Half A Million Students Given ChatGPT As CSU System Makes AI History — from forbes.com by Dan Fitzpatrick

The California State University system has partnered with OpenAI to launch the largest deployment of AI in higher education to date.

The CSU system, which serves nearly 500,000 students across 23 campuses, has announced plans to integrate ChatGPT Edu, an education-focused version of OpenAI’s chatbot, into its curriculum and operations. The rollout, which includes tens of thousands of faculty and staff, represents the most significant AI deployment within a single educational institution globally.

We’re still in the early stages of AI adoption in education, and it is critical that the entire ecosystem—education systems, technologists, educators, and governments—work together to ensure that all students globally have access to AI and develop the skills to use it responsibly

Leah Belsky, VP and general manager of education at OpenAI.




HOW educators can use GenAI – where to start and how to progress — from aliciabankhofer.substack.com by Alicia Bankhofer
Part of 3 of my series: Teaching and Learning in the AI Age

As you read through these use cases, you’ll notice that each one addresses multiple tasks from our list above.

1. Researching a topic for a lesson
2. Creating Tasks For Practice
3. Creating Sample Answers
4. Generating Ideas
5. Designing Lesson Plans
6. Creating Tests
7. Using AI in Virtual Classrooms
8. Creating Images
9. Creating worksheets
10. Correcting and Feedback


 
 

Degree in hand, jobs out of reach: Why recent grads are struggling in a competitive market — from cnn.com by Nayeli Jaramillo-Plata; via Ryan Craig

Bellebuono’s story isn’t unique. A recent study from the Federal Reserve Bank of New York reported the widest unemployment gap between new graduates and experienced degree holders since the 1990s.

The struggle to find work
The unemployment gap is partly due to the increase in competition and changing employer expectations, said David Deming, professor of public policy at the Harvard Kennedy School.

Skill requirements for entry-level roles are higher today than a decade ago, he said. But the change has been gradual from year to year.

 

How to Make Learning as Addictive as Social Media | Duolingo’s Luis Von Ahn | TED — from youtube.com; via Kamil Banc at AI Adopter

When technologist Luis von Ahn was building the popular language-learning platform Duolingo, he faced a big problem: Could an app designed to teach you something ever compete with addictive platforms like Instagram and TikTok? He explains how Duolingo harnesses the psychological techniques of social media and mobile games to get you excited to learn — all while spreading access to education across the world.
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DeepSeek: How China’s AI Breakthrough Could Revolutionize Educational Technology — from nickpotkalitsky.substack.com by Nick Potkalitsky
Can DeepSeek’s 90% efficiency boost make AI accessible to every school?

The most revolutionary aspect of DeepSeek for education isn’t just its cost—it’s the combination of open-source accessibility and local deployment capabilities. As Azeem Azhar notes, “R-1 is open-source. Anyone can download and run it on their own hardware. I have R1-8b (the second smallest model) running on my Mac Mini at home.”

Real-time Learning Enhancement

  • AI tutoring networks that collaborate to optimize individual learning paths
  • Immediate, multi-perspective feedback on student work
  • Continuous assessment and curriculum adaptation

The question isn’t whether this technology will transform education—it’s how quickly institutions can adapt to a world where advanced AI capabilities are finally within reach of every classroom.


Over 100 AI Tools for Teachers — from educatorstechnology.com by Med Kharbach, PhD

I know through your feedback on my social media and blog posts that several of you have legitimate concerns about the impact of AI in education, especially those related to data privacy, academic dishonesty, AI dependence, loss of creativity and critical thinking, plagiarism, to mention a few. While these concerns are valid and deserve careful consideration, it’s also important to explore the potential benefits AI can bring when used thoughtfully.

Tools such as ChatGPT and Claude are like smart research assistants that are available 24/7 to support you with all kinds of tasks from drafting detailed lesson plans, creating differentiated materials, generating classroom activities, to summarizing and simplifying complex topics. Likewise, students can use them to enhance their learning by, for instance, brainstorming ideas for research projects, generating constructive feedback on assignments, practicing problem-solving in a guided way, and much more.

