IT in HE

A Practical Digital Accessibility Check for Learning Materials [Bigelow]

On 09/19/2026, in accessibility / UDL, Australia, education, IT in HE, neurodivergent/special needs, student-related, teachers, teaching & learning, by Daniel Christian

A Practical Digital Accessibility Check for Learning Materials — from educationtechnologysolutions.com by John Bigelow

A learning resource can be accurate, attractive and impossible for some students to use.

The barrier is often not dramatic. It may be a scanned PDF that cannot be read aloud, a video without captions, a chart whose meaning depends on colour, or a page with headings that only look like headings. Each problem is small at the point of creation and expensive when a student encounters it during a lesson.

Accessibility is therefore best treated as part of preparing the material, not as a repair requested after access has already failed.

Australian educators and education providers have obligations under the Disability Discrimination Act 1992 and the Disability Standards for Education 2005, including making reasonable adjustments so students with disability can participate and learn on the same basis as other students. The Australian Government provides educator resources explaining those responsibilities.

The following check is not a full accessibility audit. It is a practical first pass that teachers can apply before uploading a document, slide deck, video or LMS page.

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.

Risks and Rewards of IoT in Higher Education [Dickson]

On 09/16/2026, in 24x7x365 access, colleges, communities of practice, community colleges, computer science, higher education, internet, IT in HE, productivity / tips and tricks, student-related, the downsides of how people use tech, United States, universities, by Daniel Christian

Risks and Rewards of IoT in Higher Education — from edtechmagazine.com by Akeya Dickson
Shadow IT is flooding college and university networks as students move back to campus. Here’s how higher ed IT leaders can secure their networks without impacting the student experience.

Connected minifridges, smart speakers and internet-based gaming consoles are among the devices students are bringing to college that IT departments may not have accounted for — or even know are connected to the campus network. These devices may run on default passwords and have little to no security configuration, making them an attractive and vulnerable entry point for a cyberattack.

For higher ed IT, the stakes couldn’t be higher. According to the Zscaler ThreatLabz 2025 Mobile, IoT and OT Threat Report, the education sector saw an 861% rise in malware attacks from Internet of Things devices between 2024 and 2025.

How a studio-based format transforms online teaching [via multiple authors from Cranfield University]

On 09/10/2026, in adjunct faculty, collaboration, colleges, communities of practice, community colleges, content development, aggregation, repositories, digital audio, digital learning, digital video, distance education, education technology, engagement / engaging students, experimentation, faculty/staff, higher education, ideas, instructional design, IT in HE, law schools, learning, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, multimedia, online learning, online media, pedagogy, teaching & learning, tools, UK, universities, using teams, vendors, vision/possibilities, by Daniel Christian

How a studio-based format transforms online teaching — from timeshighereducation.com by multiple authors from Cranfield University in England
Online teaching works best when teaching, facilitation and technical delivery are treated as complementary professional roles. Read guidance on studio-based delivery

Our experience of studio-based delivery suggests the need for a different approach. In this model, teaching is delivered as a live, facilitated learning experience in which academics focus on teaching and interaction, while facilitators and production staff manage interaction, session flow and technology.

Responsibilities are intentionally divided to enable more effective online pedagogy: academics concentrate on explaining concepts, facilitating discussion and responding to students while production staff manage the technical environment, transition between activities, recordings, multimedia integration, chat moderation, breakout rooms and troubleshooting, enabling a more engaging and authentic learning experience.

The most transferable lesson is that online teaching works best when teaching, facilitation and technical delivery are treated as complementary professional roles and when it is underpinned by pedagogical design.

“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.

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.

