“Using AI Right Now: A Quick Guide” [Molnick] + other items re: AI in our learning ecosystems

Thoughts on thinking — from dcurt.is by Dustin Curtis

Intellectual rigor comes from the journey: the dead ends, the uncertainty, and the internal debate. Skip that, and you might still get the insight–but you’ll have lost the infrastructure for meaningful understanding. Learning by reading LLM output is cheap. Real exercise for your mind comes from building the output yourself.

The irony is that I now know more than I ever would have before AI. But I feel slightly dumber. A bit more dull. LLMs give me finished thoughts, polished and convincing, but none of the intellectual growth that comes from developing them myself. 


Using AI Right Now: A Quick Guide — from oneusefulthing.org by Ethan Mollick
Which AIs to use, and how to use them

Every few months I put together a guide on which AI system to use. Since I last wrote my guide, however, there has been a subtle but important shift in how the major AI products work. Increasingly, it isn’t about the best model, it is about the best overall system for most people. The good news is that picking an AI is easier than ever and you have three excellent choices. The challenge is that these systems are getting really complex to understand. I am going to try and help a bit with both.

First, the easy stuff.

Which AI to Use
For most people who want to use AI seriously, you should pick one of three systems: Claude from Anthropic, Google’s Gemini, and OpenAI’s ChatGPT.


Student Voice, Socratic AI, and the Art of Weaving a Quote — from elmartinsen.substack.com by Eric Lars Martinsen
How a custom bot helps students turn source quotes into personal insight—and share it with others

This summer, I tried something new in my fully online, asynchronous college writing course. These classes have no Zoom sessions. No in-person check-ins. Just students, Canvas, and a lot of thoughtful design behind the scenes.

One activity I created was called QuoteWeaver—a PlayLab bot that helps students do more than just insert a quote into their writing.

Try it here

It’s a structured, reflective activity that mimics something closer to an in-person 1:1 conference or a small group quote workshop—but in an asynchronous format, available anytime. In other words, it’s using AI not to speed students up, but to slow them down.

The bot begins with a single quote that the student has found through their own research. From there, it acts like a patient writing coach, asking open-ended, Socratic questions such as:

What made this quote stand out to you?
How would you explain it in your own words?
What assumptions or values does the author seem to hold?
How does this quote deepen your understanding of your topic?
It doesn’t move on too quickly. In fact, it often rephrases and repeats, nudging the student to go a layer deeper.


The Disappearance of the Unclear Question — from jeppestricker.substack.com Jeppe Klitgaard Stricker
New Piece for UNESCO Education Futures

On [6/13/25], UNESCO published a piece I co-authored with Victoria Livingstone at Johns Hopkins University Press. It’s called The Disappearance of the Unclear Question, and it’s part of the ongoing UNESCO Education Futures series – an initiative I appreciate for its thoughtfulness and depth on questions of generative AI and the future of learning.

Our piece raises a small but important red flag. Generative AI is changing how students approach academic questions, and one unexpected side effect is that unclear questions – for centuries a trademark of deep thinking – may be beginning to disappear. Not because they lack value, but because they don’t always work well with generative AI. Quietly and unintentionally, students (and teachers) may find themselves gradually avoiding them altogether.

Of course, that would be a mistake.

We’re not arguing against using generative AI in education. Quite the opposite. But we do propose that higher education needs a two-phase mindset when working with this technology: one that recognizes what AI is good at, and one that insists on preserving the ambiguity and friction that learning actually requires to be successful.




Leveraging GenAI to Transform a Traditional Instructional Video into Engaging Short Video Lectures — from er.educause.edu by Hua Zheng

By leveraging generative artificial intelligence to convert lengthy instructional videos into micro-lectures, educators can enhance efficiency while delivering more engaging and personalized learning experiences.


This AI Model Never Stops Learning — from link.wired.com by Will Knight

Researchers at Massachusetts Institute of Technology (MIT) have now devised a way for LLMs to keep improving by tweaking their own parameters in response to useful new information.

The work is a step toward building artificial intelligence models that learn continually—a long-standing goal of the field and something that will be crucial if machines are to ever more faithfully mimic human intelligence. In the meantime, it could give us chatbots and other AI tools that are better able to incorporate new information including a user’s interests and preferences.

The MIT scheme, called Self Adapting Language Models (SEAL), involves having an LLM learn to generate its own synthetic training data and update procedure based on the input it receives.


Edu-Snippets — from scienceoflearning.substack.com by Nidhi Sachdeva and Jim Hewitt
Why knowledge matters in the age of AI; What happens to learners’ neural activity with prolonged use of LLMs for writing

Highlights:

  • Offloading knowledge to Artificial Intelligence (AI) weakens memory, disrupts memory formation, and erodes the deep thinking our brains need to learn.
  • Prolonged use of ChatGPT in writing lowers neural engagement, impairs memory recall, and accumulates cognitive debt that isn’t easily reversed.
 