The point here is that AI is here to stay and expand, and we better learn how to use it thoughtfully and responsibly rather than avoid it out of fear or skepticism.


Beth’s posting links to:

 


Derek’s posting on LinkedIn


From Theory to Practice: How Generative AI is Redefining Instructional Materials — from edtechinsiders.substack.com by Alex Sarlin
Top trends and insights from The Edtech Insiders Generative AI Map research process about how Generative AI is transforming Instructional Materials

As part of our updates to the Edtech Insiders Generative AI Map, we’re excited to release a new mini market map and article deep dive on Generative AI tools that are specifically designed for Instructional Materials use cases.

In our database, the Instructional Materials use case category encompasses tools that:

  • Assist educators by streamlining lesson planning, curriculum development, and content customization
  • Enable educators or students to transform materials into alternative formats, such as videos, podcasts, or other interactive media, in addition to leveraging gaming principles or immersive VR to enhance engagement
  • Empower educators or students to transform text, video, slides or other source material into study aids like study guides, flashcards, practice tests, or graphic organizers
  • Engage students through interactive lessons featuring historical figures, authors, or fictional characters
  • Customize curriculum to individual needs or pedagogical approaches
  • Empower educators or students to quickly create online learning assets and courses

On a somewhat-related note, also see:


 

Law School Trends 2025: AI, Bar Exam Changes & Career Shifts — from jdjournal.com by Maria Lenin Laus
This comprehensive guide explores these significant trends, their implications, and what to expect in the coming years.

AI’s Growing Role in Legal Education
AI-powered platforms are being utilized for legal research, document automation, and predictive analytics. Tools like AI-driven case analysis systems are helping students develop advanced research and drafting capabilities.

3.2. Integration of AI into Law School Curricula

  • AI-Powered Research Labs: Schools are incorporating AI-driven tools to assist students in case law research and document drafting.
  • Ethics and AI Courses: New courses explore the ethical implications and legal ramifications of AI in law practice.
  • AI-Assisted Exam Prep: Intelligent tutoring systems and adaptive learning platforms are enhancing bar exam preparation.

3.3. Future Outlook
By 2030, AI proficiency will be a standard expectation for law graduates. Students who fail to familiarize themselves with AI tools risk falling behind in a technology-driven legal market.

4. The Bar Exam Is Changing: The NextGen Bar Exam
The introduction of the NextGen Bar Exam in 2026 marks a significant shift in how aspiring lawyers are tested. Unlike the traditional exam, this new format emphasizes practical legal skills over rote memorization.

4.1. Key Differences Between the Traditional and NextGen Bar Exam
The NextGen Bar Exam replaces multiple-choice and essay-based testing with performance-based tasks that assess practical legal skills. It aims to better prepare graduates for real-world practice by focusing on essential competencies rather than memorization.

4.2. Future Outlook
By 2028, at least half of U.S. states are expected to adopt the NextGen Bar Exam. Law schools will need to adjust their curricula accordingly to prepare students for a more skills-focused licensing process.


Also see:

 

Four objectives to guide artificial intelligence’s impact on higher education — from timeshighereducation.com by Susan C. Aldridge
How can higher education leaders manage both the challenge and the opportunity artificial intelligence presents? Here are four objectives to guide the way

That’s why, today, the question I’m asking is: How best can we proactively guide AI’s use in higher education and shape its impact on our students, faculty and institution? The answer to that broad, strategic question lies in pursuing four objectives that, I believe, are relevant for many colleges and universities.


In This Week’s Gap Letter — by Ryan Craig

Learning to use business software is different from learning to think. But if the software is sufficiently complex, how different is it really? What if AI’s primary impact on education isn’t in the classroom, but rather shifting the locus of learning to outside the classroom?

