From DSC:
The vision that I’ve been tracking for well over a decade begins with this graphic:
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Could LearnVector be this next-generation company/platform? Perhaps. Time will tell.
Per Matt Tower via The EdSheet Vol. 38 dated 7/31/26:
Agilities — from agilities.org by the DeBruce Foundation; via Paul Fain
Help students build confidence and prepare for bright careers!
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Also from Paul Fain, see:
Unlocking Opportunity: Progress on Moving More Students Toward Good Jobs — from highered.aspeninstitute.org
Released in partnership with the Community College Research Center (CCRC), Unlocking Opportunity: Progress on Moving More Students Toward Good Jobs highlights early outcomes from the first 10 colleges in the Unlocking Opportunity network. The report demonstrates that community colleges can rapidly increase enrollment in high-value workforce and transfer pathways while reducing enrollment in or improving programs with weaker labor market and bachelor’s degree outcomes.
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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
From DSC:
Could this be a part of our future learning ecosystems? Education as a personalized content feed.
Coursera wants users to learn through shorter, faster content — from digitaltrends.com by Moinak Pal
Coursera wants online learning to feel more like TikTok
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Online learning platform Coursera is taking a page straight out of TikTok’s playbook. The company has launched a new AI-powered feed designed to serve short-form educational content in a scrollable, personalized format, signaling a major shift in how digital learning platforms may try to keep users engaged.
The feature introduces bite-sized video lessons, clips, and explainers curated through artificial intelligence based on a user’s interests, learning habits, career goals, and previous course activity. Instead of committing to hour-long lectures or full certification programs upfront, users can now discover short educational snippets designed to make learning feel more casual, accessible, and addictive.
Users scroll through a feed of short educational videos and AI-curated learning moments covering topics ranging from coding and business to AI, productivity, data science, and personal development.
Want Students to Build a Healthier Relationship With Technology? Start With The Arts — from techlearning.com by Adrianna Marshall
Arts classrooms demonstrate what technology integration at its best can look like
But at a moment defined by rapid AI adoption and ongoing debates about screen time, the argument for protecting and investing in arts education needs to take on a new tone. The arts continue to be one of the most effective places in school for students to build healthier, more intentional relationships with technology.
In short, in the age of AI, we need the arts more than ever.
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Digital composition software, notation tools, and recording platforms allow students to experiment, revise, and refine their ideas in ways that would have been far more time-consuming a decade ago. Students can layer tracks, hear immediate playback, annotate their own scores, and collaborate across devices. The same is true in other contexts besides music; in visual arts, for instance, a variety of digital drawing and painting platforms enable students to practice with new mediums, styles, and techniques without having to worry about supplies or messes. But in either case, the core intellectual work of looking and listening critically, understanding structure, and making aesthetic choices remains entirely human and part of the learning.
From DSC:
I agree. At one of my previous positions, I spent 10 years supervising a digital studio — helping professors and students use a variety of applications to create things. The applications were from Adobe, Apple, and a variety of smaller vendors. The deliverables could be graphics, edited soundtracks, music, videos, flyers, posters, collages, edited photographs, presentations, websites, and more. I longed for people to discover the power of multimedia to communicate their messages, tell stories, stir emotion, powerfully engage themselves (and others), and unleash their creativity.
There were several obstacles to our digital studio being more impactful at that institution. It was under the IT department, not the academic side of the house. It was in the basement of the library, where few students and faculty traveled. During those years, it was highly uncommon for faculty members to require multimedia-based assignments — so many students had to WANT to develop these skills on their own time. The majority of students didn’t see the value in developing the types of digital skills that we were trying to build…or they didn’t have the time.
Also relevant/see:
This Is a Hard Time to Start a Career. These Two Words Can Help. — a gifted article from nytimes.com by Jodi Kantor
Advice on building a rewarding work life, even amid employment gloom.
