As a new academic year begins, many instructors, trainers, and program leaders are bracing for familiar challenges—keeping learners engaged, making complex material accessible, and preparing students for real-world application.
But there’s a quiet shift happening in classrooms and online courses everywhere.
This fall, it’s not the syllabus that’s guiding the learning experience—it’s the conversation between the learner and an AI tool.
From bootcamp to bust: How AI is upending the software development industry — from reuters.com by Anna Tong; via Paul Fain Coding bootcamps have been a mainstay in Silicon Valley for more than a decade. Now, as AI eliminates the kind of entry-level roles for which they trained people, they’re disappearing.
Coding bootcamps have been a Silicon Valley mainstay for over a decade, offering an important pathway for non-traditional candidates to get six-figure engineering jobs. But coding bootcamp operators, students and investors tell Reuters that this path is rapidly disappearing, thanks in large part to AI.
“Coding bootcamps were already on their way out, but AI has been the nail in the coffin,” said Allison Baum Gates, a general partner at venture capital fund SemperVirens, who was an early employee at bootcamp pioneer General Assembly.
Gates said bootcamps were already in decline due to market saturation, evolving employer demand and market forces like growth in international hiring.
Millions of college students around the world are getting ready to start classes. To help make the school year even better, we’re making our most advanced AI tools available to them for free, including our new Guided Learning mode. We’re also providing $1 billion to support AI education and job training programs and research in the U.S. This includes making our AI and career training free for every college student in America through our AI for Education Accelerator — over 100 colleges and universities have already signed up.
… Guided Learning: from answers to understanding
AI can broaden knowledge and expand access to it in powerful ways, helping anyone, anywhere learn anything in the way that works best for them. It’s not about just getting an answer, but deepening understanding and building critical thinking skills along the way. That opportunity is why we built Guided Learning, a new mode in Gemini that acts as a learning companion guiding you with questions and step-by-step support instead of just giving you the answer. We worked closely with students, educators, researchers and learning experts to make sure it’s helpful for understanding new concepts and is backed by learning science.
Another major AI lab just launched “education mode.”
Google introduced Guided Learningin Gemini, transforming it into a personalized learning companion designed to help you move from quick answers to real understanding.
Instead of immediately spitting out solutions, it:
Asks probing, open-ended questions
Walks learners through step-by-step reasoning
Adapts explanations to the learner’s level
Uses visuals, videos, diagrams, and quizzes to reinforce concepts
I’m not too naive to understand that, no matter how we present it, some students will always be tempted by “the dark side” of AI. What I also believe is that the future of AI in education is not decided. It will be decided by how we, as educators, embrace or demonize it in our classrooms.
My argument is that setting guidelines and talking to our students honestly about the pitfalls and amazing benefits that AI offers us as researchers and learners will define it for the coming generations.
Can AI be the next calculator? Something that, yes, changes the way we teach and learn, but not necessarily for the worse? If we want it to be, yes.
How it is used, and more importantly, how AI is perceived by our students, can be influenced by educators. We have to first learn how AI can be used as a force for good. If we continue to let the dominant voice be that AI is the Terminator of education and critical thinking, then that will be the fate we have made for ourselves.
AI Tools for Strategy and Research – GT #32 — from goodtools.substack.com by Robin Good Getting expert advice, how to do deep research with AI, prompt strategy, comparing different AIs side-by-side, creating mini-apps and an AI Agent that can critically analyze any social media channel
In this week’s blog post, I’ll share my take on how the instructional design role is evolving and discuss what this means for our day-to-day work and the key skills it requires.
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With this in mind, I’ve been keeping a close eye on open instructional design roles and, in the last 3 months, have noticed the emergence of a new flavour of instructional designer: the so-called “Generative AI Instructional Designer.”
Let’s deep dive into three explicitly AI-focused instructional design positions that have popped up in the last quarter. Each one illuminates a different aspect of how the role is changing—and together, they paint a picture of where our profession is likely heading.
Designers who evolve into prompt engineers, agent builders, and strategic AI advisors will capture the new premium. Those who cling to traditional tool-centric roles may find themselves increasingly sidelined—or automated out of relevance.
