faculty/staff

How to Design Around Cognitive Offloading [Hardman]

On 08/10/2026, in corporate universities / corporate training, emerging technologies, faculty/staff, instructional design, learning, Learning Experience Design, learning theories, productivity / tips and tricks, professional development, research, teachers, teaching & learning, training / L&D, by Daniel Christian

How to Design Around Cognitive Offloading — from drphilippahardman.substack.com by Dr Philippa Hardman
Three new studies shed light on when offloading to AI harms learning, when it helps — and how to design for the difference

A cluster of new studies — most published in the last few weeks in the International Journal of Educational Technology in Higher Education — lets us answer two questions with more precision than ever before:

When does cognitive offloading actually happen?
When does using AI improve cognition?

The answers in turn help us to start to design learning which intentionally uses AI to drive – rather than diminish – learning.
.

The 5 college majors students are betting on right now [Borden]

On 08/10/2026, in adjunct faculty, business, colleges, community colleges, computer science, drama/theater, faculty/staff, higher education, learning ecosystem, liberal arts, music, psychology, student-related, teaching & learning, United States, universities, by Daniel Christian

The 5 college majors students are betting on right now — from linkedin.com by Taylor Border

This quote/insight is also for the Instructional Technologists out there

On 08/10/2026, in change, colleges, community colleges, education technology, faculty/staff, higher education, innovation, journalism, United States, universities, vendors, by Daniel Christian

From MLive.com’s John Hiner — in his Letter from the Editor dated 8/6/26:
40 years of change taught me this: It’s never really been about the technology

What I’ve learned over 40 years is that people rarely resist change because they dislike innovation. More often, it’s because they’re worried they’ll lose something they value.

What Nobody Warns You About Teaching College for the First Time [Morales]

On 08/10/2026, in adjunct faculty, colleges, community colleges, education, faculty/staff, higher education, ideas, instructional design, K-12 related, learning ecosystem, Learning Experience Design, teachers, teaching & learning, United States, universities, vendors, by Daniel Christian

What Nobody Warns You About Teaching College for the First Time — from facultyfocus.com by Christopher Morales; this article is behind a firewall

What became clear throughout these conversations was that secondary and postsecondary education often prioritize very different instructional skill sets.

In K–12 education, teachers frequently receive formal preparation in:

classroom management,
scaffolding,
differentiation,
assessment design,
engagement strategies,
and developmental learning theory.

In higher education, disciplinary expertise is often treated as the primary qualification for teaching.

Yet many instructors pointed out that expertise alone does not automatically translate into effective instruction.

Also from Faculty Focus, see:

The Syllabus and the Scar Tissue: What Leadership Preparation Programs Leave Out and Why It Matters — from facultyfocus.com Andy Szeto, EdD; this article is also behind a firewall

A Writer’s Toolkit [Tenore Tarpley & Caplan]

On 08/10/2026, in Artificial Intelligence/Agents/LLMs/and Related, faculty/staff, Google, human-computer interaction (HCI), journalism, Open AI, research, tools, United States, vendors, writing, by Daniel Christian

A Writer’s Toolkit — from wondertools.substack.com by Mallary Tenore Tarpley and Jeremy Caplan
Useful tools for every stage

Per Jeremy Caplan: My friend and colleague Mallary Tenore Tarpley writes an excellent newsletter, Write at the Edge, with helpful writing tips and best practices. She’s a professor, journalist, and author who has written for The New York Times, The Washington Post, and other outlets. She recently published an important debut book, Slip: Life in the Middle of Eating Disorder Recovery.

Mallary recently interviewed me about writing tools. She wrote up a summary of our conversation, highlighting the tools I talked about. I’m turning the rest of this post over to her summary.

The 2031 Crisis in Higher Education: a stark scenario — from bryanalexander.org by Bryan Alexander — with commentary on a posting by Matthew F. Wilson, Ph.D.

Today’s post is about a scenario for higher ed’s future. It’s not from me, but created by one Matthew F. Wilson, director of Research Translation and AI Strategy at Baylor’s Institute for Global Human Flourishing. “The 2031 Crisis in Higher Education” is a dark one, imagining an accelerating decline for American colleges and universities.
…
One key assumption of this scenario is that most colleges and universities will be unable to redesign themselves for the changing time.
…
All of that said, it’s a fascinating and daunting scenario. It’s an interesting vision of my post-peak higher ed world.