 

The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI — from papers.ssrn.com by Barbara Oakley, Michael Johnston, Kenzen Chen, Eulho Jung, and Terrence Sejnowski; via George Siemens

Abstract
In an era of generative AI and ubiquitous digital tools, human memory faces a paradox: the more we offload knowledge to external aids, the less we exercise and develop our own cognitive capacities.
This chapter offers the first neuroscience-based explanation for the observed reversal of the Flynn Effect—the recent decline in IQ scores in developed countries—linking this downturn to shifts in educational practices and the rise of cognitive offloading via AI and digital tools. Drawing on insights from neuroscience, cognitive psychology, and learning theory, we explain how underuse of the brain’s declarative and procedural memory systems undermines reasoning, impedes learning, and diminishes productivity. We critique contemporary pedagogical models that downplay memorization and basic knowledge, showing how these trends erode long-term fluency and mental flexibility. Finally, we outline policy implications for education, workforce development, and the responsible integration of AI, advocating strategies that harness technology as a complement to – rather than a replacement for – robust human knowledge.

Keywords
cognitive offloading, memory, neuroscience of learning, declarative memory, procedural memory, generative AI, Flynn Effect, education reform, schemata, digital tools, cognitive load, cognitive architecture, reinforcement learning, basal ganglia, working memory, retrieval practice, schema theory, manifolds

 

Mary Meeker AI Trends Report: Mind-Boggling Numbers Paint AI’s Massive Growth Picture — from ndtvprofit.com
Numbers that prove AI as a tech is unlike any other the world has ever seen.

Here are some incredibly powerful numbers from Mary Meeker’s AI Trends report, which showcase how artificial intelligence as a tech is unlike any other the world has ever seen.

  • AI took only three years to reach 50% user adoption in the US; mobile internet took six years, desktop internet took 12 years, while PCs took 20 years.
  • ChatGPT reached 800 million users in 17 months and 100 million in only two months, vis-à-vis Netflix’s 100 million (10 years), Instagram (2.5 years) and TikTok (nine months).
  • ChatGPT hit 365 billion annual searches in two years (2024) vs. Google’s 11 years (2009)—ChatGPT 5.5x faster than Google.

Above via Mary Meeker’s AI Trend-Analysis — from getsuperintel.com by Kim “Chubby” Isenberg
How AI’s rapid rise, efficiency race, and talent shifts are reshaping the future.

The TLDR
Mary Meeker’s new AI trends report highlights an explosive rise in global AI usage, surging model efficiency, and mounting pressure on infrastructure and talent. The shift is clear: AI is no longer experimental—it’s becoming foundational, and those who optimize for speed, scale, and specialization will lead the next wave of innovation.

 

Also see Meeker’s actual report at:

Trends – Artificial Intelligence — from bondcap.com by Mary Meeker / Jay Simons / Daegwon Chae / Alexander Krey



The Rundown: Meta aims to release tools that eliminate humans from the advertising process by 2026, according to a report from the WSJ — developing an AI that can create ads for Facebook and Instagram using just a product image and budget.

The details:

  • Companies would submit product images and budgets, letting AI craft the text and visuals, select target audiences, and manage campaign placement.
  • The system will be able to create personalized ads that can adapt in real-time, like a car spot featuring mountains vs. an urban street based on user location.
  • The push would target smaller companies lacking dedicated marketing staff, promising professional-grade advertising without agency fees or skillset.
  • Advertising is a core part of Mark Zuckerberg’s AI strategy and already accounts for 97% of Meta’s annual revenue.

Why it matters: We’re already seeing AI transform advertising through image, video, and text, but Zuck’s vision takes the process entirely out of human hands. With so much marketing flowing through FB and IG, a successful system would be a major disruptor — particularly for small brands that just want results without the hassle.

 

“The AI-enhanced learning ecosystem” [Jennings] + other items re: AI in our learning ecosystems

The AI-enhanced learning ecosystem: A case study in collaborative innovation — from chieflearningofficer.com by Kevin Jennings
How artificial intelligence can serve as a tool and collaborative partner in reimagining content development and management.

Learning and development professionals face unprecedented challenges in today’s rapidly evolving business landscape. According to LinkedIn’s 2025 Workplace Learning Report, 67 percent of L&D professionals report being “maxed out” on capacity, while 66 percent have experienced budget reductions in the past year.

Despite these constraints, 87 percent agree their organizations need to develop employees faster to keep pace with business demands. These statistics paint a clear picture of the pressure L&D teams face: do more, with less, faster.

