Light Bulb Moments from 100 Episodes — from intentionalteaching.buzzsprout.com by Derek Bruff

Per Derek:

Welcome to the 100th episode of Intentional Teaching! I am very excited to reach this milestone, and I am deeply grateful to all my guests and all my listeners for making this possible. I learn so much from my guests, and I’m thrilled to know that folks out there appreciate the interviews and the show.

In this 100th episode, I take a look back at some of the light bulb moments I’ve had interviewing guests over the last four years. What quotes and ideas and examples have really stuck with me and informed my work? 

My hope is that you’ll find these moments interesting and perhaps useful to your work in higher education, and that this clip show might interest you in digging into the podcast archives and listening to some older episodes.

 

 

The Age of Perpetual Change — from educause.edu
Artificial intelligence (AI) has risen to the number one spot on the 2027 EDUCAUSE Top 10. But its rank signals urgency more than consensus, and higher education is still grappling with what AI-driven change could mean for institutions. In many ways, AI embodies a broader reality: We’re living in an age of perpetual change.

2027 Top 10 Overview
Technological innovation is outpacing our ability to manage and harness it. The political and economic winds around us are erratic. And higher education itself is undergoing profound shifts that will reshape how our institutions function.

While this atmosphere breeds uncertainty and anxiety, it can also inspire innovation and growth. The 2027 EDUCAUSE Top 10 highlights how the higher education technology community is responding to those opportunities.

 

The State of AI Report 2026 — from press.airstreet.com by Nathan Benaich
AI is building AI, physical AI, booming inference, and escalating cyber risks.

For nearly a decade, this report has been a labor of love. My interest in AI began with my PhD in cancer research and computational biology in the early 2010s. Since 2013, I’ve invested in companies building and applying AI to accelerate technological progress. The report is my way of sharing what I’m learning and helping more people understand the research shaping our world.

Every day brings new papers, model releases, and developments in industry and politics. Much of the work goes into deciding what deserves your attention, checking what the evidence supports, debating it with researchers and builders, and explaining why it matters.

 

October is National Disability Employment Awareness Month — from nationaldisabilityinstitute.org

Every October, we pause to honor something that should be obvious all year long: people with disabilities are part of the American workforce, and they always have been.

This is National Disability Employment Awareness Month, or NDEAM. The 2026 theme, set by the U.S. Department of Labor’s Office of Disability Employment Policy, is “Celebrating Value and Talent.” It’s the same theme as last year, carried forward as the country marks its 250th birthday.

We love that choice. It doesn’t ask anyone to prove their worth. It starts from the truth that the value and talent are already here.

A month with a long history
NDEAM began in 1945 as a single week. Congress expanded it to a full month in 1988, and today it’s observed by employers, schools, community groups and advocates across the country.

Eighty years in, the work is far from done.


Also relevant/see:


National Disability Institute (NDI) has launched a new podcast series. — from nationaldisabilityinstitute.org

“Moving the Needle” explores the ideas, innovations and partnerships that shape NDI’s work and are defining what comes next.

Episode 1 chronicles the gaps that existed prior to NDI’s formation, the organization’s humble beginnings and how far NDI has moved the needle toward building a better financial future for people with disabilities.

From vinyl records and paper checks to QR codes and smartphone apps, the first episode takes listeners on a then-and-now journey through NDI’s history, told through personal and professional reflections from members of its leadership team.


From DSC:
The topic of disability hits close to our home. One of our daughters was born with Mosaic Down Syndrome. She is different, but wonderfully so. Literally, from day one, the LORD said that He was going to teach me through her, and He has absolutely done so. I won’t get into all the ways that that has occurred, but valuing differences is not a cliché for me. For one thing, I don’t use the word “normal” much anymore…as there is no “normal.” I might use the word typical, but not normal. 

Our daughter flavors the world in wonderful ways. For example:

  • Even as a little girl, she would go up and give hugs to people — surprising them in wonderfully positive ways.
  • She thrived in preschool all the way up through 5th grade, but then struggled mightily in 6th grade and 7th grade; she then went into homeschooling in 8th-12th grade. She also went to a Christian High School and a Career Technical Center.
  • Within the last several years, she’s really hit her stride in school. Her note-taking and skills have jumped to a completely new level. She learns, but she learns at her own pace.
  • She loves to dance and to draw things. She loves to write fantastical stories.
  • She has successfully worked two jobs already.
  • She’s in a special program for college and is learning a lot — but not without challenges. 
  • She has an imagination that is off the charts. She can see movies in her head and hear songs without issue. We’re hoping that this special program will give her access to creative writing classes, art, criminology, and other topics that she’s interested in. 