Google’s parent company announced Wednesday (8/6/25) that it’s planning to spend $1 billion over the next three years to help colleges teach and train students about artificial intelligence.
Google is joining other AI companies, including OpenAI and Anthropic, in investing in AI training in higher education. All three companies have rolled out new tools aimed at supporting “deeper learning” among students and made their AI platforms available to certain students for free.
Based on current technology capabilities, adoption patterns, and the mission of community colleges, here are five well-supported predictions for AI’s impact in the coming years.
“Where generative AI creates, agentic AI acts.” That’s how my trusted assistant, Gemini 2.5 Pro deep research, describes the difference.
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Agents, unlike generative tools, create and perform multistep goals with minimal human supervision. The essential difference is found in its proactive nature. Rather than waiting for a specific, step-by-step command, agentic systems take a high-level objective and independently create and execute a plan to achieve that goal. This triggers a continuous, iterative workflow that is much like a cognitive loop. The typical agentic process involves six key steps, as described by Nvidia:
Our 2025 national survey of over 650 respondents across 49 states and Puerto Rico reveals both encouraging trends and important challenges. While AI adoption and optimism are growing, concerns about cheating, privacy, and the need for training persist.
Despite these challenges, I’m inspired by the resilience and adaptability of educators. You are the true game-changers in your students’ growth, and we’re honored to support this vital work.
This report reflects both where we are today and where we’re headed with AI. More importantly, it reflects your experiences, insights, and leadership in shaping the future of education.
This groundbreaking collaboration represents a transformative step forward in education technology and will begin with, but is not limited to, an effort between Instructure and OpenAI to enhance the Canvas experience by embedding OpenAI’s next-generation AI technology into the platform.
IgniteAI announced earlier today, establishes Instructure’s future-ready, open ecosystem with agentic support as the AI landscape continues to evolve. This partnership with OpenAI exemplifies this bold vision for AI in education. Instructure’s strategic approach to AI emphasizes the enhancement of connections within an educational ecosystem comprising over 1,100 edtech partners and leading LLM providers.
“We’re committed to delivering next-generation LMS technologies designed with an open ecosystem that empowers educators and learners to adapt and thrive in a rapidly changing world,” said Steve Daly, CEO of Instructure. “This collaboration with OpenAI showcases our ambitious vision: creating a future-ready ecosystem that fosters meaningful learning and achievement at every stage of education. This is a significant step forward for the education community as we continuously amplify the learning experience and improve student outcomes.”
Faculty Latest Targets of Big Tech’s AI-ification of Higher Ed— from insidehighered.com by Kathryn Palmer A new partnership between OpenAI and Instructure will embed generative AI in Canvas. It may make grading easier, but faculty are skeptical it will enhance teaching and learning.
The two companies, which have not disclosed the value of the deal, are also working together to embed large language models into Canvas through a feature called IgniteAI. It will work with an institution’s existing enterprise subscription to LLMs such as Anthropic’s Claude or OpenAI’s ChatGPT, allowing instructors to create custom LLM-enabled assignments. They’ll be able to tell the model how to interact with students—and even evaluate those interactions—and what it should look for to assess student learning. According to Instructure, any student information submitted through Canvas will remain private and won’t be shared with OpenAI.
… Faculty Unsurprised, Skeptical
Few faculty were surprised by the Canvas-OpenAI partnership announcement, though many are reserving judgment until they see how the first year of using it works in practice.
AI and Higher Ed: An Impending Collapse — from insidehighered.com by Robert Niebuhr; via George Siemens; I also think George’s excerpt (see below) gets right to the point. Universities’ rush to embrace AI will lead to an untenable outcome, Robert Niebuhr writes.
Herein lies the trap. If students learn how to use AI to complete assignments and faculty use AI to design courses, assignments, and grade student work, then what is the value of higher education? How long until people dismiss the degree as an absurdly overpriced piece of paper? How long until that trickles down and influences our economic and cultural output? Simply put, can we afford a scenario where students pretend to learn and we pretend to teach them?