If you’re sweating about what field to enter, here are a few things you can do now. Buy a cheap, thin notebook. Keep it on you. Every week, make a practice of writing down which actions you enjoy and which ones you hate, whom you like being around and whom you can’t stand. Keep running lists of what you’re good at and what ideas move you. Notice yourself.
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Look to your friends instead. Think about what roles you take on with them: math tutor, party planner, psychologist, workout coach. These answers often reveal truths that our résumés do not. In social relationships, we aren’t bound by suffocating expectations about our future. Our friends have needs, and by noticing how we respond to them, we can learn who we are.
There is a wiser way to seize the future, which is to think about need. What is your own assessment of what society will need most during your working years, the next four or five decades? What kind of care; what kind of products; what kind of information?
The people I see thriving at work are the ones who chased some bigger need — not imposed by hollow conventional wisdom, but articulated through independent observation. Craft gives their work authority. Need gives it propulsion.
“Learning ecosystems begin with people.” — Getting Smart
ASU/GSV Summit
There’s something about walking into a space like the ASU+GSV Summit that feels a little like stepping into a living, breathing idea. You hear fragments of possibility in passing conversations, see it in the way people lean in a little closer during sessions, feel it in the quiet moments when something lands and you know it’s going to stay with you. This year, what lingered wasn’t just the talk of innovation; it was a deeper pull toward something more human. A reminder that before we build better systems, we have to create better conditions for dreaming. And there’s a kind of quiet joy that emerges when educators find each other in that work, when ideas connect, and you can feel the bridges across networks and ecosystems getting stronger in real time.
And dreaming is not a given. It requires space, safety, and adults who understand the weight of what they’re holding. The most powerful moments weren’t about what we can do for learners, but how we show up with them. Adults who are still learning, still stretching, still willing to have their thinking reshaped are the ones who make room for young people to imagine beyond what they’ve seen. That kind of space doesn’t happen by accident. It’s protected. It’s intentional. It’s built by people who know their non-negotiables, who draw clear lines around dignity and belonging so learners can take risks without fear of losing themselves in the process.
Across conversations on pathways, experience, and AI, there was a steady undercurrent. Knowledge alone isn’t carrying the day anymore. Young people need chances to test, to try, to wrestle with ideas in real contexts. That’s where wisdom starts to take shape. AI showed up as a partner in that work, not the main character, but a tool that can expand thinking when used well. Still, the heartbeat of it all is human. It’s the relationships, the networks, the shared belief that we don’t have to do this alone. When adults come together to learn, to challenge each other, and to build something bigger than their own corner, they create the kind of ecosystems where young people don’t just prepare for the future, they begin to shape it.
Also from Getting Smart:
The quest to build a better AI tutor — from hechingerreport.org by Jill Barshay
Researchers make progress with an older ed tech idea: personalized practice
One promising idea has less to do with how an AI tutor explains concepts and more with what it asks students to practice next.
A team at the University of Pennsylvania, which included some AI skeptics, recently tested this approach in a study of close to 800 Taiwanese high school students learning Python programming. All the students used the same AI tutor, which was designed not to give away answers.
But there was one key difference. Half the students were randomly assigned to a fixed sequence of practice problems, progressing from easy to hard. The other half received a personalized sequence with the AI tutor continuously adjusting the difficulty of each problem based on how the student was performing and interacting with the chatbot.
The idea is based on what educators call the “zone of proximal development.” When problems are too easy, students get bored. When they’re too hard, students get frustrated. The goal is to keep students in a sweet spot: challenged, but not overwhelmed.
The researchers found that students in the personalized group did better on a final exam than students in the fixed problem group. The difference was characterized as the equivalent of 6 to 9 months of additional schooling, an eye-catching claim for an after-school online course that lasted only five months.
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To address this, Chung’s team combined a large language model with a separate machine-learning algorithm that analyzes how students interact with the online course platform — how they answer the practice questions, how many times they revise or edit their coding, and the quality of their conversations with the chatbot — and uses that information to decide which problem to serve up next.
