experimentation

We’re Waiting Too Long to Talk to Kids About Their Futures (Julie Lammers, Britebound) — from thehumanist.xyz by Julie Lammers and Allison Dulin Salisbury
“By eighth grade, kids are already ruling out careers they’ve never had a chance to explore.”

Per Allison:

Julie Lammers, who leads Britebound, makes a compelling case for starting career exploration in middle school for precisely this reason. The goal isn’t to get a 12-year-old to choose a profession, but to help them discover possibilities they might otherwise never consider, while they’re still forming their sense of who they are and what they’re capable of.

I love the implication of this argument. If we care about preserving choice, we should start career exploration earlier, not later. Done well, it could be one of the most powerful ways to expand opportunity for young people whose circumstances might otherwise define the limits of their ambition.

Julie and I talked about what this looks like in practice, why career navigation should be a skill we develop rather than only a service we deliver, and what young people themselves are telling Britebound about the support they want. It’s a conversation that challenged some of my assumptions about when and how we should prepare kids for their futures.

Also from Allison, check out:

[Special Report] AI Could Radically Accelerate How We Learn About Learning — from thehumanist.xyz by Allison Dulin Salisbury and Mandy McLean
AI is making a virtual laboratory for human learning possible, opening the door to testing ideas we could never afford or safely attempt with actual students.

Recent advances in AI raise the possibility of building something education has never really had: a virtual laboratory for learning.

At the center of this possibility are synthetic students—AI models designed to simulate aspects of how real students behave and, eventually, how they learn. Researchers could test hundreds of ways to teach a concept, give feedback, or sequence a curriculum and identify the most promising approaches for different kinds of learners. Developers could try out a new assessment or AI tutor on thousands of simulated students before putting it in front of real ones. Districts could stress-test AI products for safety and begin to understand which tools might work for whom and under what circumstances.

True, this could make running education studies faster and cheaper. But change the cost of experimentation enough and you change what gets experimented on. The promise of synthetic students is that researchers could ask more questions, test more ambitious ideas, explore edge cases, and discard failures before spending years and millions of dollars studying them with actual students.

The Faculty Bargain Of 2040: What Should The University And Its Faculty Owe One Another? — from forbes.com by David Rosowsky

What if we stopped reforming the existing faculty career for a moment? What if we started over?

Suppose we were designing the faculty career today for the university of 2040, starting with a blank sheet of paper. There would be no rigid departmental affiliations, ranks, tenure systems, promotion policies, or inherited assumptions. We could keep anything that still made sense. But nothing would be presumed necessary simply because it has been around for decades.

What would we build?

When machines can increasingly perform parts of the work that once consumed enormous amounts of human time, the scarce attributes may shift toward judgment, curiosity, creativity, disciplinary depth, the ability to frame consequential questions, the capacity to connect ideas across fields, and the ability to know when a machine is wrong.

That is not an argument for fewer faculty. It may be an argument for different faculty – and for a faculty employment system capable of recognizing those important (even desirable) differences.

The fourth annual AI in L&D report [Taylor & Vinauskaite]

On 10/19/2026, in adult learning, analytics, Artificial Intelligence/Agents/LLMs/and Related, career development, communications, communities of practice, content development, aggregation, repositories, corporate / business world, corporate universities / corporate training, data related items, emerging technologies, experimentation, Google, human-computer interaction (HCI), ideas, innovation, instructional design, intelligent systems, interactivity, learning, learning ecosystem, Learning Experience Design, multimedia, Open AI, personalized/customized learning, platforms, productivity / tips and tricks, skills, tools, training / L&D, UK, United States, vendors, workplace, by Daniel Christian

In case it’s helpful, here is Donald’s posting on LinkedIn

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.

5 Writing Tools to Get Unstuck [Caplan]

On 10/13/2026, in Artificial Intelligence/Agents/LLMs/and Related, education technology, experimentation, Google, ideas, journalism, tools, United States, vendors, vision/possibilities, writing, by Daniel Christian

5 Writing Tools to Get Unstuck — from wondertools.substack.com by Jeremy Caplan
Dictation, writing sprints, mind maps, AI interviews, and folded paper

The hardest part of writing is the first 10 minutes. Get past that, and momentum kicks in.

Having gotten stuck and unstuck a lot over the past 325 weeks of writing this newsletter, I’ve found a few tricks that help. Pick one to try, and leave a comment to share what you do to get unstuck.

As a little experiment, I’ve made an Unstuck Studio page to accompany this post and to help you get unstuck playfully.

As Daniel Pink explains, starting comes first.

“Action precedes motivation, not the other way around.”

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 Education Design Lab’s Reimagining Community Colleges Summit [Lauritsen]

On 10/08/2026, in career development, change, changing business models, communities of practice, community colleges, design, experimentation, future of higher education, game-changing environment, higher education, ideas, instructional design, learning, learning ecosystem, Learning Experience Design, United States, by Daniel Christian

From DSC:
A couple of key quotes jumped out at me here:

And what would have to change for every learner to experience a college that knows them, believes in them, and is connected to the community where they hope to build a life?
…
The ideas these colleges started with aren’t necessarily the ideas they’re pursuing now, and that’s exactly the point. Through human-centered design, they listened deeply to learners, employers, faculty, staff, and community partners. They challenged assumptions and changed direction based on what they learned. What they are building reflects the needs, strengths, and aspirations of their communities. That is what designing with people looks like.