A graphic from Matthew Wilson:

From DSC:
From what I can tell and have read, the things in the above graphic are already happening — and have been happening for some time now.

Also, I don’t think traditional institutions of higher education have the culture(s) it takes to change. So that part about colleges and U’s not being able to redesign themselves for the changing times could easily turn out to be the case.

Why AI Is Changing What It Means to Be Intelligent [Elliott]

On 07/31/2026, in adjunct faculty, Artificial Intelligence/Agents/LLMs/and Related, colleges, community colleges, education technology, experimentation, faculty/staff, higher education, human-computer interaction (HCI), instructional design, intelligent systems, learning ecosystem, Learning Experience Design, teaching & learning, United States, universities, by Daniel Christian

Why AI Is Changing What It Means to Be Intelligent — from facultyfocus.com by Lydia Elliott, EdD; article may be behind a paywall

Practical adjustments may include:

oral explanations of written work
case-based or scenario-based exercises
reflective reasoning assignments
feedback conversations
requiring students to justify decisions

These approaches do not eliminate AI. They place learning where AI cannot substitute: interpretation, reasoning, and responsibility.

Why Engineering Is No Longer A Sure Thing College Major When It Comes To Jobs [Connley-Hampton]

On 07/29/2026, in career development, change, colleges, community colleges, computer science, corporate / business world, engineering, faculty/staff, higher education, programming, student-related, United States, universities, workplace, by Daniel Christian

Why Engineering Is No Longer A Sure Thing College Major When It Comes To Jobs — from forbes.com by Courtney Connley-Hampton; this article is behind a paywall
Exclusive LinkedIn data shows engineering majors have been hit hard in today’s weak, AI-affected job market. Here’s what specialties are hurting and what top schools are doing to respond.

While lots of recent college graduates are being battered by a weak hiring market, perhaps no group is more shocked by their current plight than those who majored in engineering, long considered among the safest, most secure, and highest-paying majors. Yes, engineering grads still make more than education and English majors.

Also relevant/see:

Chris Mayer’s posting on LinkedIn.com

What’s the Story Your Syllabus Tells?: Setting the Stage for Learning — from facultyfocus.com by Dr. Daniel Andrés Rivera Rosado

But as I was reading Everyday Christian Teaching by Dr. David Smith, in the chapter titled Speaking, Hearing, Hospitality, in just the first paragraph he writes: “Teachers do not simply list course content for learners. They sequence it in ways that imply a story about what fits together and where it is headed.” (2025, p.73). Automatically I asked myself, what is the story my syllabus is telling students? To be completely honest, is it even telling a story at all?

“Is your syllabus helping your students at all?”

From DSC:
To that last sentence, I might add the words engaging, raising curiosity or wonder, or is it interesting to your students at all? Is there a “hook” that gets them interested? It might be a central question or two — and/or use some beneficial and relevant graphics. I’ve heard faculty use digital/interactive syllabi as well to engage their students.

What China’s Calligraphy Lessons Can Teach Us About AI — from linkedin.com by Rebecca Winthrop

He recalled visiting a primary school classroom in China several years ago where students were practicing calligraphy—the centuries-old art of writing Chinese characters with brush and ink. While the scene appeared entirely traditional, there was something innovative happening beneath the surface. Embedded in students’ desks was an AI system capable of providing immediate feedback on their brushwork, noting misplaced strokes and suggesting corrections.

The image is striking. Students were learning in much the same way generations before them had learned—brush in hand, immersed in a deeply human and cultural practice. Yet AI was quietly enhancing the experience by providing immediate, personalized feedback that would have been difficult for any teacher to provide to her whole class.

That example captures something important for me. The most promising uses of AI may not be the applications that place technology front and center, but those that support learners to do what they have always done: connect with educators, practice with concentration, and learn and grow.

AI Can Build Your Course, but Can it Design the Learning? — from drphilippahardman.substack.com by Dr. Philippa Hardman
Aka, the current state of AI’s instructional design ability & what it means for L&D

So these models have learned design artefacts in extraordinary volume and design method barely at all. They know what e-learning looks like. They have very little access to why any of it looks that way and whether it actually works or not.

That produces a completely consistent behaviour: ask an LLM for an e-learning module and it generates the statistical centre of every e-learning module it has ever seen. What AI produces isn’t the output of a design process. It’s the output of a sampling process — the statistical centre of every e-learning module it has ever seen.
…
So the honest conclusion isn’t that AI can design learning; it’s that AI executes design well when someone who already knows what to ask for is doing the asking and the checking.