Instead of sitting in a classroom listening to a teacher, high school and college students could be assigned real work and learn from that work. Students could be matched with employers or specific projects provided by or derived from employers, then do the work on the same software used in the enterprise. As AI-powered digital adoption platforms (DAPs) become increasingly powerful, they have the potential to transform real or simulated work into educational best practice for students only a few years away from seeking full-time employment.

If DAPs take us in this direction, four implications come to mind….


The Impact of Gen AI on Human Learning: a research summary — from drphilippahardman.substack.com by  Dr. Philippa Hardman
A literature review of the most recent & important peer-reviewed studies

In this week’s blog post, I share a summary of five recent studies on the impact of Gen AI on learning to bring you right up to date.

Implications for Educators and Developers

For Educators:

  • Combine ChatGPT with Structured Activities: …
  • Use ChatGPT as a Supplement, Not a Replacement:…
  • Promote Self-Reflection and Evaluation:

For Developers:

  • Reimagine AI for Reflection-First Design: …
  • Develop Tools that Foster Critical Thinking: …
  • Integrate Adaptive Support: …

Assessing the GenAI process, not the output — from timeshighereducation.com by Paul McDermott, Leoni Palmer, and Rosemary Norton
A framework for building AI literacy in a literature-review-type assessment

In this resource, we outline our advice for implementing an approach that opens AI use up to our students through a strategy of assessing the process rather than outputs.

To start with, we recommend identifying learning outcomes for your students that can be achieved in collaboration with AI.


What’s New: The Updated Edtech Insiders Generative AI Map — from edtechinsiders.substack.com by Sarah Morin, Alex Sarlin, and Ben Kornell
A major expansion on our previously released market map, use case database, and AI tool company directory.

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Tutorial: 4 Ways to Use LearnLM as a Professor — from automatedteach.com by Graham Clay
Create better assessments, improve instructions and feedback, and tutor your students with this fine-tuned version of Gemini.

I cover how to use LearnLM

  • to create sophisticated assessments that promote learning
  • to develop clearer and more effective assignment instructions
  • to provide more constructive feedback on student work, and
  • to support student learning through guided tutoring
 

6% of Faculty Feel Supported on AI?! — from automatedteach.com by Graham Clay
Plus, a webinar on building AI tutors this Friday.

The Digital Education Council just released their Global AI Faculty Survey of 1,681 faculty members from 52 institutions across 28 countries, and the findings are eye-opening. (Click here if you missed their analogous survey of students.)

While 86% of faculty see themselves using AI in their future teaching [p. 21], only 6% strongly agree that their institutions have provided sufficient resources to develop their AI literacy [p. 35].

This is a concerning gap between the recognized power of AI and institutional support, and it’s a clear signal about where higher education needs to focus in 2025.

Speaking with faculty about AI around the world, I’ve seen this firsthand. But let’s dig into the survey’s findings.
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Why the gap? Well, one explanation is that faculty lack institutional support.

The survey reveals that…

  • 80% of faculty don’t find their institutional AI guidelines comprehensive [p. 32]
  • 80% say their institutions haven’t made clear how AI can be used in teaching [p. 33]
  • The top barrier to AI adoption, at 40%? “I don’t have time or resources to explore AI” [p. 9]
  • The second-highest barrier, at 38%? “I am not sure how to use AI in my teaching” [p. 9]

From DSC:


I was in a teaching and learning group for 10+ years (and in several edtech-related positions before that). We had a senior staff established there but we were mainly called upon for edtech, instructional technology, learning spaces, or LMS types of tasks and questions. Though we could have brought a lot of value to the pedagogical table, the vast majority of the faculty wanted to talk to other faculty members. Our group’s hard-earned — and expensive — expertise didn’t count. We ourselves were teaching classes..but not enough to be on par with the faculty members (at least in their minds). They didn’t seek us out. Perhaps we should have gone door to door, but we didn’t have the resources to do that. 