New study: 15 community college presidents on what it will take to lead higher ed’s next chapter [eddesignlab.org]

On 09/19/2026, in career development, change, changing business models, community colleges, competency-based learning/education, experimentation, faculty/staff, future, future of higher education, game-changing environment, generational differences, higher education, skills, student-related, teaching & learning, United States, workplace, by Daniel Christian

New study: 15 community college presidents on what it will take to lead higher ed’s next chapter — from eddesignlab.org

Community colleges are at a pivotal moment.

As learners, employers, and regional economies change, these institutions have an opportunity to lead the next evolution of higher education — if they can move beyond structures built for a different era.

In the Lab’s new study, Insights from 15 Community College Presidents on the Future of Learning in a New Economy, leaders share their perspectives on the opportunities and barriers facing the sector, as well as the institutional changes needed to help more learners access economic mobility.

Their insights validate Education Design Lab’s Future of Learning framework, which asks five essential design questions:

How might we make skills and competencies visible?
How might we create stackable pathways that credential skills along the way?
How might we make learning universally flexible through multiple modalities?
How might we guarantee applied learning opportunities for every learner?
How might we ensure every learner has access to adequate support services?

Several items re: the changes needed within higher education

On 09/14/2026, in 21st century, adjunct faculty, business side of he, career development, change, changing business models, collaboration, colleges, communities of practice, community colleges, corporate / business world, cost of getting a degree, culture, dangers of the status quo, entrepreneurship, expectations, experimentation, faculty/staff, future of higher education, game-changing environment, generational differences, higher education, ideas, innovation, instructional design, leadership, learning ecosystem, Learning Experience Design, liberal arts, lifelong learning, new business models, reinvent, skills, society, staying relevant, strategy, student-related, surviving, teaching & learning, traditional / tradition, United States, universities, youth, by Daniel Christian

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

MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training — from aiandeducation.mit.edu

However, what we learned as a group quickly convinced us that the Institute community, particularly the faculty, must tackle a set of deeper questions about the structure, meaning, and value of an MIT education in an era in which AI is one of several factors complicating the Institute’s mission.
…

In this report, we:

Highlight key aspects of the current educational landscape at MIT.
Share eight principles we relied on and that we hope will guide the Institute in the work ahead.
Recommend immediate and long-term actions for both instructors and the administration.

On somewhat related notes:

Your AI Teaching Toolkit — from wondertools.substack.com by Jeremy Caplan
11 prompts to save time and spark ideas
AI Should Accelerate How We Learn About Learning (Rajen Sheth, Kyron Learning) — from humanistxyz.substack.com by Allison Dulin Salisbury
“We shouldn’t have to wait years to find out if an approach works. We can test and learn in weeks.”

How a studio-based format transforms online teaching [via multiple authors from Cranfield University]

On 09/10/2026, in adjunct faculty, collaboration, colleges, communities of practice, community colleges, content development, aggregation, repositories, digital audio, digital learning, digital video, distance education, education technology, engagement / engaging students, experimentation, faculty/staff, higher education, ideas, instructional design, IT in HE, law schools, learning, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, multimedia, online learning, online media, pedagogy, teaching & learning, tools, UK, universities, using teams, vendors, vision/possibilities, by Daniel Christian

How a studio-based format transforms online teaching — from timeshighereducation.com by multiple authors from Cranfield University in England
Online teaching works best when teaching, facilitation and technical delivery are treated as complementary professional roles. Read guidance on studio-based delivery

Our experience of studio-based delivery suggests the need for a different approach. In this model, teaching is delivered as a live, facilitated learning experience in which academics focus on teaching and interaction, while facilitators and production staff manage interaction, session flow and technology.

Responsibilities are intentionally divided to enable more effective online pedagogy: academics concentrate on explaining concepts, facilitating discussion and responding to students while production staff manage the technical environment, transition between activities, recordings, multimedia integration, chat moderation, breakout rooms and troubleshooting, enabling a more engaging and authentic learning experience.

The most transferable lesson is that online teaching works best when teaching, facilitation and technical delivery are treated as complementary professional roles and when it is underpinned by pedagogical design.

‘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.

OpenAI’s chief scientist says no lab should keep scaling at maximum speed [Constantin]

On 09/10/2026, in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, emerging technologies, experimentation, game-changing environment, generational differences, global, legislatures / government / legal, Open AI, platforms, policy, political science, the downsides of how people use tech, United States, by Daniel Christian

OpenAI’s chief scientist says no lab should keep scaling at maximum speed — from thenextweb.com by Ana Maria Constantin
OpenAI put out two posts on Sunday. Its research organisation now uses 3.1 agent-workdays for every human one, and its chief scientist says no lab has solved alignment well enough to keep scaling at full speed. The case for an OpenAI slowdown arrived with the numbers against it.

Chief scientist Jakub Pachocki wrote the second post, an essay called An Alien Mind. It closes on a line that reads oddly from the man who runs research at the company shipping fastest.

“Currently I believe that no lab has solved alignment and monitoring to a sufficient degree to continue responsibly scaling at maximum speed for much longer,” he wrote.

…
He is not gentle about the stakes either. “This is a time that calls for extreme caution. I am concerned no one is prepared for the consequences of a continued rapid rise in machine intelligence,” Pachocki wrote.

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.
…
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.

Learning Ecosystems

The Education Design Lab’s Reimagining Community Colleges Summit [Lauritsen]

How a studio-based format transforms online teaching [via multiple authors from Cranfield University]

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