The Current State of Play: AI in Higher Education and the Road Ahead [Gamby, Kil, Koblic, LeBlanc, Moldoveanu & Siemens]

On 06/29/2026, in 21st century, Artificial Intelligence/Agents/LLMs/and Related, business side of he, career development, change, colleges, community colleges, corporate / business world, culture, emerging technologies, ethics, expectations, experimentation, faculty/staff, game-changing environment, generational differences, higher education, ideas, innovation, instructional design, intelligent systems, IT in HE, leadership, learning, learning agents, learning ecosystem, Learning Experience Design, liberal arts, morals/values, organizational change, pace of change, parents/guardians, personalized/customized learning, policy, reinvent, relationships, skills, society, strategy, student-related, teaching & learning, trends, United States, universities, vision/possibilities, by Daniel Christian

The Current State of Play: AI in Higher Education and the Road Ahead — from er.educause.edu by Tanya Gamby, David Kil, Rachel Koblic, Paul LeBlanc, Mihnea Moldoveanu and George Siemens

The conventional explanation for this strategic vacuum points to the speed of technological change; it is moving too fast for institutions built for deliberation. That is true. . . and incomplete. The deeper issue is cultural. In fairness to higher education, many industries are struggling to keep up with the pace of AI advances. Higher education, however, moves even more slowly and is not built for the kind of transformational speed now underway. Getting institutional stakeholders to engage, rethink the work, and move faster may be the central challenge facing presidents and chancellors today, and that’s saying a lot in such volatile times.

From DSC:
I highlighted this paragraph because it hits upon the key item involved here — culture. “The deeper issue is cultural.” I think that’s a very true statement.

Part of the culture and setup of many institutions includes giving faculty members full rein of their classes and their departments. Faculty members have a great deal of leeway and power in how they do things. So trying to get X faculty members to get on board — including the Department Chairs — is not an easy task.

Another part of culture involves being willing — or not — to change in the first place. Some institutions are like Google and are used to making changes and being more innovative. But those institutions are not the norm, at least in my experience. And this doesn’t even address another topic the article mentioned — the pace of these changes. As the authors point out, most institutions of traditional higher education are not equipped to deal with the current pace of change (nor are most of our other types of institutions and our corporations as well).

I’m going to end this posting with another brief excerpt from the article:

Institutions rooted in human relationships, committed to truth-seeking, and oriented toward the full development of persons play a central role. AI cannot manufacture the experience of mattering to another human being. It cannot model intellectual courage or ethical discernment. It cannot build the kind of community in which students discover who they are and what they believe.

These are not small things. They are, in fact, the things most worth doing. At their best, colleges and universities are not only preparing better workers but shaping individuals and strengthening society.

Check Your Mic Before You Wreck Your Project [Rasnake]

On 06/29/2026, in A/V -- audio/visual, content development, aggregation, repositories, corporate universities / corporate training, digital audio, instructional design, IT in HE, productivity / tips and tricks, tools, training / L&D, vendors, voice recognition / voice enabled interfaces, by Daniel Christian

Check Your Mic Before You Wreck Your Project — from learningguild.com by Kendal Rasnake

While a lot of our narration may be produced by AI nowadays, there are times when you need to record audio, such as when you need someone in-house to do a voiceover, or you are recording an interview, job shadow video, demonstration video, etc. Now, the responsibility of recording high-quality audio falls on you.

Well, all you have to do is grab a mic and point, right? Wrong!

The last thing you need is to record the CEO and have him/her sound horrible or look ridiculous because they are holding a fuzzy mic on a long wire up to their mouth. Instead, just learn a little about mics and you can purchase and/or choose the right one.

All Mics Are Not Built Equally
I had someone who was having audio trouble tell me that they used a “Brand Name” mic before and it sounded good, so maybe they would go back to using a “Brand Name” mic. As you can imagine, choosing a mic for a certain purpose based on the brand name is equivalent to choosing a Chevy mini-hybrid car to tow an RV because your truck used to tow the RV well and it was a Chevy. Brands make different types of microphones and understanding how mics are built can help you to choose the right one, no matter the brand.

AI’s getting expensive for us these days

On 06/23/2026, in Adobe, Artificial Intelligence/Agents/LLMs/and Related, business side of he, change, changing business models, cloud-based computing / apps / other cloud-related, colleges, communities of practice, community colleges, content development, aggregation, repositories, corporate / business world, cost of getting a degree, Daniel S. Christian, education technology, emerging technologies, experimentation, faculty/staff, funding / grants, game-changing environment, graphics, higher education, human-computer interaction (HCI), innovation, intelligent systems, IT in HE, learning ecosystem, liberal arts, library / librarians, new business models, Open AI, platforms, student-related, teaching & learning, technologies for your home, tools, trends, United States, universities, by Daniel Christian

From DSC:
I used to be able to bring up Firefly on the web and use it “free” of charge — I didn’t have to go purchase tokens or credits. (I was actually paying for the Adobe Creative Cloud Pro suite of tools…so it wasn’t really free.)