Book groups were effective when the T&L group met with faculty members to discuss things. The discussions were productive. And in those groups, we DID have a seat at the pedagogical table.

But I’m not going to jump on the “we don’t have enough support” bandwagon. Faculty members seek out other faculty members. In many cases, if you aren’t faculty, you don’t count. 

So if I were still working and I was in a leadership position, I would sponsor some book study groups with faculty and personnel from teaching and learning centers. Topics for those books could be:

  • What AI is
  • What those techs can offer
  • What the LMS vendors are doing in this regard
  • and ideas on how to use AI in one’s teaching 
 

Your AI Writing Partner: The 30-Day Book Framework — from aidisruptor.ai by Alex McFarland and Kamil Banc
How to Turn Your “Someday” Manuscript into a “Shipped” Project Using AI-Powered Prompts

With that out of the way, I prefer Claude.ai for writing. For larger projects like a book, create a Claude Project to keep all context in one place.

  • Copy [the following] prompts into a document
  • Use them in sequence as you write
  • Adjust the word counts and specifics as needed
  • Keep your responses for reference
  • Use the same prompt template for similar sections to maintain consistency

Each prompt builds on the previous one, creating a systematic approach to helping you write your book.


Using NotebookLM to Boost College Reading Comprehension — from michellekassorla.substack.com by Michelle Kassorla and Eugenia Novokshanova
This semester, we are using NotebookLM to help our students comprehend and engage with scholarly texts

We were looking hard for a new tool when Google released NotebookLM. Not only does Google allow unfettered use of this amazing tool, it is also a much better tool for the work we require in our courses. So, this semester, we have scrapped our “old” tools and added NotebookLM as the primary tool for our English Composition II courses (and we hope, fervently, that Google won’t decide to severely limit its free tier before this semester ends!)

If you know next-to-nothing about NotebookLM, that’s OK. What follows is the specific lesson we present to our students. We hope this will help you understand all you need to know about NotebookLM, and how to successfully integrate the tool into your own teaching this semester.


Leadership & Generative AI: Hard-Earned Lessons That Matter — from jeppestricker.substack.com by Jeppe Klitgaard Stricker
Actionable Advice for Higher Education Leaders in 2025

AFTER two years of working closely with leadership in multiple institutions, and delivering countless workshops, I’ve seen one thing repeatedly: the biggest challenge isn’t the technology itself, but how we lead through it. Here is some of my best advice to help you navigate generative AI with clarity and confidence:

  1. Break your own AI policies before you implement them.
  2. Fund your failures.
  3. Resist the pilot program. …
  4. Host Anti-Tech Tech Talks
  5. …+ several more tips

While generative AI in higher education obviously involves new technology, it’s much more about adopting a curious and human-centric approach in your institution and communities. It’s about empowering learners in new, human-oriented and innovative ways. It is, in a nutshell, about people adapting to new ways of doing things.



Maria Anderson responded to Clay’s posting with this idea:

Here’s an idea: […] the teacher can use the [most advanced] AI tool to generate a complete solution to “the problem” — whatever that is — and demonstrate how to do that in class. Give all the students access to the document with the results.

And then grade the students on a comprehensive followup activity / presentation of executing that solution (no notes, no more than 10 words on a slide). So the students all have access to the same deep AI result, but have to show they comprehend and can iterate on that result.



Grammarly just made it easier to prove the sources of your text in Google Docs — from zdnet.com by Jack Wallen
If you want to be diligent about proving your sources within Google Documents, Grammarly has a new feature you’ll want to use.

In this age of distrust, misinformation, and skepticism, you may wonder how to demonstrate your sources within a Google Document. Did you type it yourself, copy and paste it from a browser-based source, copy and paste it from an unknown source, or did it come from generative AI?

You may not think this is an important clarification, but if writing is a critical part of your livelihood or life, you will definitely want to demonstrate your sources.

