We have an opportunity right now to reorient the university around student experience—not as an aspiration, but as a necessity. I’m calling this shift TechEd, which I explore in detail in my LinkedIn series The TechEd Revolution.
AI poses a fundamental shift in how technology might empower students to own their discovery and educational journey, and to drastically reduce the friction that makes college so unappealing to so many.
To that end, we need to urgently redesign systems and opportunities around skills and competencies. That work should be far more advanced than it currently is. And one of the hardest challenges is rethinking how we operate as a workforce in academia.
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
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
The conventional explanation for this strategic vacuum points to the speed of technological change; it is moving too fast for institutions built for deliberation. That is true. . . and incomplete. The deeper issue is cultural.In fairness to higher education, many industries are struggling to keep up with the pace of AI advances. Higher education, however, moves even more slowly and is not built for the kind of transformational speed now underway. Getting institutional stakeholders to engage, rethink the work, and move faster may be the central challenge facing presidents and chancellors today, and that’s saying a lot in such volatile times.
From DSC: I highlighted this paragraph because it hits upon the key item involved here — culture.“The deeper issue is cultural.” I think that’s a very true statement.
Part of the culture and setup of many institutions includes giving faculty members full rein of their classes and their departments. Faculty members have a great deal of leeway and power in how they do things. So trying to get X faculty members to get on board — including the Department Chairs — is not an easy task.
Another part of culture involves being willing — or not — to change in the first place. Some institutions are like Google and are used to making changes and being more innovative. But those institutions are not the norm, at least in my experience. And this doesn’t even address another topic the article mentioned — the pace of these changes. As the authors point out, most institutions of traditional higher education are not equipped to deal with the current pace of change (nor are most of our other types of institutions and our corporations as well).
I’m going to end this posting with another brief excerpt from the article:
Institutions rooted in human relationships, committed to truth-seeking, and oriented toward the full development of persons play a central role. AI cannot manufacture the experience of mattering to another human being. It cannot model intellectual courage or ethical discernment. It cannot build the kind of community in which students discover who they are and what they believe.
These are not small things. They are, in fact, the things most worth doing. At their best, colleges and universities are not only preparing better workers but shaping individuals and strengthening society.
From DSC: I used to be able to bring up Firefly on the web and use it “free” of charge — I didn’t have to go purchase tokens or credits. (I was actually paying for the Adobe Creative Cloud Pro suite of tools…so it wasn’t really free.)
But the other day I was trying to figure out what the latest pricing is at Adobe with that suite of tools and the use of credits for AI-based features. They say Adobe Creative Cloud Pro users get 4000 credits a month. Well, I have that suite and I’m still getting prompted to purchase credits. Firefly for individuals runs from $9.99 (2,000 credits/month) to $139.91 per month (50,000 credits per month). Not inexpensive, right? Below are other items along these lines.
The Era of Affordable AI Is Over. What Comes Next? — from builtin.com by Ameya Kanitkar AI providers are shifting to usage-based billing for their services. AI fluency is more important now than ever to make the most of your tools to avoid unnecessary spending.
Summary: The era of cheap, flat-rate AI is ending as providers shift to usage-based billing. Every prompt now carries a direct cost, turning casual use into major budget risks, as seen when Uber depleted its 2026 AI budget in four months. Leaders must now track real-time value and token efficiency.
For a brief window, companies had access to the most transformative technology in a generation at the cost of a streaming subscription. Tools like ChatGPT put AI within reach of anyone with a browser and time for experimentation, while GitHub Copilot came in at just $10 a month, with token costs remaining relatively low. In the beginning, experimentation felt cost-effective, easy and relatively low-risk.
But that era is ending, and the bill is coming due faster than a lot of enterprise leaders anticipated.
Tokenomics will be a hot topic of discussion on university campuses because, as Marc Watkins notes in this article, there is no realistic path forward to providing all students with access to advanced AI.
And now there is a third layer emerging. Institutions are waking up to a systems-level question they are likely not remotely prepared for. Who pays for AI? How are budgets managed when there are unclear token consumption pricing models? How is AI procured? Who decides what tools get used and by whom and who gets access and at what level?
Why Students Aren’t All In on AI—And What They Want From Colleges— from insidehighered.com by Colleen Flaherty New Student Voice data reveal students are embracing AI as a learning tool while worrying about dependence, career disruption and inconsistent institutional responses.
Read on for six takeaways from the survey and additional insights—including how institutions can start to close the gap between students’ optimism about AI as a learning tool and their faith in their colleges’ ability to help them navigate change.
Takeaway 1: More students are using AI than ever for coursework, while a significant share—20 percent—remain resisters.
Takeaway 2: “Worried about dependence” is the most common student stance on AI.
Takeaway 3: A majority of all students expect AI to somewhat (39 percent) or very (16 percent) negatively impact their career prospects.
Takeaway 4: Just one in 10 students says that their institution is handling AI’s rise very well, in a thoughtful and proactive way.
The National Microschooling Center just published its latest report, the American Microschools 2026 Sector Analysis, it’s most ambitious yet.