This article explores how one L&D leader’s strategic partnership with artificial intelligence transformed these persistent challenges into opportunities, creating a responsive learning ecosystem that addresses the modern demands of rapid product evolution and diverse audience needs. With 71 percent of L&D professionals now identifying AI as a high or very high priority for their learning strategy, this case study demonstrates how AI can serve not merely as a tool but as a collaborative partner in reimagining content development and management.
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How we use GenAI and AR to improve students’ design skills — from timeshighereducation.com by Antonio Juarez, Lesly Pliego and Jordi Rábago who are professors of architecture at Monterrey Institute of Technology in Mexico; Tomas Pachajoa is a professor of architecture at the El Bosque University in Colombia; & Carlos Hinrichsen and Marietta Castro are educators at San Sebastián University in Chile.
Guidance on using generative AI and augmented reality to enhance student creativity, spatial awareness and interdisciplinary collaboration

Blend traditional skills development with AI use
For subjects that require students to develop drawing and modelling skills, have students create initial design sketches or models manually to ensure they practise these skills. Then, introduce GenAI tools such as Midjourney, Leonardo AI and ChatGPT to help students explore new ideas based on their original concepts. Using AI at this stage broadens their creative horizons and introduces innovative perspectives, which are crucial in a rapidly evolving creative industry.

Provide step-by-step tutorials, including both written guides and video demonstrations, to illustrate how initial sketches can be effectively translated into AI-generated concepts. Offer example prompts to demonstrate diverse design possibilities and help students build confidence using GenAI.

Integrating generative AI and AR consistently enhanced student engagement, creativity and spatial understanding on our course. 


How Texas is Preparing Higher Education for AI — from the74million.org by Kate McGee
TX colleges are thinking about how to prepare students for a changing workforce and an already overburdened faculty for new challenges in classrooms.

“It doesn’t matter if you enter the health industry, banking, oil and gas, or national security enterprises like we have here in San Antonio,” Eighmy told The Texas Tribune. “Everybody’s asking for competency around AI.”

It’s one of the reasons the public university, which serves 34,000 students, announced earlier this year that it is creating a new college dedicated to AI, cyber security, computing and data science. The new college, which is still in the planning phase, would be one of the first of its kind in the country. UTSA wants to launch the new college by fall 2025.

But many state higher education leaders are thinking beyond that. As AI becomes a part of everyday life in new, unpredictable ways, universities across Texas and the country are also starting to consider how to ensure faculty are keeping up with the new technology and students are ready to use it when they enter the workforce.


In the Room Where It Happens: Generative AI Policy Creation in Higher Education — from er.educause.edu by Esther Brandon, Lance Eaton, Dana Gavin, and Allison Papini

To develop a robust policy for generative artificial intelligence use in higher education, institutional leaders must first create “a room” where diverse perspectives are welcome and included in the process.


Q&A: Artificial Intelligence in Education and What Lies Ahead — from usnews.com by Sarah Wood
Research indicates that AI is becoming an essential skill to learn for students to succeed in the workplace.

Q: How do you expect to see AI embraced more in the future in college and the workplace?
I do believe it’s going to become a permanent fixture for multiple reasons. I think the national security imperative associated with AI as a result of competing against other nations is going to drive a lot of energy and support for AI education. We also see shifts across every field and discipline regarding the usage of AI beyond college. We see this in a broad array of fields, including health care and the field of law. I think it’s here to stay and I think that means we’re going to see AI literacy being taught at most colleges and universities, and more faculty leveraging AI to help improve the quality of their instruction. I feel like we’re just at the beginning of a transition. In fact, I often describe our current moment as the ‘Ask Jeeves’ phase of the growth of AI. There’s a lot of change still ahead of us. AI, for better or worse, it’s here to stay.




AI-Generated Podcasts Outperform Textbooks in Landmark Education Study — form linkedin.com by David Borish

A new study from Drexel University and Google has demonstrated that AI-generated educational podcasts can significantly enhance both student engagement and learning outcomes compared to traditional textbooks. The research, involving 180 college students across the United States, represents one of the first systematic investigations into how artificial intelligence can transform educational content delivery in real-time.


What can we do about generative AI in our teaching?  — from linkedin.com by Kristina Peterson

So what can we do?

  • Interrogate the Process: We can ask ourselves if we I built in enough checkpoints. Steps that can’t be faked. Things like quick writes, question floods, in-person feedback, revision logs.
  • Reframe AI: We can let students use AI as a partner. We can show them how to prompt better, revise harder, and build from it rather than submit it. Show them the difference between using a tool and being used by one.
  • Design Assignments for Curiosity, Not Compliance: Even the best of our assignments need to adapt. Mine needs more checkpoints, more reflective questions along the way, more explanation of why my students made the choices they did.

