice opportunities online students need” [Miller Perkins] + other items re: AI in our learning ecosystems

On 08/19/2026, in 21st century, 24x7x365 access, adjunct faculty, adult learning, Artificial Intelligence/Agents/LLMs/and Related, assessment, business, career development, colleges, communications, communities of practice, community colleges, content development, aggregation, repositories, corporate / business world, corporate universities / corporate training, digital learning, education, education technology, emerging technologies, Emotion, engineering, experimentation, faculty/staff, game-changing environment, higher education, homeschooling/homeschoolers, human-computer interaction (HCI), ideas, innovation, instructional design, intelligent systems, intelligent tutoring, IT in HE, K-12 related, leadership, learning, learning agents, learning ecosystem, Learning Experience Design, learning spaces, liberal arts, mathematics, online learning, personalized/customized learning, productivity / tips and tricks, professional development, student-related, teachers, teaching & learning, technologies for your home, tools, training / L&D, UK, United States, universities, vendors, workplace, youth, by Daniel Christian

Using AI to create the practice opportunities online students need— from timeshighereducation.com by Kathy Miller Perkins
For disciplines that depend on interpersonal skills such as communication, leadership, negotiation and mediation, AI can provide elusive experiential learning, particularly in asynchronous environments

What if AI’s greatest educational value lies not in generating answers but in generating experiences?

The result surprised me. Students did not simply interact with the AI, they meaningfully engaged with it. Many reported that the experience felt valuable precisely because the simulated conversation partner pushed back. Unlike classmates who sometimes hesitate to challenge one another, the AI consistently maintained its position. Students had to work harder, listen more carefully and apply the communication strategies they had spent weeks learning. And, importantly, they practised.

Here’s how to show students what responsible AI use looks like — from timeshighereducation.com by Andrew Firr and Alex Fenton
Well-designed assessments can highlight generative AI’s limitations, where it can provide support and what responsible practice looks like. Andrew Firr and Alex Fenton offer strategies

We have been exploring ways to offer this in our Level 7 module for MSc engineering management students, many of whom come from the Global South. Assessment is built around a practical design problem: students examine how a real campus process operates, where delays occur, evidence they observe and how a feasible redesign might improve the system – in a 3,000-word essay.

They can use AI for planning and providing clarity, but not for generating the evidence on which the analysis rests. Invented observations, measurements, screenshots or unverifiable citations are prohibited. The assessment design reinforces this through a case study, diagram, fact-checking exercise and a short AI use statement.

Requiring an AI use statement encourages students to reflect on their processes. We ask which tool they used, how they used it, how they checked the output and which parts of the work depended on their own evidence and judgement. It reminds them that evidence must be theirs and that citations must be manually verified.

Slow math: Kids may learn more when AI makes them review mistakes — from hechingerreport.org by Jill Barshay and Kristin Fasiang
A randomized experiment involved more than 6,000 Tennessee middle school students learning fractions

In an experiment involving more than 6,000 middle schoolers in Tennessee, students learned slightly more math when an AI tutor walked them through their mistakes and then required them to demonstrate the same skill correctly three times in a row before moving on.
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The winning combination wasn’t the addition of AI tutoring alone, but AI tutoring plus repetition, with the idea that students needed to stick with the skill to demonstrate some level of mastery. The students who practiced math with this AI-enhanced “mastery learning” approach scored about 3 percentage points higher than students receiving conventional computerized instruction. The advantage was small.

I Built a Team of AI Bots That Write Feedback Better than Me. Here’s How. — from drphilippahardman.substack.com by Dr Philippa Hardman
Aka, how to make learners love AI-assisted feedback, rather than loathe it.

This wasn’t hidden from the cohort. For reasons I’ll come to below, I always tell my cohorts at the very start that an AI assistant will be helping me draft their feedback. The aim wasn’t to remove me from the process, but to find out whether AI could help me to deliver feedback at a volume and quality that I simply couldn’t maintain without AI.