That’s where the new Grammarly feature comes in.

The new feature is called Authorship, and according to Grammarly, “Grammarly Authorship is a set of features that helps users demonstrate their sources of text in a Google doc. When you activate Authorship within Google Docs, it proactively tracks the writing process as you write.”


AI Agents Are Coming to Higher Education — from govtech.com
AI agents are customizable tools with more decision-making power than chatbots. They have the potential to automate more tasks, and some schools have implemented them for administrative and educational purposes.

Custom GPTs are on the rise in education. Google’s version, Gemini Gems, includes a premade version called Learning Coach, and Microsoft announced last week a new agent addition to Copilot featuring use cases at educational institutions.


Generative Artificial Intelligence and Education: A Brief Ethical Reflection on Autonomy — from er.educause.edu by Vicki Strunk and James Willis
Given the widespread impacts of generative AI, looking at this technology through the lens of autonomy can help equip students for the workplaces of the present and of the future, while ensuring academic integrity for both students and instructors.

The principle of autonomy stresses that we should be free agents who can govern ourselves and who are able to make our own choices. This principle applies to AI in higher education because it raises serious questions about how, when, and whether AI should be used in varying contexts. Although we have only begun asking questions related to autonomy and many more remain to be asked, we hope that this serves as a starting place to consider the uses of AI in higher education.

 

AI Is Unavoidable, Not Inevitable — from marcwatkins.substack.com by Marc Watkins

I had the privilege of moderating a discussion between Josh Eyler and Robert Cummings about the future of AI in education at the University of Mississippi’s recent AI Winter Institute for Teachers. I work alongside both in faculty development here at the University of Mississippi. Josh’s position on AI sparked a great deal of debate on social media:

To make my position clear about the current AI in education discourse I want to highlight several things under an umbrella of “it’s very complicated.”

Most importantly, we all deserve some grace here. Dealing with generative AI in education isn’t something any of us asked for. It isn’t normal. It isn’t fixable by purchasing a tool or telling faculty to simply ‘prefer not to’ use AI. It is and will remain unavoidable for virtually every discipline taught at our institutions.

If one good thing happens because of generative AI let it be that it helps us clearly see how truly complicated our existing relationships with machines are now. As painful as this moment is, it might be what we need to help prepare us for a future where machines that mimic reasoning and human emotion refuse to be ignored.


“AI tutoring shows stunning results.”
See below article.


From chalkboards to chatbots: Transforming learning in Nigeria, one prompt at a time — from blogs.worldbank.org by Martín E. De Simone, Federico Tiberti, Wuraola Mosuro, Federico Manolio, Maria Barron, and Eliot Dikoru

Learning gains were striking
The learning improvements were striking—about 0.3 standard deviations. To put this into perspective, this is equivalent to nearly two years of typical learning in just six weeks. When we compared these results to a database of education interventions studied through randomized controlled trials in the developing world, our program outperformed 80% of them, including some of the most cost-effective strategies like structured pedagogy and teaching at the right level. This achievement is particularly remarkable given the short duration of the program and the likelihood that our evaluation design underestimated the true impact.

Our evaluation demonstrates the transformative potential of generative AI in classrooms, especially in developing contexts. To our knowledge, this is the first study to assess the impact of generative AI as a virtual tutor in such settings, building on promising evidence from other contexts and formats; for example, on AI in coding classes, AI and learning in one school in Turkey, teaching math with AI (an example through WhatsApp in Ghana), and AI as a homework tutor.

Comments on this article from The Rundown AI:

Why it matters: This represents one of the first rigorous studies showing major real-world impacts in a developing nation. The key appears to be using AI as a complement to teachers rather than a replacement — and results suggest that AI tutoring could help address the global learning crisis, particularly in regions with teacher shortages.


