human-computer interaction (HCI)

The Age of Perpetual Change — from educause.edu
Artificial intelligence (AI) has risen to the number one spot on the 2027 EDUCAUSE Top 10. But its rank signals urgency more than consensus, and higher education is still grappling with what AI-driven change could mean for institutions. In many ways, AI embodies a broader reality: We’re living in an age of perpetual change.

2027 Top 10 Overview
Technological innovation is outpacing our ability to manage and harness it. The political and economic winds around us are erratic. And higher education itself is undergoing profound shifts that will reshape how our institutions function.

While this atmosphere breeds uncertainty and anxiety, it can also inspire innovation and growth. The 2027 EDUCAUSE Top 10 highlights how the higher education technology community is responding to those opportunities.

The State of AI Report 2026 — from press.airstreet.com by Nathan Benaich
AI is building AI, physical AI, booming inference, and escalating cyber risks.

For nearly a decade, this report has been a labor of love. My interest in AI began with my PhD in cancer research and computational biology in the early 2010s. Since 2013, I’ve invested in companies building and applying AI to accelerate technological progress. The report is my way of sharing what I’m learning and helping more people understand the research shaping our world.

Every day brings new papers, model releases, and developments in industry and politics. Much of the work goes into deciding what deserves your attention, checking what the evidence supports, debating it with researchers and builders, and explaining why it matters.

The AI Tools that L&D Teams Are Building (Rather than Buying) — from drphilippahardman.substack.com by Dr. Philippa Hardman
Aka, how build skills are changing how L&D thinks & works

More and more of my advisory work is no longer about helping organisations choose a vendor; it’s about helping their teams build their own AI tools, and build them well. Over the last couple of years I’ve watched several hundred L&D practitioners do it, and in this week’s post I share what they built, why, and what it tells us about what L&D actually needs from AI.

A Tool of One’s Own
So, what do these tools look like in practice? I did some analysis and the tools that L&D teams are building first fall into three categories:

1. Tools that support the work before and after the course
2. Tools that make the learning experience more dynamic
3. Tools that do L&D tasks to the team’s own methods, processes and standards

Some solid warnings about AI from Blake Montgomery’s recent newsletter:

There is an extensive and ongoing review related to our agents’ use of internet access during training and evaluation. We’ve been publishing summaries at the link below and will continue to.

We have not been as fast as we would have liked but we are trying to balance our desire… https://t.co/8zoMxas5Eq

— Sam Altman (@sama) September 25, 2026

Scoop: Top AI companies probing tens of thousands of security incidents — from axios.com by Madison Mills

OpenAI, Anthropic and security researchers are investigating tens of thousands of incidents in which their frontier models took steps that outside evaluators would consider problematic, sources told Axios.

Why it matters: The sheer number of incidents, which occurred in recent months in internal testing and the real world, indicates that the problem is orders of magnitude more complex than what is publicly known.

The findings, which are surfacing as part of internal work to assess models and in investigations at both companies into model behavior, raise questions about whether either company — or any top model-maker — is currently capable of establishing complete control over its technology.

Key Risk Factors for AI Loss of Control Came Together in 2026 Incident, Independent UN Scientific Panel Finds
Halting this incident is no assurance that humans will keep control of more capable systems

NEW YORK, 21 September 2026 – The Independent International Scientific Panel on AI, established by the UN General Assembly, released its first thematic brief, an assessment of the breach of Hugging Face’s systems by AI agents under evaluation at OpenAI this summer. The Panel, made up of 40 independent experts from all regions, is publishing it as an advance unedited version as world leaders gather in New York for the Assembly’s High-Level Week.

“Researchers have long warned that three conditions could lead to loss of control: a misaligned goal, the capability to pursue it, and an environment that allows it. This summer, all three came together in a real system, not a laboratory. Since this is not an isolated observation of misaligned goals, this raises serious questions about the way AI agents are currently trained.” – Yoshua Bengio, Co-Chair of the Panel and Turing Award laureate

Meta’s New Muse AI Agent Read My Private Messages. I Never Asked It To — from inc.com by Jason Aten
Permission isn’t the same and what a user actually expects your AI product will do with their personal information.

