UK

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.

“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.
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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

Course design for a new age — from timeshighereducation.com by Campus contributors , Laura Duckett
Learn how to design courses that respond to the evolving needs of both students and employers

At a time when the value of a degree is being called into question, many argue that teaching subject knowledge is no longer enough. University courses must be flexible and embed work training and skills development to serve today’s students. This collection of resources is a guide to designing courses that respond to the evolving needs of both students and employers.

Teaching is a verb: what sets good teachers apart — from timeshighereducation.com by William J. Owen
Reflection is what moves teaching beyond performance and towards continuous improvement, writes William Owen

What sets good teachers apart is their willingness to continually reflect, learn and grow alongside their students. Great teaching is not a destination, it is an ongoing practice of curiosity, humility and intentionality. And perhaps that is what makes it so challenging and so rewarding.

How to set your students up to write compelling op-eds — from timeshighereducation.com by Arafat Reza and Shahariar Sadat
Help students develop their writing and refine their arguments by publishing opinion pieces. Find advice for supporting student writers here

Academics are ideally placed to bridge this gap. Beyond teaching disciplinary knowledge, they can help students develop the confidence and practical skills needed to engage in public discourse. Here, we discuss practical ways in which academics can encourage students to write op-eds that inform people, influence law and policymaking, and inspire positive change in society. Plus, find five tips for teaching students to write compelling arguments.

Rethinking course design to enliven student engagement — from timeshighereducation.com by
To foster students’ meaningful learning, educators should be using class time to maximise the benefits of the in-person experience, as Nasreen Sultana explains

We landed on four practical strategies to make learning more interactive, relevant and student-centred. Although developed in a management classroom, these tips can be adapted across disciplines.

“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.

Bring playfulness into teaching for collaboration and creativity — from timeshighereducation.com by Isabel Taurel
In learning, playfulness is often overlooked. Yet it could be key to embedding the soft, ‘human’ skills students will need for the future. Isabel Taurel shows how

The same is true in higher education. Students who are able to rediscover a playful mindset leave the classroom with something far more valuable than a good grade. They develop confidence, are more willing to contribute unusual ideas and learn how to adapt when the unexpected happens. These are precisely the qualities employers increasingly tell us they need.

Yet the creative and cultural industries demonstrate their growing value. Success increasingly depends on collaboration, adaptability and creative confidence, all qualities that are fostered through arts-based learning and increasingly sought in every kind of workplace.

Playfulness isn’t a distraction from serious learning. It’s one of the most powerful ways we have of preparing students for a future in which the most valuable skills may be the most deeply human ones: listening, presence, responsiveness, imagination and a capacity to create meaning together.

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.

Will learning curated by employers replace degrees? — from universityworldnews.com by Louise Nicol

If universities do not future-proof their offer through deeper and more credible partnerships with employers and industry, what exactly prevents employers from educating and training people themselves?

This is why the future of higher education depends on far deeper and more operational partnerships with industry. Not symbolic advisory boards or occasional guest lectures but genuine co-design of curricula, shared ownership of applied projects and clear accountability for graduate capability.

Universities that integrate live industry problems, cross-faculty collaboration and work-based learning into the core of their programmes make themselves harder to replace. Those that acknowledge the existence of external learning platforms and deliberately build them into a broader educational journey strengthen rather than weaken their position.

The real risk for universities is not replacement but marginalisation. Employers will not abandon universities out of hostility or ideology. They will do so pragmatically if universities fail to add distinctive value beyond what employers can now deliver themselves.

What a disco ball teaches us about learning and leadership [Flannery]

On 06/19/2026, in Bias | Diversity Equity & Inclusion (DE&I), colleges, community colleges, education, higher education, ideas, instructional design, K-12 related, leadership, learning, learning ecosystem, Learning Experience Design, liberal arts, neurodivergent/special needs, student-related, teaching & learning, UK, universities, by Daniel Christian

What a disco ball teaches us about learning and leadership — from timeshighereducation.com by Lauren Flannery
By acknowledging that perspectives are evolving and relational, educators and leaders can encourage contribution and connection without sacrificing what makes people distinct

It also shows us that difference does not always need to be resolved. In teaching, learning and leadership, the aim is not to create uniformity but to create conditions in which different people can contribute, connect and shine without losing what makes them distinct.

In classrooms, inclusion is sometimes approached as ensuring access to the same knowledge, resources and opportunities for all students. The beach ball helps here: it encourages us to explore multiple perspectives. But the disco ball pushes us further to explore how learning environments can support students to bring their experiences, identities and knowledge into the room – not to smooth them out but to draw from them.

Designing for multiple perspectives also means recognising that expressing an opinion is not only about confidence; it is also about conditions. People are more likely to speak when they feel their contribution will be heard without being dismissed, appropriated or flattened. Creating those conditions may involve discussing uncertainty, welcoming challenge, slowing down decision-making or making space for quieter forms of participation. The aim is not to make everyone agree, but to allow different reflections to interact in ways that generate richer understanding.

Two years ago, AI broke assessment. Now, it’s helping us to reinvent it. — from linkedin.com by Dr. Philippa Hardman

Also from Dr. Hardman, see:

A new study shows AI helped deliver 1.5 years of maths progress in 8 weeks — here’s how. — from linkedin.com by Dr. Philippa Hardman

…a new study shows AI helped deliver 1.5 years of maths progress in 8 weeks — here’s how.

Google DeepMind just shared the results of a randomised trial involving 1,763 students. Half used Gemini’s “Guided Learning” to learn maths; half didn’t.