Teachers Are Not OK — from 404media.co by Jason Koebler

The response from teachers and university professors was overwhelming. In my entire career, I’ve rarely gotten so many email responses to a single article, and I have never gotten so many thoughtful and comprehensive responses.

One thing is clear: teachers are not OK.

In addition, universities are contracting with companies like Microsoft, Adobe, and Google for digital services, and those companies are constantly pushing their AI tools. So a student might hear “don’t use generative AI” from a prof but then log on to the university’s Microsoft suite, which then suggests using Copilot to sum up readings or help draft writing. It’s inconsistent and confusing.

I am sick to my stomach as I write this because I’ve spent 20 years developing a pedagogy that’s about wrestling with big ideas through writing and discussion, and that whole project has been evaporated by for-profit corporations who built their systems on stolen work. It’s demoralizing.

 

May Brought Deep Cuts at Multiple Colleges — from insidehighered.com by  Josh Moody
Colleges laid off well over 800 employees last month due to a mix of enrollment challenges and state funding issues. Ivy Tech saw the deepest cuts with more than 200 jobs axed.

With the academic year coming to an end, multiple universities announced deep cuts in May, shedding dozens of jobs amid financial pressures often linked to enrollment shortfalls.

But the cuts below, for the most part, are not directly tied to the rapid-fire actions of the Trump administration but rather stem from other financial pressures weighing on the sector. Many of the institutions listed are contending with declining enrollment and, for public universities, shrinking state support, which has necessitated fiscal changes.

From DSC:
I survived several job reductions at one of my former workplaces. But I didn’t survive the one that laid off 12 staff members after the Spring 2017 Semester. So, more and more, faculty and staff have been starting to dread the end of the academic year — as they may not survive another round of cuts. 

 

In ‘The Quilters,’ Men in a Missouri Prison Sew Gifts for Children — from thisiscolossal.com by Grace Ebert

In a room bigger than most at South Central Correctional Center in Licking, Missouri, a group of men has volunteered for a creative project that stretches beyond prison walls. For about 40 hours each week, they cut and stitch quilts for children in foster care or with disabilities, sewing vibrant, patterned patchworks and finding joy and camaraderie while doing so.

 

Opinions | This Baltimore program shows how to fight generational poverty – from washingtonpost.com by Leana S. Wen; this is a gifted article
How one grassroots organization is teaching young people leadership skills and giving them hope.

She recognized their desperation and felt called to return and use what she had learned to help them realize a different future. So she set up an organization, HeartSmiles, to do just that — one young person at a time.

Holifield’s experience is one that city officials and public health workers can learn from. If they want to disrupt the generational cycle of poverty, trauma and hopelessness that afflicts so many communities, a good place to focus their efforts is children.

How can communities overcome inertia and resignation? Holifield’s organization starts with two core interventions. The first is career and leadership development. Children as young as 8 go to the HeartSmiles center to participate in facilitated sessions on youth entrepreneurship, budgeting and conflict resolution. Those who want to explore certain career paths are matched with professionals in these fields.

The second part of her vision is youth-led mentorship, which involves pairing young people with those not much older than they are. 


Also relevant/see:

Lost boys, trapped men, and the role of lifers in prison education — from college-inside.beehiiv.com by Charlotte West

This week, we’re publishing Part 2 of a Q&A with Erik Maloney, a lifer in Arizona, and Kevin Wright, a criminal justice professor at Arizona State University. They co-authored Imprisoned Minds, a book about trauma and healing published in December 2024, over the course of seven years. Check out Part 1 of the Q&A.

West: The fact that you created your own curriculum to accompany the book makes me think about the role of lifers in creating educational opportunities in prisons. What do you see as the role of lifers in filling some of these gaps?

Maloney
: I’ve said for years that lifers are so underutilized in prison. It’s all about punishment for what you’re in for, and [the prison system] overlooks us as a resource. We are people who, if allowed to be educated properly, can teach courses indefinitely while also being a role model for those with shorter sentences. This gives the lifer meaning and purpose to do good again. He serves as a mentor, whether he likes it or not, to [those] people coming into the prisons. When they see him doing well, it inspires others to want to do well.

But if it’s all about punishment, and a person has no meaning and no purpose in life, then all they have is hopelessness. With hopelessness comes despair, and with despair, you have rampant drug and alcohol abuse in prison, and violence stems from that.