But the other day I was trying to figure out what the latest pricing is at Adobe with that suite of tools and the use of credits for AI-based features. They say Adobe Creative Cloud Pro users get 4000 credits a month. Well, I have that suite and I’m still getting prompted to purchase credits. Firefly for individuals runs from $9.99 (2,000 credits/month) to $139.91 per month (50,000 credits per month). Not inexpensive, right? Below are other items along these lines.

The Era of Affordable AI Is Over. What Comes Next? — from builtin.com by Ameya Kanitkar
AI providers are shifting to usage-based billing for their services. AI fluency is more important now than ever to make the most of your tools to avoid unnecessary spending.

Summary: The era of cheap, flat-rate AI is ending as providers shift to usage-based billing. Every prompt now carries a direct cost, turning casual use into major budget risks, as seen when Uber depleted its 2026 AI budget in four months. Leaders must now track real-time value and token efficiency.

For a brief window, companies had access to the most transformative technology in a generation at the cost of a streaming subscription. Tools like ChatGPT put AI within reach of anyone with a browser and time for experimentation, while GitHub Copilot came in at just $10 a month, with token costs remaining relatively low. In the beginning, experimentation felt cost-effective, easy and relatively low-risk.

But that era is ending, and the bill is coming due faster than a lot of enterprise leaders anticipated.

The Fable of AI in Education — from downes.ca by Stephen Downes
Marc Watkins, Rhetorica, Jun 17, 2026

Tokenomics will be a hot topic of discussion on university campuses because, as Marc Watkins notes in this article, there is no realistic path forward to providing all students with access to advanced AI.

From this posting on LinkedIn.com from Dr. Nick Jackson:

And now there is a third layer emerging. Institutions are waking up to a systems-level question they are likely not remotely prepared for. Who pays for AI? How are budgets managed when there are unclear token consumption pricing models? How is AI procured? Who decides what tools get used and by whom and who gets access and at what level?

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Why Students Aren’t All In on AI—And What They Want From Colleges — from insidehighered.com by Colleen Flaherty
New Student Voice data reveal students are embracing AI as a learning tool while worrying about dependence, career disruption and inconsistent institutional responses.

Read on for six takeaways from the survey and additional insights—including how institutions can start to close the gap between students’ optimism about AI as a learning tool and their faith in their colleges’ ability to help them navigate change.

Takeaway 1: More students are using AI than ever for coursework, while a significant share—20 percent—remain resisters.

Takeaway 2: “Worried about dependence” is the most common student stance on AI.

Takeaway 3: A majority of all students expect AI to somewhat (39 percent) or very (16 percent) negatively impact their career prospects.

Takeaway 4: Just one in 10 students says that their institution is handling AI’s rise very well, in a thoughtful and proactive way.

…and more >>

Pinpoint, Explained | A guide to Google’s free tool, now open to all [Caplan]

On 06/05/2026, in adjunct faculty, Artificial Intelligence/Agents/LLMs/and Related, colleges, communities of practice, community colleges, content development, aggregation, repositories, data related items, digital audio, digital learning, digital video, education, education technology, faculty/staff, Google, higher education, human-computer interaction (HCI), ideas, innovation, instructional design, intelligent systems, IT in HE, journalism, K-12 related, languages and translation, learning, learning agents, learning ecosystem, liberal arts, library / librarians, research, teaching & learning, tools, United States, universities, vendors, by Daniel Christian

Pinpoint, Explained — from wondertools.substack.com by Jeremy Caplan
A guide to Google’s free tool, now open to all

.Jeremy prompted ChatGPT to generate illustrations in his post.

.

Learn about Pinpoint— from support.google.com

Pinpoint is an AI-powered research platform designed to help journalists and academics analyze large collections of documents. With Pinpoint, you can:

Analyze massive collections: Easily search, filter, transcribe and organize thousands of documents, including PDFs, images, and audio files.
Leverage generative AI: Use Gemini’s capabilities to answer research questions together with supporting evidence found in your documents.
Foster collaborative research: share your work with colleagues and tackle large scale projects as a team. You can also publicly share – supporting community-driven research.