The result: the group working with AI gained the equivalent of 1.2 to 1.7 years of extra progress compared to those who didn’t.

It’s tempting to read this as “Gemini’s Guided Learning mode works!” But the key point here is that Gemini didn’t work alone….

Look closer, and what made the difference wasn’t just the tech — it was a great teacher making expert use of it.

Important insights from this PwC global jobs report — from linkedin.com by Ross Dawson & PwC

Inside the latest global research on school cellphone bans — from hechingerreport.org by Jill Barshay
First wave of studies raises questions about other digital distractions and cellphones at home

But the first wave of rigorous research on those policies — including two major U.S. studies — does not point neatly in one direction. Some studies have found modest academic gains from cellphone restrictions. Others have found little to no effect on test scores, even when student phone use dropped sharply. Some studies suggest benefits for low-achieving students, others for girls, and still others for boys. In some places, attendance or student well-being improved. In others, they didn’t.

The scientific process can be messy. Cultural differences may explain why the bans are more effective in some places than others. But almost any education reform will get different results in different places, even within a single country. And the current confusion may also stem from how difficult it is to study cellphone bans in the real world.

Ideally, researchers would randomly assign some students to surrender their phones while others kept them, and then measure the effect on academic performance — the equivalent of a clinical trial for an education policy. But those experiments are difficult to enforce in schools, and so far only one study, conducted among college students in India, has attempted a randomized controlled trial. It produced a notably strong improvement in course grades for lower achieving students.

Instead, most studies rely on rougher real world comparisons that capture only partial effects of cellphone restrictions.

Why universities must become flexible lifelong partners, not one-time providers [Sivarajah]

On 05/28/2026, in adjunct faculty, apprenticeships, business, career development, change, changing business models, colleges, community colleges, corporate / business world, credentialing, curriculum, Daniel S. Christian, Europe, faculty/staff, future of higher education, game-changing environment, generational differences, higher education, ideas, innovation, internships, leadership, learning ecosystem, lifelong learning, MBA, new business models, organizational change, pace of change, participation, pathways, professional development, skills, strategy, student-related, teaching & learning, technology (general), UK, United States, universities, vision/possibilities, workplace, by Daniel Christian

Why universities must become flexible lifelong partners, not one-time providers — from timeshighereducation.com by Sankar Sivarajah
As careers become increasingly non-linear and shaped by rapid change, universities must evolve beyond traditional degree provision, says Sankar Sivarajah. Here, he outlines strategies

From programmes to learning ecosystems
These pressures point towards a broader redefinition of higher education. Rather than viewing education as a one-time experience culminating in a degree, universities increasingly need to see themselves as partners in professional development across an entire career.

This means moving from a model centred on programmes to one focused on learning ecosystems that allow individuals to enter, leave and re-engage with higher education as their needs evolve.

Business schools may be particularly well placed to lead this shift because of their close engagement with employers and their long tradition of educating professionals at different stages of their careers.

But success will depend on more than introducing new modules or certificates. Universities must confront a fundamental question. Are the systems, structures and cultures that define higher education capable of supporting genuinely flexible learning?

The sector has already embraced the language of lifelong learning – the next step is ensuring that universities themselves are built to deliver it.

From DSC:
Long-time readers of this blog have seen this graphic of mine posted over the last 12+ years:
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Also relevant/see:

What if the undergraduate journey were a four-year internship? — from timeshighereducation.com by Michelle Seref
Treating work placements and co-curricular programmes as optional or supplementary misses deeper questions about whether traditional degrees prepare students for careers. Michelle Seref explains

Attending workshops or polishing a résumé in their final semester does not make students career-ready. They need to practise how to work – how to collaborate, navigate ambiguity, manage projects and apply knowledge in context – throughout their academic experience. The reality is that career readiness is not a co-curricular programme; it is an essential part of an integrated curriculum.

To be clear, employers do not expect classrooms to become training centres. What they are asking for – implicitly and explicitly – is graduates who can function in complex environments from day one. That means graduates who can work in teams, communicate professionally with stakeholders, adapt when plans change, apply theory to real constraints and learn continuously on the job.

These capabilities do not develop through passive learning. But experiential learning is often misunderstood as a single, high-impact activity: an internship, a capstone project or study abroad. In reality, its power comes from repetition and progression. One experience introduces exposure. A sequence of experiences builds competence.

We are proposing a paradigm shift: repositioning the undergraduate journey as a four-year professional internship rather than a continuation of the K-12 classroom environment.

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From DSC:
The problem with this innovative idea is that faculty often are not out in the “real world.” The best chance higher ed has to deliver on this idea is via the adjunct faculty members out there. Often, they are the ones practicing what they are teaching. They are constantly pulse-checking — and actively involved with — their industries and have more up-to-date, practical knowledge.

But this is a problem for traditional institutions of higher education, which have treated their adjunct faculty members poorly through the years. Adjunct faculty members hardly make minimum wage, have no benefits, no retirement plans, etc. — plus they have little to no say in faculty senates.

Organizational change would be a requirement.

Global AI Diffusion — Q1 2026 Trends and Insights [Microsoft]

On 05/21/2026, in 21st century, Africa, Artificial Intelligence/Agents/LLMs/and Related, Asia, Australia, Canada, emerging technologies, Europe, game-changing environment, global / globalization, human-computer interaction (HCI), India, innovation, Microsoft, platforms, trends, UK, United States, by Daniel Christian

Global AI Diffusion Q1 2026 Trends and Insights — from Microsoft

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Learning Ecosystems

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

Bring playfulness into teaching for collaboration and creativity [Taurel]

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