 

How to Use AI and Universal Design to Empower Diverse Thinkers with Susan Tanner — from legaltalknetwork.com by Zack Glaser, Stephanie Everett, and Susan Tanner

What if the key to better legal work isn’t just smarter tools but more inclusive ones? Susan Tanner, Associate Professor at the University of Louisville Brandeis School of Law, joins Zack Glaser to explore how AI and universal design can improve legal education and law firm operations. Susan shares how tools like generative AI can support neurodiverse thinkers, enhance client communication, and reduce anxiety for students and professionals alike. They also discuss the importance of inclusive design in legal tech and how law firms can better support their teams by embracing different ways of thinking to build a more accessible, future-ready practice. The conversation emphasizes the need for educators and legal professionals to adapt to the evolving landscape of AI, ensuring that they leverage its capabilities to better serve their clients and students.


Maximizing Microsoft Copilot in Your Legal Practice — from legaltalknetwork.com by Tom Mighell, Dennis Kennedy, and Ben Schorr

Copilot is a powerful tool for lawyers, but are you making the most of it within your Microsoft apps? Tom Mighell is flying solo at ABA TECHSHOW 2025 and welcomes Microsoft’s own Ben Schorr to the podcast. Ben shares expert insights into how lawyers can implement Copilot’s AI-assistance to work smarter, not harder. From drafting documents to analyzing spreadsheets to streamlining communication, Copilot can handle the tedious tasks so you can focus on what really matters. Ben shares numerous use-cases and capabilities for attorneys and later gives a sneak peek at Copilot’s coming enhancements.


 

 

What does ‘age appropriate’ AI literacy look like in higher education? — from timeshighereducation.com by Fun Siong Lim
As AI literacy becomes an essential work skill, universities need to move beyond developing these competencies at ‘primary school’ level in their students. Here, Fun Siong Lim reflects on frameworks to support higher-order AI literacies

Like platforms developed at other universities, Project NALA offers a front-end interface (known as the builder) for faculty to create their own learning assistant. An idea we have is to open the builder up to students to allow them to create their own GenAI assistant as part of our AI literacy curriculum. As they design, configure and test their own assistant, they will learn firsthand how generative AI works. They get to test performance-enhancement approaches beyond prompt engineering, such as grounding the learning assistant with curated materials (retrieval-augmented generation) and advanced ideas such as incorporating knowledge graphs.

They should have the opportunity to analyse, evaluate and create responsible AI solutions. Offering students the opportunity to build their own AI assistants could be a way forward to develop these much-needed skills.


How to Use ChatGPT 4o’s Update to Turn Key Insights Into Clear Infographics (Prompts Included) — from evakeiffenheim.substack.com by Eva Keiffenheim
This 3-step workflow helps you break down books, reports, or slide-decks into professional visuals that accelerate understanding.

This article shows you how to find core ideas, prompt GPT-4o3 for a design brief, and generate clean, professional images that stick. These aren’t vague “creative visuals”—they’re structured for learning, memory, and action.

If you’re a lifelong learner, educator, creator, or just someone who wants to work smarter, this process is for you.

You’ll spend less time re-reading and more time understanding. And maybe—just maybe—you’ll build ideas that not only click in your brain, but also stick in someone else’s.


SchoolAI Secures $25 Million to Help Teachers and Schools Reach Every Student — from globenewswire.com
 The Classroom Experience platform gives every teacher and student their own AI tools for personalized learning

SchoolAI’s Classroom Experience platform combines AI assistants for teachers that help with classroom preparation and other administrative work, and Spaces–personalized AI tutors, games, and lessons that can adapt to each student’s unique learning style and interests. Together, these tools give teachers actionable insights into how students are doing, and how the teacher can deliver targeted support when it matters most.

“Teachers and schools are navigating hard challenges with shrinking budgets, teacher shortages, growing class sizes, and ongoing recovery from pandemic-related learning gaps,” said Caleb Hicks, founder and CEO of SchoolAI. “It’s harder than ever to understand how every student is really doing. Teachers deserve powerful tools to help extend their impact, not add to their workload. This funding helps us double down on connecting the dots for teachers and students, and later this year, bringing school administrators and parents at home onto the platform as well.”


AI in Education, Part 3: Looking Ahead – The Future of AI in Learning — from rdene915.com by Dr. Rachelle Dené Poth

In the first and second parts of my AI series, I focused on where we see AI in classrooms. Benefits range from personalized learning and accessibility tools to AI-driven grading and support of a teaching assistant. In Part 2, I chose to focus on some of the important considerations related to ethics that must be part of the conversation. Schools need to focus on data privacy, bias, overreliance, and the equity divide. I wanted to focus on the future for this last part in the current AI series. Where do we go from here?