That obviously requires a certain amount of trust. An AI agent isn’t especially useful if it can’t see your files, interact with your apps, or understand what you’re working on. Meta says Muse is designed around that reality, while still putting users in control of what it can access.

At least, that’s what I thought.

Not only had I not asked it to do that sort of thing, I never gave it permission to read my messages. In fact, I remember explicitly choosing not to let it have access to my messages, calendar, and other personal information.

Although not from Blake, also see this free/gifted article out at the Washington Post:

ChatGPT-maker’s AI inappropriately probed federal government websites — from washingtonpost.com by Gerrit De Vynck and Nitasha Tiku
OpenAI said that its artificial intelligence agents inappropriately accessed sites for the Commerce Department and the Securities and Exchange Commission.

SAN FRANCISCO — Artificial intelligence technology from ChatGPT maker OpenAI probed U.S. government websites including the Departments of Education and Commerce, researchers said Friday, adding to the growing list of incidents in which OpenAI’s AI agents acted without the company’s knowledge.

The company’s AI agents attempted to hack into the website for the Education Department’s Office for Civil Rights but were not successful, according to a statement Friday from AI research firm Transluce. OpenAI’s software also accessed data from the U.S. Census Bureau using log-in information discovered on the web and copied public information from the Securities and Exchange Commission, a spokesperson for OpenAI said after the Transluce statement.

How to Improve Your AI Skills [Andrade & Caplan]

On 09/19/2026, in Artificial Intelligence/Agents/LLMs/and Related, Google, human-computer interaction (HCI), Open AI, platforms, productivity / tips and tricks, professional development, skills, tools, United States, vendors, by Daniel Christian

How to Improve Your AI Skills — from wondertools.substack.com by Frank Andrade and Jeremy Caplan
What to learn next, from prompts to workflows
.

Today I’m sharing a guest post on how to strengthen your AI skills. It’s by Frank Andrade, an AI instructor who has helped thousands of people on Substack master ChatGPT and Claude with beginner-friendly guides and in-depth tutorials.

Will smart glasses make it too risky to speak up in class? — from timeshighereducation.com by Georgia Luckhurst & Kieran Phelan
AI-powered glasses may boost learning but they also pose obvious threats to privacy, academic integrity and the sanctity of the seminar room as a safe space to explore ideas. Are universities doing enough to head off the risks, asks Georgia Luckhurst – while Kieran Phelan suggests they are not

Smart glasses can superimpose information on to a viewer’s field of vision, and they can access apps and communicate with your phone. Some are hands-free while some require touch, and some also respond to voice automation. Most controversially, they can take pictures and record footage, potentially without anyone else knowing that it is happening. And they are becoming ever more common.

But, for academia, the biggest issue associated with smart glasses may be a decline in intellectual risk-taking and freedom of expression, Murray said.

How useful are smart glasses in improving accessibility in higher education? — from timeshighereducation.com by Helen Nicholson-Benn
Smart glasses have the potential to support learning for disabled students, but this technology also comes with significant privacy concerns. Helen Nicholson-Benn looks at how to balance functional benefits with data security and safeguarding

Consider how different models of smart glasses might be used to improve accessibility in higher education. A deaf student might use XRai’s ar2 captioning glasses during a lecture to view live captions in their eyeline, allowing them to follow the content without looking down at a separate screen. A visually impaired student might use Envision’s Ally Solos glasses to generate a description of images on printed material or read text during a class. The glasses provide a hands-free option rather than scanning the materials with their smartphone.

These examples are hypothetical, and smart glasses are far from an everyday teaching tool, but that may be about to change. Smart glasses available today are far more capable than their predecessors, particularly boosted by developments in artificial intelligence (AI) technology.