This next report doesn’t look too good for traditional institutions of higher education either:
For the first time in modern history, a bachelor’s degree is no longer a reliable path to professional employment. Recent graduates face rising unemployment and widespread underemployment as structural—not cyclical—forces reshape entry?level work. This new report identifies four interlocking drivers: an AI?powered “Expertise Upheaval” eliminating many junior tasks, a post?pandemic shift to lean staffing and risk?averse hiring, AI acting as an accelerant to these changes, and a growing graduate glut. As a result, young degree holders are uniquely seeing their prospects deteriorate – even as the rest of the economy remain robust. Read the full report to explore the data behind these trends.
What is Study Mode?
Study Mode is OpenAI’s take on a smarter study partner – a version of the ChatGPT experience designed to guide users through problems with Socratic prompts, scaffolded reasoning, and adaptive feedback (instead of just handing over the answer).
Built with input from learning scientists, pedagogy experts, and educators, it was also shaped by direct feedback from college students. While Study Mode is designed with college students in mind, it’s meant for anyone who wants a more learning-focused, hands-on experience across a wide range of subjects and skill levels.
Who can access it? And how?
Starting July 29, Study Mode is available to users on Free, Plus, Pro, and Team plans. It will roll out to ChatGPT Edu users in the coming weeks.
ChatGPT became your tutor— from theneurondaily.com by Grant Harvey PLUS: NotebookLM has video now & GPT 4o-level AI runs on laptop
Here’s how it works: instead of asking “What’s 2+2?” and getting “4,” study mode asks questions like “What do you think happens when you add these numbers?” and “Can you walk me through your thinking?” It’s like having a patient tutor who won’t let you off the hook that easily.
The key features include:
Socratic questioning: It guides you with hints and follow-up questions rather than direct answers.
Scaffolded responses: Information broken into digestible chunks that build on each other.
Personalized support: Adjusts difficulty based on your skill level and previous conversations.
Knowledge checks: Built-in quizzes and feedback to make sure concepts actually stick.
Toggle flexibility: Switch study mode on and off mid-conversation depending on your goals.
Try study mode yourself by selecting “Study and learn” from tools in ChatGPT and asking a question.
Introducing study mode— from openai.com A new way to learn in ChatGPT that offers step by step guidance instead of quick answers.
[On 7/29/25, we introduced] study mode in ChatGPT—a learning experience that helps you work through problems step by step instead of just getting an answer. Starting today, it’s available to logged in users on Free, Plus, Pro, Team, with availability in ChatGPT Edu coming in the next few weeks.
ChatGPT is becoming one of the most widely used learning tools in the world. Students turn to it to work through challenging homework problems, prepare for exams, and explore new concepts. But its use in education has also raised an important question: how do we ensure it is used to support real learning, and doesn’t just offer solutions without helping students make sense of them?
We’ve built study mode to help answer this question. When students engage with study mode, they’re met with guiding questions that calibrate responses to their objective and skill level to help them build deeper understanding. Study mode is designed to be engaging and interactive, and to help students learn something—not just finish something.
We need a coherent approach grounded in understanding how the technology works, where it is going and what it will be used for.
From DSC: I almost feel like Meghan should right the words “this week” or “this month” after the above sentence. Whew! Things are moving fast.
For example, we’re now starting to see more agents hitting the scene — software that can DO things. But that can open up a can of worms too.
Students know the ground has shifted — and that the world outside the university expects them to shift with it. A.I. will be part of their lives regardless of whether we approve. Few issues expose the campus cultural gap as starkly as this one.ce
From DSC: Universities and colleges have little choice but to integrate AI into their programs and offerings. There’s enough pressure on institutions of traditional higher education to prove their worth/value. Students and their families want solid ROI’s. Students know that they are going to need AI-related skills (see the link immediately below for example), or they are going to be left out of the competitive job search process.
In Episode 5 of The Neuron Podcast, Corey Noles and Grant Harvey tackle the education crisis head-on. We explore the viral UCLA “CheatGPT” controversy, MIT’s concerning brain study, and innovative solutions like Alpha School’s 2-hour learning model. Plus, we break down OpenAI’s new $10M teacher training initiative and share practical tips for using AI to enhance learning rather than shortcut it. Whether you’re a student, teacher, or parent, you’ll leave with actionable insights on the future of education.