For assistance with Pinpoint, please consult our Community Forum or you can contact our support team.

Addendum:

AI Budgets in Education Show No Sign of Decline — from campustechnology.com by Rhea Kelly

Key Takeaways

Education AI budgets are holding steady or increasing: Wasabi found that 98% of education organizations expect AI infrastructure budgets to increase or remain steady, with 46% planning increases.
Storage costs are the top AI implementation challenge: Half of education respondents cited data storage issues, including storage and access costs, as the No. 1 challenge for AI projects.
Cloud security and ROI remain pressure points: Only 47% feel confident keeping data unaltered and operational after a cyberattack, 44% lost access to public cloud data after an attack, and 37% of AI projects currently show positive ROI.

4 Strategies For Teaching With AI Effectively — from techlearning.com by Erik Ofgang
Health sciences professor Humberto López Castillo urges students to use AI to help with science research, but never to lose sight of the human element.

Castillo, a trained pediatrician and professor in the Department of Health Sciences, has also seen students use AI in creative ways to promote public health understanding, and as a research tool. For one project, Castillo asks students to explain health concepts from class to non-experts, and since he started encouraging students to use AI, he’s seen the projects get better. Students have created health-themed board games and Hamilton-style rap songs. Others have designed AI to aid in health research in ways that wouldn’t be possible without the technology.

This compassionate and student-centered approach to AI use is part of why Castillo was named Superhuman (formerly Grammarly’s) 2026 Educator of the Year.
.

.

“You are the one who’s responsible for that writing,” Castillo tells his students. “Your name is the only name that’s going to be among the published authors, so you are the one who needs to verify those sources.”

He adds that rather than being a drawback, allowing students to make these types of mistakes with AI use in the college setting has value.

“It is a teaching opportunity,” Castillo says. “This is the moment to make those mistakes.”

Workplace Readiness: Can Higher Education Develop AI-Ready Students? — from learningguild.com by Eddie Lin and Roshan Bharwaney

For higher education to remain relevant, curricula must evolve. Here are some overarching recommendations for directions in higher education to bridge the skills gaps between universities and workplaces:

AI ethics and safety: Prepare students to navigate issues of fairness, bias, privacy, and societal impact.
Tackling complex questions: Emphasize open-ended challenges that blend structured and unstructured skills and reduce reliance on standardized tests and repetitive drills.
Critical thinking: Develop new assessments for judgment, creativity, and metacognition—essential to supervise AI outputs.
Human-AI synergy: Embed AI fluency across all disciplines, encouraging students to find the niches where human value is maximized.
Industry connection: Maintain close industry partnerships and collaborations including open innovation opportunities and collective intelligence approaches (Bharwaney & Sleeva, 2024).

Experiential learning and communities of practice are central to this vision. Internships, simulations, and cross-disciplinary projects can help students practice human-AI collaboration, resilience, and decision-making in environments that mirror the workplace’s ambiguity and complexity.

Universities that condemn the use of AI by students risk isolating themselves from the realities of today’s workplace, where interns and new hires are expected to be or quickly become adept at using AI for routine tasks and complex projects.

A New Era of Security: Frontier AI Defense — from paloaltonetworks.com by Sam Rubin

For the last several months, we have had early, unbounded access to the latest frontier AI models. What we’ve seen from that vantage point has made it clear that the window for organizations to get ahead of what’s coming is shorter than most leaders realize.

We have moved past the era of incremental AI improvements into a threat landscape shift. Our testing has revealed a step-change in capability that demonstrates an intuitive understanding of software vulnerabilities. This is more than faster code generation, it is a shift from AI as an assistant to AI as an autonomous agent capable of discovering and chaining flaws at a scale that most defenders aren’t prepared for.

These capabilities will not stay confined to controlled environments for long. When Mythos first launched, we predicted a six-month window before attackers gained access. We now believe that timeline has accelerated significantly.

Learning Ecosystems

How a studio-based format transforms online teaching [via multiple authors from Cranfield University]

Check Your Mic Before You Wreck Your Project [Rasnake]

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