Which brings up the topic of how to handle these technologies in the classroom:

Are device-free classrooms the way forward? — from timeshighereducation.com by Georgia Luckhurst
US universities embrace laptop and phone bans, but professor suggests policies may be more about political positioning than pedagogy

Growing numbers of US universities are making some of their courses “device free”, drawing mixed enthusiasm from edtech experts.

In the latest move the University of Chicago is introducing a no-laptops and no-phones policy on three of its 15 required undergraduate modules, aiming to “reduce distractions and encourage face-to-face discussions”. Exceptions remain, including for students with disabilities.
…
Device-free policies appear in other countries, experts suggested, despite the trend picking up pace in the US.

How to Reduce Cognitive Load in Digital Lessons — from educationtechnologysolutions.com by etsmagazine

A digital lesson can be beautifully designed and still make learning unnecessarily hard.

Students may be asked to watch a video, remember an instruction, find a link, switch tabs, interpret unfamiliar icons and submit a response—all before they have dealt with the idea the lesson was meant to teach.

That friction is often mistaken for rigour. It is not. Productive difficulty comes from thinking about the content. Avoidable difficulty comes from navigating the lesson.

The goal is not to make learning effortless. It is to spend students’ limited attention on the knowledge and skill that matter.

The above article links to:

Managing cognitive load optimises learning — from edresearch.edu.au
Implications for policymakers

MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training — from aiandeducation.mit.edu

However, what we learned as a group quickly convinced us that the Institute community, particularly the faculty, must tackle a set of deeper questions about the structure, meaning, and value of an MIT education in an era in which AI is one of several factors complicating the Institute’s mission.
…

In this report, we:

Highlight key aspects of the current educational landscape at MIT.
Share eight principles we relied on and that we hope will guide the Institute in the work ahead.
Recommend immediate and long-term actions for both instructors and the administration.

On somewhat related notes:

Your AI Teaching Toolkit — from wondertools.substack.com by Jeremy Caplan
11 prompts to save time and spark ideas
AI Should Accelerate How We Learn About Learning (Rajen Sheth, Kyron Learning) — from humanistxyz.substack.com by Allison Dulin Salisbury
“We shouldn’t have to wait years to find out if an approach works. We can test and learn in weeks.”

‘You can’t just lecture students any more’ — from timeshighereducation.com by Debra Page
GenAI does not make lecturers redundant, but it does weaken one-way content delivery as a reason to bring students together, writes Debra Page. She shares four practical shifts to teaching in the age of AI

This does not make lecturers redundant; it makes one-way content delivery a weaker reason to bring students together. Expert explanation, intellectual storytelling and the modelling of disciplinary thinking still matter, but in-person teaching must also help students apply knowledge, question claims, build arguments and encounter perspectives beyond their own.

The problem is not lecturing; it is speaking for an hour and treating content coverage as evidence that learning has occurred.

These four practical shifts can make lectures more valuable in the age of AI.

Links:

We are at a tipping point. In the next 25 years, technologies like AI, clean energy, and bioengineering are poised to reshape society on a scale few can imagine.

Peter Leyden draws on decades of observing technological revolutions and historical patterns to show how old systems collapse, new ones rise, and humanity faces both extraordinary risk and unprecedented opportunity.

0:00 We’re on the cusp of an era of progress
0:37 The Great Progression
1:08 What was the ‘Long Boom?’
4:56 How often do these epoch resets happen?
6:12 3 Tipping points
6:39 Artificial Intelligence
7:13 Clean energy technologies
7:32 Biotechnology
9:00 The 80-year cycle
13:27 The Gilded Age
17:50 The Founding Era
22:46 The new enlightenment
32:18 The clean energy revolution
37:13 Bioengineering the genome
39:43 Industrial production vs biological engineering
47:40 What will the future think?