For today’s chief learning officer, the days of just rolling out compliance training are long gone. In 2025, learning and development leaders are architects of innovation, crafting ecosystems that are agile, automated and AI-infused. This quarter’s Tech Check invites us to pause, assess and get strategic about where tech is taking us. Because the goal isn’t more tools—it’s smarter, more human learning systems that scale with the business.
Sections include:
The state of AI in L&D: Hype vs. reality
AI in design: From static content to dynamic experiences
AI in development: Redefining production workflows
NEW YORK – The AFT, alongside the United Federation of Teachers and lead partner Microsoft Corp., founding partner OpenAI, and Anthropic, announced the launch of the National Academy for AI Instruction today. The groundbreaking $23 million education initiative will provide access to free AI training and curriculum for all 1.8 million members of the AFT, starting with K-12 educators. It will be based at a state-of-the-art bricks-and-mortar Manhattan facility designed to transform how artificial intelligence is taught and integrated into classrooms across the United States.
The academy will help address the gap in structured, accessible AI training and provide a national model for AI-integrated curriculum and teaching that puts educators in the driver’s seat.
In an era when the college-going rate of high school graduates has dropped from an all-time high of 70 percent in 2016 to roughly 62 percent now, AI seems to be heightening the anxieties about the value of college.
According to the survey, two-thirds of parents say AI is impacting their view of the value of college. Thirty-seven percent of parents indicate they are now scrutinizing college’s “career-placement outcomes”; 36 percent say they are looking at a college’s “AI-skills curriculum,” while 35 percent respond that a “human-skills emphasis” is important to them.
This echoes what I increasingly hear from college leadership: Parents and students demand to see a difference between what they are getting from a college and what they could be “learning from AI.”
Culture matters here. Organizations that foster psychological safety—where experimentation is welcomed and mistakes are treated as learning—are making the most progress. When leaders model curiosity, share what they’re trying, and invite open dialogue, teams follow suit. Small tests become shared wins. Shared wins build momentum.
Career development must be part of this equation. As roles evolve, people will need pathways forward. Some will shift into new specialties. Others may leave familiar roles for entirely new ones. Making space for that evolution—through upskilling, mobility, and mentorship—shows your people that you’re not just investing in AI, you’re investing in them.
And above all, people need transparency. Teams don’t expect perfection. But they do need clarity. They need to understand what’s changing, why it matters, and how they’ll be supported through it. That kind of trust-building communication is the foundation for any successful change.
These shifts may play out differently across sectors—but the core leadership questions will likely be similar.
AI marks a turning point—not just for technology, but for how we prepare our people to lead through disruption and shape the future of learning.
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.
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.
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.
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.
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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.
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.
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.
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.
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.
The 2025 Global Skills Report— from coursera.org Discover in-demand skills and credentials trends across 100+ countries and six regions to deliver impactful industry-aligned learning programs.
GenAI adoption fuels global skill demands
In 2023, early adopters flocked to GenAI, with approximately one person per minute enrolling in a GenAI course on Coursera —a rate that rose to eight per minute in 2024. Since then, GenAI has continued to see exceptional growth, with global enrollment in GenAI courses surging 195% year-over-year—maintaining its position as one of the most rapidly growing skill domains on our platform. To date, Coursera has recorded over 8 million GenAI enrollments, with 12 learners per minute signing up for GenAI content in 2025 across our catalog of nearly 700 GenAI courses.
Driving this surge, 94% of employers say they’re likely to hire candidates with GenAI credentials, while 75% prefer hiring less-experienced candidates with GenAI skills over more experienced ones without these capabilities.8 Demand for roles such as AI and Machine Learning Specialists is projected to grow by up to 40% in the next four years.9 Mastering AI fundamentals—from prompt engineering to large language model (LLM) applications—is essential to remaining competitive in today’s rapidly evolving economy.
Countries leading our new AI Maturity Index— which highlights regions best equipped to harness AI innovation and translate skills into real-world applications—include global frontrunners such as Singapore, Switzerland, and the United States.
Insights in action
Businesses
Integrate role-specific GenAI modules into employee development programs, enabling teams to leverage AI for efficiency and innovation.