This report comprises the most thorough research published to date on microschools in America, examining 1,000 microschools located in all 50 states, the District of Columbia and Puerto Rico. Most are currently operating, with prelaunch microschools as well as those which have closed their doors also included.
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This 2026 edition of the annual American Microschools Sector Analysis series by the National Microschooling Center includes questions on a number of new topics, including ways microschools are impacted by different regulatory and policy stipulations, specifics of educational, business and operational aspects within the microschooling sector. Other questions revisit topics examined in previous studies, to illuminate trends over time and effects of growth and evolution on the ways microschools operate.
Education AI budgets are holding steady or increasing: Wasabi found that 98% of education organizations expect AI infrastructure budgets to increase or remain steady, with 46% planning increases.
Storage costs are the top AI implementation challenge: Half of education respondents cited data storage issues, including storage and access costs, as the No. 1 challenge for AI projects.
Cloud security and ROI remain pressure points: Only 47% feel confident keeping data unaltered and operational after a cyberattack, 44% lost access to public cloud data after an attack, and 37% of AI projects currently show positive ROI.
Early data from the 2025-26 academic year shows historically deep tuition discounts getting even deeper at private nonprofit colleges, according to a study released Monday from the National Association of College and University Business Officers.
For first-time undergraduates, the tuition discount rate at these colleges is projected to reach 57.1% in the current academic year. That’s up from 54.5% from the year before, and the highest point in the past decade. For all undergraduates, the discount rate is poised to hit 51.3%, up from 50% last year and above the most recent peak at 50.8% in 2022-23.
However, revenue declines across the undergraduate body pose difficulties for tuition-dependent colleges. It “suggests that retention alone is not enough to eliminate financial strain at many tuition-dependent institutions,” NACUBO said in its report.
For higher education to remain relevant, curricula must evolve. Here are some overarching recommendations for directions in higher education to bridge the skills gaps between universities and workplaces:
AI ethics and safety: Prepare students to navigate issues of fairness, bias, privacy, and societal impact.
Tackling complex questions: Emphasize open-ended challenges that blend structured and unstructured skills and reduce reliance on standardized tests and repetitive drills.
Critical thinking: Develop new assessments for judgment, creativity, and metacognition—essential to supervise AI outputs.
Human-AI synergy: Embed AI fluency across all disciplines, encouraging students to find the niches where human value is maximized.
Industry connection: Maintain close industry partnerships and collaborations including open innovation opportunities and collective intelligence approaches (Bharwaney & Sleeva, 2024).
Experiential learning and communities of practice are central to this vision. Internships, simulations, and cross-disciplinary projects can help students practice human-AI collaboration, resilience, and decision-making in environments that mirror the workplace’s ambiguity and complexity.
Universities that condemn the use of AI by students risk isolating themselves from the realities of today’s workplace, where interns and new hires are expected to be or quickly become adept at using AI for routine tasks and complex projects.
We welcome back Sabastian Niles, President and Chief Legal Officer at Salesforce, to discuss his recent “Open Letter to Law Firms.” As the legal industry hits a critical inflection point, Sabastian argues that the era of “AI theater” and small-scale pilots is over.
The conversation dives deep into the Innovator’s Dilemma facing law firms, the shift toward agentic AI, and how firms must reimagine their business models to remain competitive. Sabastian highlights that legal professionals are uniquely positioned to lead the charge in trusted AI transformation, provided they embrace transparency, data integration, and shared efficiency gains with their clients.
Three Realities for the Modern Legal Firm To lead in this landscape, there are three realities every firm leader must understand:
Competition is intensifying: …
Client expectations will reshape the market: Clients are no longer asking whether firms use AI. Rather, they’re expecting to see the benefits of that transformation passed directly to them. They expect more for less but are not simply seeking lower costs – they want more insight, more speed, and more value for every dollar of their budget. And law firms, which operate at the center of data, ethics, and risk, have outsized influence over the structure and deployment of trusted AI across all industries. Some clients, like Salesforce, are even creating agentic tools to improve the law firm’s experience when working with clients. …
Unified client intelligence is at the heart of legal strategy: …
Are AI First Firms a Threat To Biglaw? — from legallydisrupted.com by Zach Abramowitz and Logan Brown Episode 49 features AI first law firm founder Logan Brown
Is Big Law about to become the Yellow Pages? Hey, I didn’t say it, but ex-Cooley lawyer turned AI first law firm Logan Brown did. The question is do I agree?
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Instead of the traditional billable hour, they charge flat fees like $100 for a contract review or $50 to ask a lawyer a quick question via chat. She’s already got over 40 attorneys on the platform. And in a departure from the traditional partnership track, she actually chose to raise venture capital so she could scale the firm like a tech company and tackle the access-to-justice gap.
From DSC: LOVE to hear anything and everything regarding efforts to address the access-to-justice gap here in the United States!!! Along these lines, also see:
“Legal services are out of reach for many people and small businesses, and the gap is widening,” Anthropic said in its announcement. “We’re working with the Free Law Project, Justice Technology Association and other legal aid and public service organizations to help make legal services more affordable and available.”