“Using AI to create the practice opportunities online students need” [Miller Perkins] + other items re: AI in our learning ecosystems

On 08/19/2026, in 21st century, 24x7x365 access, adjunct faculty, adult learning, Artificial Intelligence/Agents/LLMs/and Related, assessment, business, career development, colleges, communications, communities of practice, community colleges, content development, aggregation, repositories, corporate / business world, corporate universities / corporate training, digital learning, education, education technology, emerging technologies, Emotion, engineering, experimentation, faculty/staff, game-changing environment, higher education, homeschooling/homeschoolers, human-computer interaction (HCI), ideas, innovation, instructional design, intelligent systems, intelligent tutoring, IT in HE, K-12 related, leadership, learning, learning agents, learning ecosystem, Learning Experience Design, learning spaces, liberal arts, mathematics, online learning, personalized/customized learning, productivity / tips and tricks, professional development, student-related, teachers, teaching & learning, technologies for your home, tools, training / L&D, UK, United States, universities, vendors, workplace, youth, by Daniel Christian

Using AI to create the practice opportunities online students need— from timeshighereducation.com by Kathy Miller Perkins
For disciplines that depend on interpersonal skills such as communication, leadership, negotiation and mediation, AI can provide elusive experiential learning, particularly in asynchronous environments

What if AI’s greatest educational value lies not in generating answers but in generating experiences?

The result surprised me. Students did not simply interact with the AI, they meaningfully engaged with it. Many reported that the experience felt valuable precisely because the simulated conversation partner pushed back. Unlike classmates who sometimes hesitate to challenge one another, the AI consistently maintained its position. Students had to work harder, listen more carefully and apply the communication strategies they had spent weeks learning. And, importantly, they practised.

Here’s how to show students what responsible AI use looks like — from timeshighereducation.com by Andrew Firr and Alex Fenton
Well-designed assessments can highlight generative AI’s limitations, where it can provide support and what responsible practice looks like. Andrew Firr and Alex Fenton offer strategies

We have been exploring ways to offer this in our Level 7 module for MSc engineering management students, many of whom come from the Global South. Assessment is built around a practical design problem: students examine how a real campus process operates, where delays occur, evidence they observe and how a feasible redesign might improve the system – in a 3,000-word essay.

They can use AI for planning and providing clarity, but not for generating the evidence on which the analysis rests. Invented observations, measurements, screenshots or unverifiable citations are prohibited. The assessment design reinforces this through a case study, diagram, fact-checking exercise and a short AI use statement.

Requiring an AI use statement encourages students to reflect on their processes. We ask which tool they used, how they used it, how they checked the output and which parts of the work depended on their own evidence and judgement. It reminds them that evidence must be theirs and that citations must be manually verified.

Slow math: Kids may learn more when AI makes them review mistakes — from hechingerreport.org by Jill Barshay and Kristin Fasiang
A randomized experiment involved more than 6,000 Tennessee middle school students learning fractions

In an experiment involving more than 6,000 middle schoolers in Tennessee, students learned slightly more math when an AI tutor walked them through their mistakes and then required them to demonstrate the same skill correctly three times in a row before moving on.
…
The winning combination wasn’t the addition of AI tutoring alone, but AI tutoring plus repetition, with the idea that students needed to stick with the skill to demonstrate some level of mastery. The students who practiced math with this AI-enhanced “mastery learning” approach scored about 3 percentage points higher than students receiving conventional computerized instruction. The advantage was small.

I Built a Team of AI Bots That Write Feedback Better than Me. Here’s How. — from drphilippahardman.substack.com by Dr Philippa Hardman
Aka, how to make learners love AI-assisted feedback, rather than loathe it.

This wasn’t hidden from the cohort. For reasons I’ll come to below, I always tell my cohorts at the very start that an AI assistant will be helping me draft their feedback. The aim wasn’t to remove me from the process, but to find out whether AI could help me to deliver feedback at a volume and quality that I simply couldn’t maintain without AI.

Spoiler: the experiment worked — but not for the reason I expected. In this post, I’ll share exactly what I built, tell you how to build a version for your course and reveal the three conditions that decide whether learners trust AI-assisted feedback or quietly stop reading it.