Governments
Scale GenAI literacy initiatives—especially in emerging economies—to address talent shortages and foster human-machine capabilities needed to future-proof digital jobs.
Higher education
Embed credit-eligible GenAI learning into curricula, ensuring graduates enter the workforce job-ready.
Learners
Focus on GenAI courses offering real-world projects (e.g., prompt engineering) that help build skills for in-demand roles.
Five Essential Skills Kids Need (More than Coding)
I’m not saying we shouldn’t teach kids to code. It’s a useful skill. But these are the five true foundations that will serve them regardless of how technology evolves.
Day of AI Australia hosted a panel discussion on 20 May, 2025. Hosted by Dr Sebastian Sequoiah-Grayson (Senior Lecturer in the School of Computer Science and Engineering, UNSW Sydney) with panel members Katie Ford (Industry Executive – Higher Education at Microsoft), Tamara Templeton (Primary School Teacher, Townsville), Sarina Wilson (Teaching and Learning Coordinator – Emerging Technology at NSW Department of Education) and Professor Didar Zowghi (Senior Principal Research Scientist at CSIRO’s Data61).
As many students face criticism and punishment for using artificial intelligence tools like ChatGPT for assignments, new reporting shows that many instructors are increasingly using those same programs.
Our next challenge is to self-analyze and develop meaningful benchmarks for AI use across contexts. This research exhibit aims to take the first major step in that direction.
With the right approach, a transcript becomes something else:
A window into student decision-making
A record of how understanding evolves
A conversation that can be interpreted and assessed
An opportunity to evaluate content understanding
This week, I’m excited to share something that brings that idea into practice.
Over time, I imagine a future where annotated transcripts are collected and curated. Schools and universities could draw from a shared library of real examples—not polished templates, but genuine conversations that show process, reflection, and revision. These transcripts would live not as static samples but as evolving benchmarks.
This Field Guide is the first move in that direction.
Call it the ultimate proving ground. Collaborating with teammates in the modern workplace requires fast, fluid thinking. Providing insights quickly, while juggling webcams and office messaging channels, is a startlingly good test, and enterprise AI is about to pass it — just in time to provide assistance to busy knowledge workers.
To support enterprises in boosting productivity with AI teammates, NVIDIA today introduced a new NVIDIA Enterprise AI Factory validated design at COMPUTEX. IT teams deploying and scaling AI agents can use the design to build accelerated infrastructure and easily integrate with platforms and tools from NVIDIA software partners.
NVIDIA also unveiled new NVIDIA AI Blueprints to aid developers building smart AI teammates. Using the new blueprints, developers can enhance employee productivity through adaptive avatars that understand natural communication and have direct access to enterprise data.
“AI is now infrastructure, and this infrastructure, just like the internet, just like electricity, needs factories,” Huang said. “These factories are essentially what we build today.”
“They’re not data centers of the past,” Huang added. “These AI data centers, if you will, are improperly described. They are, in fact, AI factories. You apply energy to it, and it produces something incredibly valuable, and these things are called tokens.”
More’s coming, Huang said, describing the growing power of AI to reason and perceive. That leads us to agentic AI — AI able to understand, think and act. Beyond that is physical AI — AI that understands the world. The phase after that, he said, is general robotics.
May 19 (Reuters) – Dell Technologies (DELL.N), opens new tab on Monday unveiled new servers powered by Nvidia’s (NVDA.O), opens new tab Blackwell Ultra chips, aiming to capitalize on the booming demand for artificial intelligence systems.
The servers, available in both air-cooled and liquid-cooled variations, support up to 192 Nvidia Blackwell Ultra chips but can be customized to include as many as 256 chips.
Nvidia (NVDA) rolled into this year’s Computex Taipei tech expo on Monday with several announcements, ranging from the development of humanoid robots to the opening up of its high-powered NVLink technology, which allows companies to build semi-custom AI servers with Nvidia’s infrastructure.
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During the event on Monday, Nvidia revealed its Nvidia Isaac GR00T-Dreams, which the company says helps developers create enormous amounts of training data they can use to teach robots how to perform different behaviors and adapt to new environments.