The LORD has made good on His word — again. I’ve learned a lot through her. I’ve changed because of her — no doubt about it. But I’ve also learned the incredible challenges that people with disabilities face. If their unique physical and/or cognitive situations weren’t enough, the way policies are created puts them at huge disabilities that the rest of society would balk at in an instant (and our Senators and Representatives would not stand for it if this were them or someone in their families). This involves budgetary items, big time. But also, people with disabilities are often pigeonholed into certain jobs/industries. This needs to stop. 

So I’m posting the above items to give more visibility to this subculture within our society, and the challenges that they face. Let’s help straighten this situation out. 

 

The AI Tools that L&D Teams Are Building (Rather than Buying) — from drphilippahardman.substack.com by Dr. Philippa Hardman
Aka, how build skills are changing how L&D thinks & works

More and more of my advisory work is no longer about helping organisations choose a vendor; it’s about helping their teams build their own AI tools, and build them well. Over the last couple of years I’ve watched several hundred L&D practitioners do it, and in this week’s post I share what they built, why, and what it tells us about what L&D actually needs from AI.

A Tool of One’s Own
So, what do these tools look like in practice? I did some analysis and the tools that L&D teams are building first fall into three categories:

1. Tools that support the work before and after the course
2. Tools that make the learning experience more dynamic
3. Tools that do L&D tasks to the team’s own methods, processes and standards

 

Priority 1: Getting a View and Setting a Vision Across Systems — from michaelbhorn.substack.com by Michael B. Horn, Angela Jackson, Mallory Dwinal-Palisch, and Danny Curtis

The shift required to prepare learners for a labor market defined by rapid skill change and more complex transitions cannot be accomplished by any one system. Nor is it enough for K–12, higher education, and workforce development to improve independently. They must work together—aligning programs, sharing resources, creating smoother transitions, and building models that combine academic learning with real workplace experience. Career readiness is not simply a goal these systems share. It is a shared outcome that they produce together as parts of a single talent pipeline.

For that reason, the first priority for states is not a single programmatic reform. It is creating a coherent, cross-system strategy. K–12, higher education, and workforce agencies— and the providers they oversee—need a common understanding of the current system, the future state they are trying to build, the changes required to get there, and the progress they are making toward those changes.
.

 

Below are several items that were either mentioned by Matt Tower or I came across them via offshoots of items that he linked to:

Future Universities Alliance Names Inaugural Innovation Sandbox Cohort — from provost.duke.edu
Program incubated by Duke brings together higher education innovators from 23 countries for year-long peer exchange

The Future Universities Alliance has selected 49 institutions from 23 countries for the inaugural cohort of its Innovation Sandbox, a 12-month peer learning program for higher education leaders advancing ambitious, institution-level innovation.The cohort brings together founders of new universities, administrators and faculty leading high-stakes changes within established institutions, and leaders of proven models exploring how their innovations can travel to new contexts. Participants were chosen through a competitive process that drew applicants from around the world in its inaugural year.

A makeover for college: No gym, no meal plan and two years of work — from npr.org by Jon Marcus

An entirely new college is being planned here to change not only how higher education is delivered, but at what price. It also proposes to highlight ways the current business model no longer works, as evidenced by the 152 colleges and universities that have closed or merged since 2016, and the 442 others that a new projection says are at risk.

So deeply entrenched are the problems confronting higher education that a growing chorus of innovators says the easiest way to fix them is to begin again from scratch.

All undergraduates should get work-based learning, v-cs say — from timeshighereducation.com by Jack Grove. NOTE: this item is behind a paywall.
Bold plan to support graduate employment would require placements for 1.4 million students annually

 

‘You can’t just lecture students any more’ — from timeshighereducation.com by Debra Page
GenAI does not make lecturers redundant, but it does weaken one-way content delivery as a reason to bring students together, writes Debra Page. She shares four practical shifts to teaching in the age of AI

This does not make lecturers redundant; it makes one-way content delivery a weaker reason to bring students together. Expert explanation, intellectual storytelling and the modelling of disciplinary thinking still matter, but in-person teaching must also help students apply knowledge, question claims, build arguments and encounter perspectives beyond their own.

The problem is not lecturing; it is speaking for an hour and treating content coverage as evidence that learning has occurred.

These four practical shifts can make lectures more valuable in the age of AI.

 
 

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— 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.
…
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.

 

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. 

 
© 2025 | Daniel Christian