TL;DR: the spec for my feedback assistant is long not because the model needs persuading, but because it’s where my judgement lives. Every rule in it is a decision AI would otherwise make by itself, with limited expertise. A spec doesn’t make the model smarter — it makes expertise and judgement legible enough for the model to follow.

Of the five stages of AI grief, some managers are stuck in denial— from timeshighereducation.com by Shadi Mohamed
Acceptance is not surrender. It is about getting beyond a policing obsession and rethinking what assessment aims to measure, says Shadi Mohamed

In that moment of institutional blindness, it struck me: universities are not merely struggling with a technological adoption curve. They are grieving the demise of the modern university’s economic and institutional logic.

To understand the scale of this threat posed by AI, consider what the internet did to journalism. Newspapers thrived by bundling together classified advertising with quick news hits, sports scores and opinion columns, using that reliable revenue to fund the expensive social necessity of investigative reporting. The rise of internet advertising did not kill journalism outright but it shattered the bundle, leaving the expensive core product without its historical financial engine.

Universities operate on a similar logic, bundling content delivery, assessment, credentialling, research and professional formation. But generative AI is now commodifying the most visible parts of that package. When AI can deliver personalised explanations instantly and generate the essays and reports we use to measure student capability, the traditional degree loses its role in the labour market as a proxy for understanding. If the bundle breaks, the economic model that funds our deeper purposes is in peril.

Student Voices: Technology as a Future Learning Partner — from campustechnology.com by Mary Grush
A Q&A with Eli Blouin and Mark Frydenberg

Key Takeaways

With advanced technologies, we’re moving from accessing learning resources to participating in technology learning partnerships.
Technology learning partners participate in the learning process by asking questions and giving feedback, not just information search results.
Tomorrow’s technology learning partners will understand the whole learner — including knowledge gaps and personal learning style.
Student experiences with AI and other technologies today will inform the next generation of human/machine learning partners.

Grush: What do you think may be among the most meaningful changes in, say, the next five years?

Blouin: When I think of what will improve in the next five years — and I truly believe it’s where we’re ultimately heading — I think it will be the technology learning partner’s ability to gain a full picture of the learner. The technology will be able to take absurd amounts of context — and data — and understand the learner as a whole: where their gaps are, what they need to learn, how they learn, and how to create content tailored to a particular student. In an instant it would be able to assess, to test knowledge, and know the student as a whole person. That would be an ideal learning partner.

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.

Will this new company be the largest company on the Internet by 2030? [Christian]

On 08/01/2026, in 21st century, 24x7x365 access, adult learning, Artificial Intelligence/Agents/LLMs/and Related, Bias | Diversity Equity & Inclusion (DE&I), career development, cloud-based computing / apps / other cloud-related, communities of practice, content development, aggregation, repositories, corporate / business world, corporate universities / corporate training, cost of getting a degree, credentialing, curriculum, Daniel S. Christian, digital learning, disruption, education, education technology, elearning, emerging technologies, engagement / engaging students, future, future of higher education, game-changing environment, generational differences, global, higher education, homeschooling/homeschoolers, human-computer interaction (HCI), ideas, informal learning, innovation, instructional design, intelligent systems, intelligent tutoring, internet, K-12 related, learning, learning agents, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, learning hubs & pods & microschooling, learning preferences, learning spaces, lifelong learning, love of learning, more voice more choice more control, motivation, online learning, online media, online tutoring, pathways, personalized/customized learning, platforms, productivity / tips and tricks, professional development, reinvent, society, staying relevant, student-related, teachers, teaching & learning, training / L&D, United States, vision/possibilities, web-based collaboration, youth, by Daniel Christian

From DSC:
The vision that I’ve been tracking for well over a decade begins with this graphic:
.

Could LearnVector be this next-generation company/platform? Perhaps. Time will tell.

Per Matt Tower via The EdSheet Vol. 38 dated 7/31/26:

Coursera bets $100M on its own co-founder: Andrew Ng’s new venture LearnVector lands one of the largest single checks in edtech this year — from Coursera, the company he founded 14 years ago.
.

LearnVector.ai

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