Anthropic Education Report: How University Students Use Claude — from anthropic.com

The key findings from our Education Report are:

  • STEM students are early adopters of AI tools like Claude, with Computer Science students particularly overrepresented (accounting for 36.8% of students’ conversations while comprising only 5.4% of U.S. degrees). In contrast, Business, Health, and Humanities students show lower adoption rates relative to their enrollment numbers.
  • We identified four patterns by which students interact with AI, each of which were present in our data at approximately equal rates (each 23-29% of conversations): Direct Problem Solving, Direct Output Creation, Collaborative Problem Solving, and Collaborative Output Creation.
  • Students primarily use AI systems for creating (using information to learn something new) and analyzing (taking apart the known and identifying relationships), such as creating coding projects or analyzing law concepts. This aligns with higher-order cognitive functions on Bloom’s Taxonomy. This raises questions about ensuring students don’t offload critical cognitive tasks to AI systems.

From the Kuali Days 2025 Conference: A CEO’s View of Planning for AI — from campustechnology.com by Mary Grush
A Conversation with Joel Dehlin

How can a company serving higher education navigate the changes AI brings to the ed tech marketplace? What will customers expect in this dynamic? Here, CT talks with Kuali CEO Joel Dehlin, who shared his company’s AI strategies in a featured plenary session, “Sneak Peek of AI in Kuali Build,” at Kuali Days 2025 in Anaheim.


How students can use generative AI — from aliciabankhofer.substack.com by Alicia Bankhofer
Part 4 of 4 in my series on Teaching and Learning in the AI Age

This article is the culmination of a series exploring AI’s impact on education.

Part 1: What Educators Need outlined essential AI literacy skills for teachers, emphasizing the need to move beyond basic ChatGPT exploration to understand the full spectrum of AI tools available in education.

Part 2: What Students Need addressed how students require clear guidance to use AI safely, ethically, and responsibly, with emphasis on developing critical thinking skills alongside AI literacy.

Part 3: How Educators Can Use GenAI presented ten practical use cases for teachers, from creating differentiated resources to designing assessments, demonstrating how AI can reclaim 5-7 hours weekly for meaningful student interactions.

Part 4: How Students Can Use GenAI (this article) provides frameworks for guiding student AI use based on Joscha Falck’s dimensions: learning about, with, through, despite, and without AI.


Mapping a Multidimensional Framework for GenAI in Education — from er.educause.edu by Patricia Turner
Prompting careful dialogue through incisive questions can help chart a course through the ongoing storm of artificial intelligence.

The goal of this framework is to help faculty, educational developers, instructional designers, administrators, and others in higher education engage in productive discussions about the use of GenAI in teaching and learning. As others have noted, theoretical frameworks will need to be accompanied by research and teaching practice, each reinforcing and reshaping the others to create understandings that will inform the development of approaches to GenAI that are both ethical and maximally beneficial, while mitigating potential harms to those who engage with it.


Instructional Design Isn’t Dying — It’s Specialising — from drphilippahardman.substack.com by Dr. Philippa Hardman
Aka, how AI is impacting role & purpose of Instructional Design

Together, these developments have revealed something important: despite widespread anxiety, the instructional design role isn’t dying—it’s specialising.

What we’re witnessing isn’t the automation of instructional design and the death of the instructional designer, but rather the evolution of the ID role into multiple distinct professional pathways.

The generalist “full stack” instructional designer is slowly but decisively fracturing into specialised roles that reflect both the capabilities of generative AI and the strategic imperatives facing modern organisations.

In this week’s blog post, I’ll share what I’ve learned about how our field is transforming, and what it likely means for you and your career path.

Those instructional designers who cling to traditional generalist models risk being replaced, but those who embrace specialisation, data fluency, and AI collaboration will excel and lead the next evolution of the field. Similarly, those businesses that continue to view L&D as a cost centre and focus on automating content delivery will be outperformed, while those that invest in building agile, AI-enabled learning ecosystems will drive measurable performance gains and secure their competitive advantage.


Adding AI to Every Step in Your eLearning Design Workflow — from learningguild.com by George Hanshaw

We know that eLearning is a staple of training and development. The expectations of the learners are higher than ever: They expect a dynamic, interactive, and personalized learning experience. As instructional designers, we are tasked with meeting these expectations by creating engaging and effective learning solutions.

The integration of Artificial Intelligence (AI) into our eLearning design process is a game-changer that can significantly enhance the quality and efficiency of our work.

No matter if you use ADDIE or rapid prototyping, AI has a fit in every aspect of your workflow. By integrating AI, you can ensure a more efficient and effective design process that adapts to the unique needs of your learners. This not only saves time and resources but also significantly enhances the overall learning experience. We will explore the needs analysis and the general design process.