Spoiler: the experiment worked — but not for the reason I expected. In this post, I’ll share exactly what I built, tell you how to build a version for your course and reveal the three conditions that decide whether learners trust AI-assisted feedback or quietly stop reading it.

TL;DR: the spec for my feedback assistant is long not because the model needs persuading, but because it’s where my judgement lives. Every rule in it is a decision AI would otherwise make by itself, with limited expertise. A spec doesn’t make the model smarter — it makes expertise and judgement legible enough for the model to follow.

Of the five stages of AI grief, some managers are stuck in denial— from timeshighereducation.com by Shadi Mohamed
Acceptance is not surrender. It is about getting beyond a policing obsession and rethinking what assessment aims to measure, says Shadi Mohamed

In that moment of institutional blindness, it struck me: universities are not merely struggling with a technological adoption curve. They are grieving the demise of the modern university’s economic and institutional logic.

To understand the scale of this threat posed by AI, consider what the internet did to journalism. Newspapers thrived by bundling together classified advertising with quick news hits, sports scores and opinion columns, using that reliable revenue to fund the expensive social necessity of investigative reporting. The rise of internet advertising did not kill journalism outright but it shattered the bundle, leaving the expensive core product without its historical financial engine.

Universities operate on a similar logic, bundling content delivery, assessment, credentialling, research and professional formation. But generative AI is now commodifying the most visible parts of that package. When AI can deliver personalised explanations instantly and generate the essays and reports we use to measure student capability, the traditional degree loses its role in the labour market as a proxy for understanding. If the bundle breaks, the economic model that funds our deeper purposes is in peril.

Student Voices: Technology as a Future Learning Partner — from campustechnology.com by Mary Grush
A Q&A with Eli Blouin and Mark Frydenberg

Key Takeaways

With advanced technologies, we’re moving from accessing learning resources to participating in technology learning partnerships.
Technology learning partners participate in the learning process by asking questions and giving feedback, not just information search results.
Tomorrow’s technology learning partners will understand the whole learner — including knowledge gaps and personal learning style.
Student experiences with AI and other technologies today will inform the next generation of human/machine learning partners.

Grush: What do you think may be among the most meaningful changes in, say, the next five years?

Blouin: When I think of what will improve in the next five years — and I truly believe it’s where we’re ultimately heading — I think it will be the technology learning partner’s ability to gain a full picture of the learner. The technology will be able to take absurd amounts of context — and data — and understand the learner as a whole: where their gaps are, what they need to learn, how they learn, and how to create content tailored to a particular student. In an instant it would be able to assess, to test knowledge, and know the student as a whole person. That would be an ideal learning partner.

This quote/insight is also for the Instructional Technologists out there

On 08/10/2026, in change, colleges, community colleges, education technology, faculty/staff, higher education, innovation, journalism, United States, universities, vendors, by Daniel Christian

From MLive.com’s John Hiner — in his Letter from the Editor dated 8/6/26:
40 years of change taught me this: It’s never really been about the technology

What I’ve learned over 40 years is that people rarely resist change because they dislike innovation. More often, it’s because they’re worried they’ll lose something they value.

The 2031 Crisis in Higher Education: a stark scenario — from bryanalexander.org by Bryan Alexander — with commentary on a posting by Matthew F. Wilson, Ph.D.

Today’s post is about a scenario for higher ed’s future. It’s not from me, but created by one Matthew F. Wilson, director of Research Translation and AI Strategy at Baylor’s Institute for Global Human Flourishing. “The 2031 Crisis in Higher Education” is a dark one, imagining an accelerating decline for American colleges and universities.
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One key assumption of this scenario is that most colleges and universities will be unable to redesign themselves for the changing time.
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All of that said, it’s a fascinating and daunting scenario. It’s an interesting vision of my post-peak higher ed world.

A graphic from Matthew Wilson:

From DSC:
From what I can tell and have read, the things in the above graphic are already happening — and have been happening for some time now.

Also, I don’t think traditional institutions of higher education have the culture(s) it takes to change. So that part about colleges and U’s not being able to redesign themselves for the changing times could easily turn out to be the case.