This finding gives us a clearer division of labour than we’ve had before and with it a clearer picture of what the AI-enhanced workflow might look like:

The Pedagogy of Trading Places: Lessons from an Unexpected Role Reversal with AI — from facultyfocus.com by Sherrie Myers Bartell, PhD

These disruptions matter. They remind us that teaching is not a static identity but a dynamic posture. They show us that our pedagogical selves are not fixed; they are responsive to context, energy, and attention. They reveal that the qualities we value most in our teaching—curiosity, metaphor, play, and expansiveness—are not automatic. They require care and cultivation.

The AI didn’t “teach” me in the traditional sense. But it did something pedagogically adjacent: it surprised me back into myself.

The AI-native university, AI literacy, AI and accessibility + other items re: AI in our learning ecosystems

On 07/22/2026, in 21st century, 24x7x365 access, accessibility / UDL, Artificial Intelligence/Agents/LLMs/and Related, colleges, communities of practice, community colleges, corporate / business world, corporate universities / corporate training, data related items, education technology, emerging technologies, experimentation, faculty/staff, game-changing environment, generational differences, higher education, human-computer interaction (HCI), innovation, instructional design, intelligent systems, intelligent tutoring, IT in HE, learning, learning ecosystem, Learning Experience Design, liberal arts, lifelong learning, personalized/customized learning, productivity / tips and tricks, student-related, teachers, teaching & learning, training / L&D, trends, United States, universities, by Daniel Christian

AI & Accessibility: 5 Practical Ways to Use LLMs to Support Inclusive Learning — from learningguild.com by Dr. Athena Joyce Stanley (B.A., M.A.E., Ph.D.)

The following strategies focus on how instructional designers can design for accessibility by embedding or enabling AI-supported tools within learning experiences. These approaches move beyond access to content and begin to support access to understanding, expression, and participation.
…
Personalized Clarification Through AI Copilots
Learners often need additional explanation, but may hesitate to ask questions in live sessions or require more individualized support.

Instructional designers can integrate AI copilots, LLM-powered conversational interfaces, into learning environments to provide on-demand clarification. These tools can act as digital coaches, helping learners understand concepts through simplified explanations and relevant examples.

This approach supports learners who benefit from additional processing time and personalized guidance.

Sample Prompt (for IDs Configuring Copilot Behavior)
You are a workplace learning coach.

Explain the following concept in a clear, simple way:

- Use plain language
- Provide one practical, job-relevant example
- Avoid jargon unless necessary (and define it if used)

Then ask one follow-up question to check for understanding.

Jeff’s posting on LinkedIn.

Teaching AI Literacy with Susan Ray — from intentionalteaching.buzzsprout.com by Derek Bruff and Susan Ray

Per Derek Bruff:
In my conversation with Susan, we talk about that syllabus activity, as well as the AI transparency journals she asks her students to keep during her courses. We also talk about how her personal background prepared her to navigate the challenges and opportunities that AI has posed to her teaching, how she approaches assessing student learning in this age of AI—especially in her online asynchronous courses—and much more.

Building the AI-Native University: Student Success and Lifelong Learning — from coursera.org

In this episode, you’ll discover:

Why building an AI-native campus requires a data-first foundation
How to build an “achievement architecture” for lifelong success
How faculty-built AI tutors cut one program’s course attrition from 40% to 10%
What it means to be a lifelong learning companion

We’re Raising the First AI Generation. They’re Pushing Back. (Rebecca Winthrop, Brookings) — from humanistxyz.substack.com by Allison Dulin Salisbury
“Young people harbor real anger about AI because they’re already experiencing its consequences in their schools, relationships, job prospects, and feeds.”

As Rebecca told me:

“Kids harbor real anger. They’re pissed about climate impacts. They’re upset about job prospects. They’re outraged about AI being used to plagiarize writing and produce counterfeit artwork. Their relationships are being affected, and they’re seeing deepfakes in their feeds.”

Our conversation explores what follows from taking those concerns seriously. We discuss cognitive stunting, the case for small, purpose-built AI models over frontier models in education, why students should spend far more time in explorer mode, and how schools can cultivate agency, curiosity, and independent thinking in an age of AI.

The interview ends with one of Rebecca’s most practical recommendations: every school should have a student AI council, not as a symbolic gesture, but with real influence over the tools their schools adopt, the ways they’re used, and the data students are asked to hand over.