That makes this the first time that a leading AI company is explicitly naming access to justice as a foundational pillar, JTA says, with Anthropic positioning the initiative as “investing in the premise that AI should expand access to justice — making legal services more affordable and available.”
What AI hallucinations in law actually are
In a legal context, AI hallucinations are one of two things. They’re either citations to cases or statutes that don’t exist, or citations to real authorities for propositions those authorities don’t actually support.
The first kind is the one making headlines. A lawyer or pro se litigant uses a general-purpose chatbot like ChatGPT, Claude, Gemini, Copilot, or Grok to help draft a brief. The model, predicting the statistically likely next word, decides a citation belongs in a particular spot, and produces one. The reporter might be real. The volume number might fall within the right range. The Bluebook formatting is often better than what most associates produce. The case itself just doesn’t exist.
The second kind is older than AI. Lawyers have always occasionally cited a case for a proposition that the case doesn’t stand for. AI has made this kind of error easier to commit and easier to catch.
A dangerous mind — from by Jordan Furlong Generative AI is a tireless genius with no boundaries. Use it carelessly, and it can usurp your voice, overwrite your ideas, and steal your originality. Make sure you safeguard your capacity to think.
Don’t let the genius do the hard work for you. The more incisive and unique your own thinking — the more you battle and struggle and eventually succeed in getting your ideas and insights out — the more you can benefit from the AI’s complementary improvements. The great irony of Gen AI is that it actually makes your own cognitive processes your most valuable asset.
So safeguard your mind. Defend your right to think as only you can. And if you don’t want AI to replace you, then don’t send it a written invitation.
The pilot phase is over. After two years of experimentation for legal departments, 2026 will be the year AI moves from “interesting tool” to “operational infrastructure,” whether they’re ready or not. We surveyed predictions from Gartner, Forrester, McKinsey, and other leading legal tech analysts to identify where expert consensus is forming. The implications for AI governance, outside counsel relationships, and regulatory compliance are significant.
LinkedIn Grad’s Guide 2026: Starting your career in the AI era — from linkedin.com by Gianna Prudente To help you head off in the right direction, we’ve identified where those starting their careers are finding opportunity, based on data from millions of LinkedIn member profiles.
While all of this is happening, colleges are still catching up. Many students are graduating without having spent much time learning how AI actually fits into day-to-day work — even as employers seek out those exact skills.
“Colleges are moving into an era of, we’ll let the faculty decide, which leads to a very uneven experience for students because some faculty are really into AI and other faculty are not,” says Jeff Selingo, a higher education strategist. “Employers are the same; they don’t really know how to act around early careers.”
Taken together, new grads are entering a uniquely challenging environment: fewer traditional entry points, slower turnover and a workplace that’s evolving faster than the systems preparing people for it.
For a few moments, all was quiet except the classroom’s ticking radiators. Then, a teary-eyed confession: one of the ostensible authors said she only used AI because she was scared of looking stupid, of being criticized for bad writing. She said she loved writing stories and hated having used AI. But she couldn’t stop herself, recounting a sequence similar to an addict’s descent: at first she fed her story into AI for a grammar check, it suggested line edits and she accepted, then it asked if she wanted structural edits, then it offered to rewrite the entire piece.
The other would-be author admitted he had never written a short story before and he had an idea but didn’t know where to start. I asked him why he didn’t reach out to me for help. He shrugged.
One of the other students raised her hand, saying she didn’t understand why it was bad for AI to write stories as long as the stories are based on their ideas. More students spoke: one wanted to know how using AI was any different from using a human editor. Another wanted me to answer why, at a university that launched one of the world’s first AI research programs in 1959, were we even having this debate? Isn’t AI meant to make everyone’s life easier? Less stressful? Isn’t the point of AI to free humans from the tedium of rote tasks?
The conversation that followed their confessions was one of the most productive teaching moments of my eight years at MIT. Writing, I told them, isn’t supposed to be easy, and of course it can be tedious but that doesn’t make it rote. Writing isn’t just the production of sentences – it’s the training of endurance by way of sustained attention. It’s a way of learning what one thinks by attempting to say it.
This $10K AI School Promises to Future-Proof Your Career — from builtin.com by Matthew Urwin Khan Academy, TED and ETS are starting a new program to equip students and professionals with the skills to thrive in an increasingly AI-driven economy. Here’s what you need to know.
Summary: The Khan TED Institute is a higher-education program that will teach students and workers how to use AI through interactive learning. The program’s AI-centric curriculum is an unproven approach, though, casting doubt on whether it will actually improve learning outcomes and career prospects.
The biggest AI risk that L&D faces isn’t that it gets left behind: it’s that we build more — and flood the organisation with meh-quality content nobody needed in the first place.
In this post, I’ll make the case that:
The L&D job has just split in two — and most of us are still working on the wrong half.
There’s a new operating model coming for the role, and it’s already running inside a lot of the companies you’ve heard of.
The smartest critique of everything I’m about to argue comes from Ethan Mollick — and I think he’s half right.
The question we’ve been asking for the last two years — “how do I get faster at building?” — was the wrong one.
The real question is: can I look at fifteen AI-generated learning assets and decide which three are worth scaling — and put my name to that decision?