Will this new company be the largest company on the Internet by 2030? [Christian]

On 08/01/2026, in 21st century, 24x7x365 access, adult learning, Artificial Intelligence/Agents/LLMs/and Related, Bias | Diversity Equity & Inclusion (DE&I), career development, cloud-based computing / apps / other cloud-related, communities of practice, content development, aggregation, repositories, corporate / business world, corporate universities / corporate training, cost of getting a degree, credentialing, curriculum, Daniel S. Christian, digital learning, disruption, education, education technology, elearning, emerging technologies, engagement / engaging students, future, future of higher education, game-changing environment, generational differences, global / globalization, higher education, homeschooling/homeschoolers, human-computer interaction (HCI), ideas, informal learning, innovation, instructional design, intelligent systems, intelligent tutoring, internet, K-12 related, learning, learning agents, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, learning hubs & pods & microschooling, learning preferences, learning spaces, lifelong learning, love of learning, more voice more choice more control, motivation, online learning, online media, online tutoring, pathways, personalized/customized learning, platforms, productivity / tips and tricks, professional development, reinvent, society, staying relevant, student-related, teachers, teaching & learning, training / L&D, United States, vision/possibilities, web-based collaboration, youth, by Daniel Christian

From DSC:
The vision that I’ve been tracking for well over a decade begins with this graphic:
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Could LearnVector be this next-generation company/platform? Perhaps. Time will tell.

Per Matt Tower via The EdSheet Vol. 38 dated 7/31/26:

Coursera bets $100M on its own co-founder: Andrew Ng’s new venture LearnVector lands one of the largest single checks in edtech this year — from Coursera, the company he founded 14 years ago.
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LearnVector.ai

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.
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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.

The AI-native university, AI literacy, AI and accessibility + other items re: AI in our learning ecosystems

On 07/22/2026, in 21st century, 24x7x365 access, accessibility / UDL, Artificial Intelligence/Agents/LLMs/and Related, colleges, communities of practice, community colleges, corporate / business world, corporate universities / corporate training, data related items, education technology, emerging technologies, experimentation, faculty/staff, game-changing environment, generational differences, higher education, human-computer interaction (HCI), innovation, instructional design, intelligent systems, intelligent tutoring, IT in HE, learning, learning ecosystem, Learning Experience Design, liberal arts, lifelong learning, personalized/customized learning, productivity / tips and tricks, student-related, teachers, teaching & learning, training / L&D, trends, United States, universities, by Daniel Christian

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.
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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:

- Use plain language
- Provide one practical, job-relevant example
- Avoid jargon unless necessary (and define it if used)

Then ask one follow-up question to check for understanding.

Jeff’s posting on LinkedIn.

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:

Why building an AI-native campus requires a data-first foundation
How to build an “achievement architecture” for lifelong success
How faculty-built AI tutors cut one program’s course attrition from 40% to 10%
What it means to be a lifelong learning companion

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 World’s Largest Pop-Up Book

On 07/22/2026, in art, creativity, design, ideas, innovation, library / librarians, reading, by Daniel Christian

Los Angeles Public Library Unveils the World’s Largest Pop-Up Book — from thisiscolossal.com by Kate Mothes and Daniel González

Steward Stories: How Heart of Oregon Corps Turns Service into Careers — from gettingsmart.com by Karen Pittman and Merita Irby

Our “Steward Stories” series captures lessons learned from interviews with leaders of mature, purpose-built ecosystem intermediaries. An ecosystem steward is a boundary-spanning leader who goes beyond traditional out-of-school time (OST) system building to weave together the diverse people, places, and possibilities that shape a young person’s daily life. Rather than managing a single isolated network, stewards collaborate across K-12 schools, youth development programs, and workforce systems to build vibrant, equitable learning ecosystems. They drive systemic change from practice to policy by creating purpose-built intermediaries, developing scalable cross-system training tools, and championing “Future Features” of learning. These core priorities include promoting learner agency, institutionalizing “unwalled” schools that connect community resources to formal education, broadening the definition of educators to include informal mentors, and normalizing pathways for students to receive school credit or credentials for out-of-school learning.

