corporate universities / corporate training

Learning used to cascade—now it must circulate [Ravitch]

On 09/22/2026, in business, corporate / business world, corporate universities / corporate training, ideas, instructional design, leadership, learning ecosystem, Learning Experience Design, training / L&D, United States, by Daniel Christian

Learning used to cascade—now it must circulate — from chieflearningofficer.com by Sharon M. Ravitch
Long-held structural and procedural assumptions undergird how organizations promote and deepen learning that precedes its flow through organizations. What replaces the cascade isn’t a smarter platform. It’s a deliberate act of leadership; a set of adaptive mindsets that make circulation possible in the first place.

Admitting “I don’t know what I’m doing with this yet” felt like exposure, a confession of falling behind, and surfacing a workaround carried the calculated risk of “going rogue.” By the time the CLO scheduled a refresher, the tool had evolved again, and whatever either camp learned in the meantime had nowhere to go.

The issue wasn’t adoption. It was circulation. Without this step, learning cannot move through the organization quickly enough to keep pace with technological adoption.

The problem has both widened and deepened. Organizations are moving to roughly double AI investment as a share of revenue, with 72 percent of CEOs now personally directing AI strategy. Capability requirements are shifting at the pace of leadership attention, not curriculum cycles.

How to Reduce Cognitive Load in Digital Lessons — from educationtechnologysolutions.com by etsmagazine

A digital lesson can be beautifully designed and still make learning unnecessarily hard.

Students may be asked to watch a video, remember an instruction, find a link, switch tabs, interpret unfamiliar icons and submit a response—all before they have dealt with the idea the lesson was meant to teach.

That friction is often mistaken for rigour. It is not. Productive difficulty comes from thinking about the content. Avoidable difficulty comes from navigating the lesson.

The goal is not to make learning effortless. It is to spend students’ limited attention on the knowledge and skill that matter.

The above article links to:

Managing cognitive load optimises learning — from edresearch.edu.au
Implications for policymakers

The Checklist That Didn’t Exist: A Field Guide to Cutting Extraneous Load [Mervine Kiser]

On 09/14/2026, in adult learning, communities of practice, corporate / business world, corporate universities / corporate training, ideas, instructional design, learning, Learning Experience Design, learning theories, memory, productivity / tips and tricks, training / L&D, by Daniel Christian

The Checklist That Didn’t Exist: A Field Guide to Cutting Extraneous Load — from learningguild.com by Cally Mervine Kiser

If the content is fine, what else could be getting in the way?

The answer, it turns out, has been sitting in educational psychology research for decades. Working memory is finite. When learners engage with training, they are managing three competing cognitive demands simultaneously:

The complexity of the subject matter
The effortful processing that builds understanding
The mental effort generated by how the content is presented, independent of the content itself

That third one is called extraneous cognitive load, and it is the one we almost never explicitly design against. We design for accuracy. We design for alignment. We design for engagement, or at least what we hope will feel like engagement. We almost never sit down and ask, “How much unnecessary mental effort is this design generating, and how do we reduce it?”

When Learning Feels Like a Vulnerability [Hall]

On 09/14/2026, in adult learning, business, career development, change, corporate / business world, corporate universities / corporate training, Emotion, ideas, instructional design, learning, learning ecosystem, Learning Experience Design, training / L&D, vendors, by Daniel Christian

When Learning Feels Like a Vulnerability — from learningguild.com by George Hall

Why do some adult learners interpret training as a judgment about their competence, status, or identity?

Learning and development professionals often focus on content, platforms, courses, tools, and performance support. Those matter. But they are not enough. Learning is never just cognitive. It is also emotional and social.

A new skill can feel like evidence that old competence is no longer enough. Feedback can feel like judgment. A new model can feel like criticism of past practice. A request to change can feel like a loss of face.

It means learning can feel personally exposing. Adult learners do not only receive training. They interpret what needing training seems to say about their competence, status, identity, and future value. That interpretation can either open learning or shut it down.

“The Disappearing Bottom Rung in L&D” + “The AI Workflow Redesign Method: How the Top 6% of Users Get Real Returns from AI” [Hardman]

On 09/10/2026, in Artificial Intelligence/Agents/LLMs/and Related, business, career development, change, changing business models, corporate / business world, corporate universities / corporate training, ideas, instructional design, intelligent systems, learning, learning ecosystem, Learning Experience Design, training / L&D, trends, UK, United States, youth, by Daniel Christian

The Disappearing Bottom Rung in L&D — from drphilippahardman.substack.com by Dr Philippa Hardman
Junior roles are rapidly disappearing from L&D. Here’s the data + my hypothesis about why it’s happening

TL;DR: the bottom rung of L&D isn’t just narrowing — it’s being automated. The humans who remain move up the ladder, and the profession re-forms around a new set of AI and domain specialisms.
…
Is AI responsible for reshaping knowledge work, including L&D? My hypothesis is a resounding YES, primarily because of what I see in L&D job ads in 2026…

…..