Other items re: AI in our learning ecosystems:

  • Will AI revolutionise marking? — from timeshighereducation.com by Rohim Mohammed
    Artificial intelligence has the potential to improve speed, consistency and detail in feedback for educators grading students’ assignments, writes Rohim Mohammed. Here he lists the pros and cons based on his experience
  • Marty the Robot: Your Classroom’s AI Companion — from rdene915.com by Dr. Rachelle Dené Poth
  • Generative Artificial Intelligence: Cautiously Recognizing Educational Opportunities — from scholarlyteacher.com by Todd Zakrajsek, University of North Carolina at Chapel Hill
  • Personal AI — from michelleweise.substack.com by Dr. Michelle Weise
    “Personalized” Doesn’t Have To Be a Buzzword
    Today, however, is a different kind of moment. GenAI is now rapidly evolving to the point where we may be able to imagine a new way forward. We can begin to imagine solutions truly tailored for each of us as individuals, our own personal AI (pAI). pAI could unify various silos of information to construct far richer and more holistic and dynamic views of ourselves as long-life learners. A pAI could become our own personal career navigator, skills coach, and storytelling agent. Three particular areas emerge when we think about tapping into the richness of our own data:

    • Personalized Learning Pathways & Dynamic Skill Assessment: …
    • Storytelling for Employers:…
    • Ongoing Mentorship and Feedback: …
  • Speak — a language learning app — via The Neuron

 

The Rise of the Heretical Leader — from ditchthattextbook.com; a guest post by Dan Fitzpatrick

Now is the time for visionary leadership in education. The era of artificial intelligence is reshaping the demands on education systems. Rigid policies, outdated curricula, and reliance on obsolete metrics are failing students. A recent survey from Resume Genius found that graduates lack skills in communication, collaboration, and critical thinking. Consequently, there is a growing trend in companies hiring candidates based on skills instead of traditional education or work experience. This underscores the urgent need for educational leaders to prioritize adaptability and innovation in their systems. Educational leaders must embrace a transformative approach to keep pace.

[Heretical leaders] bring courage, empathy, and strategic thinking to reimagine education’s potential. Here are their defining characteristics:

  • Visionary Thinking: They identify bold, innovative paths to progress.
  • Courage to Act: These leaders take calculated risks to overcome resistance and inertia.
  • Relentless Curiosity: They challenge assumptions and seek better alternatives.
  • Empathy for Stakeholders: Understanding the personal impact of change allows them to lead with compassion.
  • Strategic Disruption: Their deliberate actions ensure systemic improvements.
    These qualities enable Heretical leaders to reframe challenges as opportunities and drive meaningful change.

From DSC:
Readers of this blog will recognize that I believe visionary leadership is extremely important — in all areas of our society, but especially within our learning ecosystems. Vision trumps data, at least in my mind. There are times when data can be used to support a vision, but having a powerful vision is more lasting and impactful than relying on data to drive the organization.

So while I’d vote for a different term other than “heretical leaders,” I get what Dan is saying and I agree with him. Such leaders are going against the grain. They are swimming upstream. They are espousing perspectives that others often don’t buy into (at least initially or for some time). 

Such were the leaders who introduced online learning into the K-16 educational systems back in the late ’90s and into the next two+ decades. The growth of online-based learning continues and has helped educate millions of people. Those leaders and the people who worked for such endeavors were going against the grain.

We haven’t seen the end point of online-based learning. I think it will become even more powerful and impactful when AI is used to determine which jobs are opening up, and which skills are needed for those jobs, and then provide a listing of sources of where one can obtain that knowledge and develop those skills. People will be key in this vision. But so will AI and personalized learning. It will be a collaborative effort.

By the way, I am NOT advocating for using AI to outsource our thinking. Also, having basic facts and background knowledge in a domain is critically important, especially to use AI effectively. But we should be teaching students about AI (as we learn more about it ourselves). We should be working collaboratively with our students to understand how best to use AI. It’s their futures at stake.