 

Reflections on “Are You Ready for the AI University? Everything is about to change.” [Latham]

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Are You Ready for the AI University? Everything is about to change. — from chronicle.com by Scott Latham

Over the course of the next 10 years, AI-powered institutions will rise in the rankings. US News & World Report will factor a college’s AI capabilities into its calculations. Accrediting agencies will assess the degree of AI integration into pedagogy, research, and student life. Corporations will want to partner with universities that have demonstrated AI prowess. In short, we will see the emergence of the AI haves and have-nots.

What’s happening in higher education today has a name: creative destruction. The economist Joseph Schumpeter coined the term in 1942 to describe how innovation can transform industries. That typically happens when an industry has both a dysfunctional cost structure and a declining value proposition. Both are true of higher education.

Out of the gate, professors will work with technologists to get AI up to speed on specific disciplines and pedagogy. For example, AI could be “fed” course material on Greek history or finance and then, guided by human professors as they sort through the material, help AI understand the structure of the discipline, and then develop lectures, videos, supporting documentation, and assessments.

In the near future, if a student misses class, they will be able watch a recording that an AI bot captured. Or the AI bot will find a similar lecture from another professor at another accredited university. If you need tutoring, an AI bot will be ready to help any time, day or night. Similarly, if you are going on a trip and wish to take an exam on the plane, a student will be able to log on and complete the AI-designed and administered exam. Students will no longer be bound by a rigid class schedule. Instead, they will set the schedule that works for them.

Early and mid-career professors who hope to survive will need to adapt and learn how to work with AI. They will need to immerse themselves in research on AI and pedagogy and understand its effect on the classroom. 

From DSC:
I had a very difficult time deciding which excerpts to include. There were so many more excerpts for us to think about with this solid article. While I don’t agree with several things in it, EVERY professor, president, dean, and administrator working within higher education today needs to read this article and seriously consider what Scott Latham is saying.

Change is already here, but according to Scott, we haven’t seen anything yet. I agree with him and, as a futurist, one has to consider the potential scenarios that Scott lays out for AI’s creative destruction of what higher education may look like. Scott asserts that some significant and upcoming impacts will be experienced by faculty members, doctoral students, and graduate/teaching assistants (and Teaching & Learning Centers and IT Departments, I would add). But he doesn’t stop there. He brings in presidents, deans, and other members of the leadership teams out there.

There are a few places where Scott and I differ.

  • The foremost one is the importance of the human element — i.e., the human faculty member and students’ learning preferences. I think many (most?) students and lifelong learners will want to learn from a human being. IBM abandoned their 5-year, $100M ed push last year and one of the key conclusions was that people want to learn from — and with — other people:

To be sure, AI can do sophisticated things such as generating quizzes from a class reading and editing student writing. But the idea that a machine or a chatbot can actually teach as a human can, he said, represents “a profound misunderstanding of what AI is actually capable of.” 

Nitta, who still holds deep respect for the Watson lab, admits, “We missed something important. At the heart of education, at the heart of any learning, is engagement. And that’s kind of the Holy Grail.”

— Satya Nitta, a longtime computer researcher at
IBM’s Watson
Research Center in Yorktown Heights, NY
.

By the way, it isn’t easy for me to write this. As I wanted AI and other related technologies to be able to do just what IBM was hoping that it would be able to do.

  • Also, I would use the term learning preferences where Scott uses the term learning styles.

Scott also mentions:

“In addition, faculty members will need to become technologists as much as scholars. They will need to train AI in how to help them build lectures, assessments, and fine-tune their classroom materials. Further training will be needed when AI first delivers a course.”

It has been my experience from working with faculty members for over 20 years that not all faculty members want to become technologists. They may not have the time, interest, and/or aptitude to become one (and vice versa for technologists who likely won’t become faculty members).

That all said, Scott relays many things that I have reflected upon and relayed for years now via this Learning Ecosystems blog and also via The Learning from the Living [AI-Based Class] Room vision — the use of AI to offer personalized and job-relevant learning, the rising costs of higher education, the development of new learning-related offerings and credentials at far less expensive prices, the need to provide new business models and emerging technologies that are devoted more to lifelong learning, plus several other things.

So this article is definitely worth your time to read, especially if you are working in higher education or are considering a career therein!


Addendum later on 4/10/25:

U-M’s Ross School of Business, Google Public Sector launch virtual teaching assistant pilot program — from news.umich.edu by Jeff Karoub; via Paul Fain

Google Public Sector and the University of Michigan’s Ross School of Business have launched an advanced Virtual Teaching Assistant pilot program aimed at improving personalized learning and enlightening educators on artificial intelligence in the classroom.

The AI technology, aided by Google’s Gemini chatbot, provides students with all-hours access to support and self-directed learning. The Virtual TA represents the next generation of educational chatbots, serving as a sophisticated AI learning assistant that instructors can use to modify their specific lessons and teaching styles.