Every generation of technology seems to relearn the same lesson: build with people, not for them. AI should be no exception.

Microsoft Discovery Platform Brings Agentic AI to Scientific Research [Paoli]

On 07/14/2026, in Artificial Intelligence/Agents/LLMs/and Related, colleges, community colleges, emerging technologies, faculty/staff, higher education, intelligent systems, Microsoft, platforms, research, science, STEAM-related, tools, United States, universities, vendors, by Daniel Christian

Microsoft Discovery Platform Brings Agentic AI to Scientific Research — from campustechnology.com by Chris Paoli

Key Takeaways

Microsoft Discovery reaches general availability, bringing agentic AI to scientific research and development workflows.
AI agents support hypothesis generation, experimentation, data analysis, and knowledge management at scale.
New Discovery app preview enables researchers to explore AI-driven scientific discovery with lower adoption barriers.

Why recruiters can’t find workers and new grads can’t find jobs (it’s not AI) — from washingtonpost.com by Jon Marcus
Experts say a major labor shortage looms because of population shifts and a mismatch between new graduates’ skills and employers’ needs.

Recent college graduates complain they can’t find entry-level jobs because artificial intelligence is taking over.

Yet, tech recruiter Matt Walsh and other experts say the growth of AI and the struggle to find entry-level work mask a bigger problem: The United States is facing what’s projected to become the largest labor shortage in its history.

In sectors such as semiconductor production, the problem isn’t AI or too few jobs, said Walsh, CEO of the Phoenix-based search firm Blue Signal.

“It’s ridiculous,” he said. “There just aren’t enough people.”

Economists warn that the worsening labor problem, due in part to a skills shortage and population shifts, will be vast and reach beyond tech.

Among the trends that have been leading to this moment: a mismatch between the careers college graduates are pursuing and the jobs employers are struggling to fill. Far fewer students are majoring in health care fields than are needed to meet demand, for instance.

“We have pumped so many young people into business and finance” when what’s really in demand are graduates in other fields, Hetrick said. “It’s like a factory producing these workers like widgets, even though society is saying, ‘We really don’t need them.’ And the factory just keeps pumping them out.”

“Teachers ban it. Employers demand it.”

Also relevant/see:

The Shifting Career Ladder — from nafez.substack.com by Nafez Dakkak
AI is changing how work works and quietly removing the pathways through which young people learn to become experts.

AI is reshaping how people build skills, enter professions, and move along the career ladder and through the labour market.

In this conversation, I sit down with Matt Sigelmen founder of LightCast and now the President of Burning Glass Institute. Matt has dedicated his career to understanding the labor market and helping society improve the connections within in it.

Matt and I explore why people and opportunities are often only “a few skills apart,” why entry-level work may be losing its traditional role as the first rung of expertise, and why schools, universities, and employers now need to rethink the pathways that turn potential into mastery.

Educators need to be deeply aligned with what these changes are, and they need to shift the AI discourse from “how” questions to “what” questions. What do we need to teach? What do we need to keep in the curriculum?

The Law School Deans Driving AI Innovation in Legal Education — from natlawreview.com by Shivani Vedhere, AI & the Law Newsletter; via Colin S. Levy

Artificial intelligence is no longer a peripheral issue for legal education. It is quickly becoming one of the central questions facing law schools: how to prepare future lawyers for a profession in which AI will affect research, client counseling, litigation strategy, access to justice, and the business of law.

For decades, law schools treated legal technology as an elective or a niche interest for students already inclined toward innovation. That era is ending. Law firms are adopting AI tools at scale and even investing in developing their own tools. Clients are asking harder questions about efficiency, cost, and competence. Courts are sanctioning lawyers and litigants for AI-generated hallucinations, with the number of identified cases in the United States now exceeding 1,000. Students entering the profession will be expected to keep up with this rapidly changing landscape.

The most forward-looking law schools are responding accordingly. That transformation is being driven in large part by a group of innovative law school deans who are treating AI not as a passing trend, but as a structural change in legal education.

These initiatives signal a broader shift in legal academia where law schools are no longer merely debating whether AI belongs in the curriculum. The more pressing question is how deeply, how early, and how responsibly AI should be integrated into legal education.

Learning Ecosystems

How to Design Around Cognitive Offloading [Hardman]

This quote/insight is also for the Instructional Technologists out there

The 2031 Crisis in Higher Education: a stark scenario [Bryan Alexander]

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