 

Students Pushback on AI Bans, India Takes a Leading Role in AI & Education & Growing Calls for Teacher Training in AI — from learningfuturesdigest.substack.com by Dr. Philippa Hardman
Key developments in the world of AI & Education at the turn of 2025

At the end of 2024 and start of 2025, we’ve witnessed some fascinating developments in the world of AI and education, from from India’s emergence as a leader in AI education and Nvidia’s plans to build an AI school in Indonesia to Stanford’s Tutor CoPilot improving outcomes for underserved students.

Other highlights include Carnegie Learning partnering with AI for Education to train K-12 teachers, early adopters of AI sharing lessons about implementation challenges, and AI super users reshaping workplace practices through enhanced productivity and creativity.

Also mentioned by Philippa:


ElevenLabs AI Voice Tool Review for Educators — from aiforeducation.io with Amanda Bickerstaff and Mandy DePriest

AI for Education reviewed the ElevenLabs AI Voice Tool through an educator lens, digging into the new autonomous voice agent functionality that facilitates interactive user engagement. We showcase the creation of a customized vocabulary bot, which defines words at a 9th-grade level and includes options for uploading supplementary material. The demo includes real-time testing of the bot’s capabilities in defining terms and quizzing users.

The discussion also explored the AI tool’s potential for aiding language learners and neurodivergent individuals, and Mandy presented a phone conversation coach bot to help her 13-year-old son, highlighting the tool’s ability to provide patient, repetitive practice opportunities.

While acknowledging the technology’s potential, particularly in accessibility and language learning, we also want to emphasize the importance of supervised use and privacy considerations. Right now the tool is currently free, this likely won’t always remain the case, so we encourage everyone to explore and test it out now as it continues to develop.


How to Use Google’s Deep Research, Learn About and NotebookLM Together — from ai-supremacy.com by Michael Spencer and Nick Potkalitsky
Supercharging your research with Google Deepmind’s new AI Tools.

Why Combine Them?
Faster Onboarding: Start broad with Deep Research, then refine and clarify concepts through Learn About. Finally, use NotebookLM to synthesize everything into a cohesive understanding.

Deeper Clarity: Unsure about a concept uncovered by Deep Research? Head to Learn About for a primer. Want to revisit key points later? Store them in NotebookLM and generate quick summaries on demand.

Adaptive Exploration: Create a feedback loop. Let new terms or angles from Learn About guide more targeted Deep Research queries. Then, compile all findings in NotebookLM for future reference.
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Getting to an AI Policy Part 1: Challenges — from aiedusimplified.substack.com by Lance Eaton, PH.D.
Why institutional policies are slow to emerge in higher education

There are several challenges to making policy that make institutions hesitant to or delay their ability to produce it. Policy (as opposed to guidance) is much more likely to include a mixture of IT, HR, and legal services. This means each of those entities has to wrap their heads around GenAI—not just for their areas but for the other relevant areas such as teaching & learning, research, and student support. This process can definitely extend the time it takes to figure out the right policy.

That’s naturally true with every policy. It does not often come fast enough and is often more reactive than proactive.

Still, in my conversations and observations, the delay derives from three additional intersecting elements that feel like they all need to be in lockstep in order to actually take advantage of whatever possibilities GenAI has to offer.

  1. Which Tool(s) To Use
  2. Training, Support, & Guidance, Oh My!
  3. Strategy: Setting a Direction…

Prophecies of the Flood — from oneusefulthing.org by Ethan Mollick
What to make of the statements of the AI labs?

What concerns me most isn’t whether the labs are right about this timeline – it’s that we’re not adequately preparing for what even current levels of AI can do, let alone the chance that they might be correct. While AI researchers are focused on alignment, ensuring AI systems act ethically and responsibly, far fewer voices are trying to envision and articulate what a world awash in artificial intelligence might actually look like. This isn’t just about the technology itself; it’s about how we choose to shape and deploy it. These aren’t questions that AI developers alone can or should answer. They’re questions that demand attention from organizational leaders who will need to navigate this transition, from employees whose work lives may transform, and from stakeholders whose futures may depend on these decisions. The flood of intelligence that may be coming isn’t inherently good or bad – but how we prepare for it, how we adapt to it, and most importantly, how we choose to use it, will determine whether it becomes a force for progress or disruption. The time to start having these conversations isn’t after the water starts rising – it’s now.