The Virtual TA facilitates self-paced learning for students, provides on-demand explanations of complex course concepts, guides them through problem-solving, and acts as a practice partner. It’s designed to foster critical thinking by never giving away answers, ensuring students actively work toward solutions.

 

The 2025 AI Index Report — from Stanford University’s Human-Centered Artificial Intelligence Lab (hai.stanford.edu); item via The Neuron

Top Takeaways

  1. AI performance on demanding benchmarks continues to improve.
  2. AI is increasingly embedded in everyday life.
  3. Business is all in on AI, fueling record investment and usage, as research continues to show strong productivity impacts.
  4. The U.S. still leads in producing top AI models—but China is closing the performance gap.
  5. The responsible AI ecosystem evolves—unevenly.
  6. Global AI optimism is rising—but deep regional divides remain.
  7. …and several more

Also see:

The Neuron’s take on this:

So, what should you do? You really need to start trying out these AI tools. They’re getting cheaper and better, and they can genuinely help save time or make work easier—ignoring them is like ignoring smartphones ten years ago.

Just keep two big things in mind:

  1. Making the next super-smart AI costs a crazy amount of money and uses tons of power (seriously, they’re buying nuclear plants and pushing coal again!).
  2. Companies are still figuring out how to make AI perfectly safe and fair—cause it still makes mistakes.

So, use the tools, find what helps you, but don’t trust them completely.

We’re building this plane mid-flight, and Stanford’s report card is just another confirmation that we desperately need better safety checks before we hit major turbulence.


Addendum on 4/16:

 

It’s the end of work as we knew it
and I feel…

powerless to fight the technology that we pioneered
nostalgic for a world that moved on without us
after decades of paying our dues
for a payday that never came
…so yeah
not exactly fine.


The Gen X Career Meltdown — from nytimes.com by Steeven Kurutz (DSC: This is a gifted article for you)
Just when they should be at their peak, experienced workers in creative fields find that their skills are all but obsolete.

If you entered media or image-making in the ’90s — magazine publishing, newspaper journalism, photography, graphic design, advertising, music, film, TV — there’s a good chance that you are now doing something else for work. That’s because those industries have shrunk or transformed themselves radically, shutting out those whose skills were once in high demand.

“I am having conversations every day with people whose careers are sort of over,” said Chris Wilcha, a 53-year-old film and TV director in Los Angeles.

Talk with people in their late 40s and 50s who once imagined they would be able to achieve great heights — or at least a solid career while flexing their creative muscles — and you are likely to hear about the photographer whose work dried up, the designer who can’t get hired or the magazine journalist who isn’t doing much of anything.

In the wake of the influencers comes another threat, artificial intelligence, which seems likely to replace many of the remaining Gen X copywriters, photographers and designers. By 2030, ad agencies in the United States will lose 32,000 jobs, or 7.5 percent of the industry’s work force, to the technology, according to the research firm Forrester.


From DSC:
This article reminds me of how tough it is to navigate change in our lives. For me, it was often due to the fact that I was working with technologies. Being a technologist can be difficult, especially as one gets older and faces age discrimination in a variety of industries. You need to pick the right technologies and the directions that will last (for me it was email, videoconferencing, the Internet, online-based education/training, discovering/implementing instructional technologies, and becoming a futurist).

For you younger folks out there — especially students within K-16 — aim to develop a perspective and a skillset that is all about adapting to change. You will likely need to reinvent yourself and/or pick up new skills over your working years. You are most assuredly required to be a lifelong learner now. That’s why I have been pushing for school systems to be more concerned with providing more choice and control to students — so that students actually like school and enjoy learning about new things.


 

 

What trauma-informed practice is not — from timeshighereducation.com by Kate Cantrell, India Bryce, and Jessica Gildersleeve from The University of Southern Queensland
Before trauma-informed care can be the norm across all areas of the university, academic and professional staff need to understand what it is. Here, three academics debunk myths and demystify best practice

Recently, we conducted focus groups at our university to better ascertain how academics, administrators and student support staff perceive the purpose and value of trauma-informed practice, and how they perceive their capacity to contribute to organisational change.

We discovered that while most staff were united on the importance of trauma-informed care, several myths persist about what trauma-informed practice is (and is not). Some academic staff, for example, conflated teaching about trauma with trauma-informed teaching, confused trigger warnings with trigger points and, perhaps most alarmingly – given the prevalence of trauma exposure and risk among university students – misjudged trauma-informed practice as “the business of psychologists” rather than educators.

 




Students and folks looking for work may want to check out:

Also relevant/see:


 
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