The AI Workflow Redesign Method: How the Top 6% of Users Get Real Returns from AI— from drphilippahardman.substack.com by Dr Philippa Hardman
A practical three-step method to redesign your workflow & get ~2X value from AI

One pattern I see emerging from that research is this: the people getting the most value from AI aren’t the ones using it most, or the ones with the most advanced technical skills: they’re the ones who redesigned their day to day workflows around AI’s capabilities.
…

How to Redesign a Workflow in Three Steps
In my bootcamp we use a task-mapping process that distils workflow redesign down to three steps: map the work, tag each task by the impact you want AI to have, then validate whether what you want is possible.

Three steps get you to a plan: two or three tasks worth building, each with a written standard. Turning that plan into a redesign takes two more moves, which I’ll cover after the steps. I’m flagging that now because the moves are the bit most people skip, and they’re where the redesign actually happens.

Here’s a how to for each step:

Escaping The Backrooms Of Workforce Development — by Ryan Craig

Which makes sense. Because far too many find themselves in a liminal space, neither in school nor a career. Needing to make a living in an era of digital transformation and AI, they’re having a horrific time finding their way into and around work. So it’s not just education and training wonks who think workforce is the defining issue of our time. The success of Backrooms demonstrates Gen Z thinks so, too.

It’s happening everywhere. Young people around the world are angry at how hard it’s become to launch careers. This summer an entire protest movement arose in India – the world’s largest Gen Z population – around entrance exams for medical school, one of the few remaining sure bets. In the UK, youth unemployment is over 16%; it’s 15% across the EU and over 25% in Sweden. Back home, nearly 400,000 young Americans stopped looking for work in June alone. So it’s urgent that we identify what actually works to help young people get good first jobs.

The answer lies between apprenticeship perfection and train-and-pray scale. We desperately need a scalable training-first model that’s as proximate as possible to employment opportunities, employers, and actual employment. That can only mean work-based learning: real work experience completed during or connected with a training, certificate, or degree program. Work-based learning can take many forms: rotations, clinics, co-ops, internships, short projects. It can be integrated into coursework or independent. But what all forms require are bona fide employers, which regulate scale. Such programs can enroll only as many as can fit within the constraint of available work-based learning opportunities.

Also see:

Is Your College A Zombie? — from forbes.com by Ann Kirschner; via Ryan Craig

I want to be careful here, because this is not an argument that universities are doing nothing. They are doing an enormous amount. New programs, AI task forces, employability frameworks, microcredentials, and shifts in how they enable lifelong learning.

Activity is not the problem; what has not changed is the machinery that decides what counts. Too many institutions move in herds rather than forging an independent path. When one college launches a new program, say, data science and AI, fifty will follow without asking whether their version serves their students or simply checks a competitive box.

Meanwhile, 88% of American professionals believe colleges and universities should be the ones providing AI training, and nearly half say their employers have offered no AI resources whatsoever.¹

“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.

How to Design Around Cognitive Offloading [Hardman]

On 08/10/2026, in corporate universities / corporate training, emerging technologies, faculty/staff, instructional design, learning, Learning Experience Design, learning theories, productivity / tips and tricks, professional development, research, teachers, teaching & learning, training / L&D, by Daniel Christian

How to Design Around Cognitive Offloading — from drphilippahardman.substack.com by Dr Philippa Hardman
Three new studies shed light on when offloading to AI harms learning, when it helps — and how to design for the difference

A cluster of new studies — most published in the last few weeks in the International Journal of Educational Technology in Higher Education — lets us answer two questions with more precision than ever before:

When does cognitive offloading actually happen?
When does using AI improve cognition?

The answers in turn help us to start to design learning which intentionally uses AI to drive – rather than diminish – learning.
.

15 Best Instructional Design Conferences To Attend [Pappas]

On 08/10/2026, in corporate universities / corporate training, elearning, emerging technologies, instructional design, intelligent systems, intelligent tutoring, learning, learning ecosystem, Learning Experience Design, productivity / tips and tricks, professional development, training / L&D, by Daniel Christian

15 Best Instructional Design Conferences To Attend — from elearningindustry.com by Christopher Pappas

Overview: Looking for the best Instructional Design conference in 2026? Explore 15 leading learning, L&D, eLearning, and training conferences worldwide to build skills, discover learning technology, and expand your professional network.

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, 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:
.

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.
.

LearnVector.ai

Digital Accessibility Lawsuits in 2026: Five Trends Companies Should Know [blog.usablenet.com]

On 07/31/2026, in 21st century, accessibility / UDL, business, colleges, communities of practice, community colleges, content development, aggregation, repositories, corporate / business world, corporate universities / corporate training, legislatures / government / legal, marketing/social marketing, policy, United States, universities, vendors, by Daniel Christian

Digital Accessibility Lawsuits in 2026: Five Trends Companies Should Know — from blog.usablenet.com

Here are five findings companies should understand, along with practical steps for reducing risk.