The starting points, paths, and targets set by these stewards are as varied as the conditions and opportunities present within their communities. But they share similarities in vision and approach. Learn More Here.

Learning ecosystems may be found anywhere, but it takes careful stewardship to help them thrive.
— Shift, Remake Learning

Free K-12 Career Literacy Resources from agilities.org

On 07/16/2026, in business, career development, collaboration, colleges, community colleges, corporate / business world, education, engagement / engaging students, engineering, entrepreneurship, experimentation, innovation, K-12 related, learning, skills, society, student-related, teachers, United States, workplace, youth, by Daniel Christian

Agilities — from agilities.org by the DeBruce Foundation; via Paul Fain
Help students build confidence and prepare for bright careers!
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Also from Paul Fain, see:

Unlocking Opportunity: Progress on Moving More Students Toward Good Jobs — from highered.aspeninstitute.org

Released in partnership with the Community College Research Center (CCRC), Unlocking Opportunity: Progress on Moving More Students Toward Good Jobs highlights early outcomes from the first 10 colleges in the Unlocking Opportunity network. The report demonstrates that community colleges can rapidly increase enrollment in high-value workforce and transfer pathways while reducing enrollment in or improving programs with weaker labor market and bachelor’s degree outcomes.
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LEGAL FUTURISMS: Consumer Legal Tech and the Soul of US Democracy

The “Dollar Legal” Thought Experiment

What if the salvation of our democratic enthusiasm doesn’t come from a political revolution, but a technological one? Enter the thought experiment of Dollar Legal.

Imagine a ubiquitous, consumer-facing legal tech platform—accessible from any smartphone—that commoditizes basic legal defense and assertion of rights. It is AI-driven legal agency for the masses, costing no more than a cup of coffee, or perhaps literally a single dollar.

Dollar Legal wouldn’t exist to litigate complex corporate mergers; it would exist to handle the agonizing friction of everyday survival. It instantly analyzes a notice to quit, drafts a legally sound response asserting warranty of habitability defenses, and files it electronically. It forces the bureaucratic machine to pause. It translates the raw, terrifying human experience of a legal threat into the cold rules and procedures that the justice system understands.

And the price, $1.00, at sufficient scale, would support the operation and maintenance of the service.

By deploying Dollar Legal, we fundamentally shift the balance of power. The democratization of legal leverage changes the calculus for bad actors who rely on the silent default judgments of the unrepresented.

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.

Higher Education Can’t Wait for the Future to Arrive (Lev Gonick, Arizona State University) [Salisbury]

On 07/07/2026, in 21st century, 24x7x365 access, career development, change, changing business models, collaboration, colleges, community colleges, competency-based learning/education, faculty/staff, future, future of higher education, game-changing environment, heutagogy, higher education, innovation, instructional design, learning, learning agents, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, learning preferences, liberal arts, more voice more choice more control, skills, society, student-related, teaching & learning, trends, United States, universities, vision/possibilities, by Daniel Christian

Higher Education Can’t Wait for the Future to Arrive (Lev Gonick, Arizona State University) — from humanistxyz.substack.com by Allison Dulin Salisbury
“The biggest risk we face as a sector is assuming we can wait out AI.”

We have an opportunity right now to reorient the university around student experience—not as an aspiration, but as a necessity. I’m calling this shift TechEd, which I explore in detail in my LinkedIn series The TechEd Revolution.

AI poses a fundamental shift in how technology might empower students to own their discovery and educational journey, and to drastically reduce the friction that makes college so unappealing to so many.

To that end, we need to urgently redesign systems and opportunities around skills and competencies. That work should be far more advanced than it currently is. And one of the hardest challenges is rethinking how we operate as a workforce in academia.

Learning Ecosystems

This quote/insight is also for the Instructional Technologists out there

The 2031 Crisis in Higher Education: a stark scenario [Bryan Alexander]

The World’s Largest Pop-Up Book

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