 

The number of 18-year-olds is about to drop sharply, packing a wallop for colleges — and the economy — from hechingerreport.org by Jon Marcus
America is about to go over the ‘demographic cliff’

That’s because the current class of high school seniors is the last before a long decline begins in the number of 18-year-olds — the traditional age of students when they enter college.

This so-called demographic cliff has been predicted ever since Americans started having fewer babies at the advent of the Great Recession around the end of 2007 — a falling birth rate that has not recovered since, except for a slight blip after the Covid-19 pandemic, according to the Centers for Disease Control.

Demographers say it will finally arrive in the fall of this year. That’s when recruiting offices will begin to confront the long-anticipated drop-off in the number of applicants from among the next class of high school seniors.

“A few hundred thousand per year might not sound like a lot,” Strohl said. “But multiply that by a decade and it has a big impact.”

From DSC:
I remember seeing graphics about this demographic cliff over a decade ago…so institutions of traditional higher education have seen this coming for many years now (and the article references this as well). But it’s still important and the ramifications of this could be significant for many colleges and universities out there (for students, faculty, staff, and administrations).

  • Will there be new business models?
  • More lifelong learning models?
  • Additions to the curricula?

I sure hope so.


Higher Ed’s Governance Problem — from chronicle.com by Brian Rosenberg; via Ryan Craig
Boards are bloated and ineffectual.

According to the Association of Governing Boards of Universities and Colleges, the average size of a private nonprofit college or university board is 28 (larger than a major-league baseball roster), though boards of elite colleges tend to skew even larger: closer to 40, according to a study done by McKinsey.

By way of comparison, the average size of the board of directors of a publicly traded company in the United States is nine. If that seems too “corporate,” consider that the average size of the board of a nonprofit health-care institution is 13…

Still, anyone who studies organizational effectiveness would tell you that college and university boards are much too large, as would almost any college or university president when speaking off the record. Getting 12 people to spend significant time studying serious challenges and then reaching consensus about how to tackle those challenges is a heavy lift. Doing this with 25 or 35 or 45 people is close to impossible.


From Google ads to NFL sponsorships: Colleges throw billions at marketing themselves to attract students — from hechingerreport.org by Jon Marcus
Marketing and branding are getting big budgets and advertising is setting new records

In fact, the sum is small compared to what other colleges and universities are investing in advertising, marketing and promotion, which has been steadily rising and is on track this year to be nearly double what it was last year.

Among the reasons are a steep ongoing decline in enrollment, made worse by the pandemic, and increasing competition from online providers and others.

“Private schools in particular are acutely conscious of the demographics in this country. They’re competing for students, and marketing is how you have to do that.”

John Garvey, president, Catholic University


From DSC:
And for you students out there, check this sound advice out!

 

10 Higher Ed Trends to Watch In 2025 — from insidetrack.org

While “polarization” was Merriam-Webster’s word of the year for 2024, we have some early frontrunners for 2025 — especially when it comes to higher education. Change. Agility. Uncertainty. Flexibility. As we take a deep dive into the trends on tap for higher education in the coming year, it’s important to note that, with an incoming administration who has vowed to shake things up, the current postsecondary system could be turned on its head. With that in mind, we wade into our yearly look at the topics and trends that will be making headlines — and making waves — in the year ahead.

#Highereducation #learningecosystems #change #trends #businessmodels #trends #onlinelearning #AI #DEI #skillsbasedlearning #skills #alternatives #LearningandEmploymentRecords #LERs #valueofhighereducation #GenAI

 
 
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