1. Digital accessibility lawsuits are on pace to reach 6,000
2. Where a company sells matters more than where it is headquartered
3. E-commerce remains the primary target
…and more

How Explainer Video Production Turns Ideas Into Clear Stories [Williamson]

On 07/31/2026, in adult learning, business, content development, aggregation, repositories, corporate / business world, corporate universities / corporate training, digital learning, digital storytelling, digital video, ideas, instructional design, learning, Learning Experience Design, productivity / tips and tricks, training / L&D, by Daniel Christian

How Explainer Video Production Turns Ideas Into Clear Stories — from bitrebels.com by Lauren Williamson

Ideas rarely arrive in a neat order. They show up as notes, claims, diagrams, feature lists, and customer questions. Explainer video production turns that raw material into a story people can process quickly. It clarifies the problem, frames the change, and shows why the answer matters. When the script, visuals, voice, and pacing work together, viewers spend less energy decoding the message and more attention on absorbing it.

Explainer video production works because it gives ideas a usable order. It turns scattered information into a story with purpose, pace, and visual evidence. The strongest results come from careful message decisions before animation starts. When script, design, voice, and timing support one central point, viewers can follow with less effort. That clarity helps organizations teach, persuade, and make it easier to remember.

What China’s Calligraphy Lessons Can Teach Us About AI — from linkedin.com by Rebecca Winthrop

He recalled visiting a primary school classroom in China several years ago where students were practicing calligraphy—the centuries-old art of writing Chinese characters with brush and ink. While the scene appeared entirely traditional, there was something innovative happening beneath the surface. Embedded in students’ desks was an AI system capable of providing immediate feedback on their brushwork, noting misplaced strokes and suggesting corrections.

The image is striking. Students were learning in much the same way generations before them had learned—brush in hand, immersed in a deeply human and cultural practice. Yet AI was quietly enhancing the experience by providing immediate, personalized feedback that would have been difficult for any teacher to provide to her whole class.

That example captures something important for me. The most promising uses of AI may not be the applications that place technology front and center, but those that support learners to do what they have always done: connect with educators, practice with concentration, and learn and grow.

AI Can Build Your Course, but Can it Design the Learning? — from drphilippahardman.substack.com by Dr. Philippa Hardman
Aka, the current state of AI’s instructional design ability & what it means for L&D

So these models have learned design artefacts in extraordinary volume and design method barely at all. They know what e-learning looks like. They have very little access to why any of it looks that way and whether it actually works or not.

That produces a completely consistent behaviour: ask an LLM for an e-learning module and it generates the statistical centre of every e-learning module it has ever seen. What AI produces isn’t the output of a design process. It’s the output of a sampling process — the statistical centre of every e-learning module it has ever seen.
…
So the honest conclusion isn’t that AI can design learning; it’s that AI executes design well when someone who already knows what to ask for is doing the asking and the checking.

This finding gives us a clearer division of labour than we’ve had before and with it a clearer picture of what the AI-enhanced workflow might look like:

The Pedagogy of Trading Places: Lessons from an Unexpected Role Reversal with AI — from facultyfocus.com by Sherrie Myers Bartell, PhD

These disruptions matter. They remind us that teaching is not a static identity but a dynamic posture. They show us that our pedagogical selves are not fixed; they are responsive to context, energy, and attention. They reveal that the qualities we value most in our teaching—curiosity, metaphor, play, and expansiveness—are not automatic. They require care and cultivation.

The AI didn’t “teach” me in the traditional sense. But it did something pedagogically adjacent: it surprised me back into myself.

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.
…
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.

Instructional Design Trends: What’s Shaping The Future Of Learning? [Pappas] + “The Future Of Personalized Learning And The Leaders Being Trained To Deliver It” [Ayers]

On 07/06/2026, in analytics, content development, aggregation, repositories, corporate / business world, corporate universities / corporate training, educational games / serious games, elearning, emerging technologies, engagement / engaging students, future, game-changing environment, human-computer interaction (HCI), ideas, immersion, innovation, instructional design, intelligent systems, intelligent tutoring, leadership, organizational change, personalized/customized learning, skills, strategy, traditional / tradition, training / L&D, trends, user experience (UX), by Daniel Christian

Instructional Design Trends: What’s Shaping The Future Of Learning? — from elearningindustry.com by Christopher Pappas

Table of contents

1. Why Instructional Design Is Entering A New Era
2. The State Of Instructional Design Today
3. Top Instructional Design Trends Shaping 2026
4. The Future Of Instructional Design And Technology

Also from elearningindustry.com, see:

The Future Of Personalized Learning And The Leaders Being Trained To Deliver It — by Ryan Ayers

Table of contents

1. Personalized Learning For Future Leaders
2. Where Personalized Learning Is Heading
3. What Implementing Personalized Learning At Scale Actually Requires
4. The Educational Leaders Being Trained To Deliver This Future
5. Conclusion

Learning Ecosystems