From DSC: In looking atMyNextChapter.ai— THIS TYPE OF FUNCTIONALITY of an AI-based chatbot talking to you re: good fits for a future job — is the kind of thing that could work well in this type of vision/learning platform. The AI asks you relevant career-oriented questions, comes up with some potential job fits, and then gives you resources about how to gain those skills, who to talk with, organizations to join, next steps to get your foot in the door somewhere, etc.
The next gen learning platform would provide links to online-based courses, blogs, peoples’ names on LinkedIn, courses from L&D organizations or from institutions of higher education or from other entities/places to obtain those skills (similar to the ” Action Plan” below from MyNextChapter.ai).
Teach business students to write like executives— from timeshighereducation.com by José Ignacio Sordo Galarza Many business students struggle to communicate with impact. Teach them to pitch ideas on a single page to build clarity, confidence and work-ready communication skills
Many undergraduate business students transition into the workforce equipped with communication habits that, while effective in academic settings, prove ineffective in professional environments. At university, students are trained to write for professors, not executives. This becomes problematic in the workplace where lengthy reports and academic jargon often obscure rather than clarify intent. Employers seek ideas they can absorb in seconds. This is where the one-pager – a single-page, high-impact document that helps students develop clarity of thought, concise expression and strategic communication – proves effective.
Also from Times Higher Education, see:
Is the dissertation dead? If so, what are the alternatives? — from timeshighereducation.com by Rushana Khusainova, Sarah Sholl, & Patrick Harte Dissertation alternatives, such as capstone projects and applied group-based projects, could better prepare graduates for their future careers. Discover what these might look like
The traditional dissertation, a longstanding pillar of higher education, is facing increasing scrutiny. Concerns about its relevance to contemporary career paths, limitations in fostering practical skills and the changing nature of knowledge production in the GenAI age have fuelled discussions about its continued efficacy. So, is the dissertation dead?
The dissertation is facing a number of challenges. It can be perceived as having little relevance to career aspirations in increasingly competitive job markets. According to The Future of Jobs Report 2025 by the World Economic Forum, employers demand and indeed prioritise skills such as collaborative problem-solving in diverse and complex contexts, which a dissertation might not demonstrate.
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.
Is graduate employability a core university priority? — from timeshighereducation.com by Katherine Emms and Andrea Laczik Universities, once judged primarily on the quality of their academic outcomes, are now also expected to prepare students for the workplace. Here’s how higher education is adapting to changing pressures
A clear, deliberate shift in priorities is under way. Embedding employability is central to an Edge Foundation report, carried out in collaboration with UCL’s Institute of Education, looking at how English universities are responding. In placing employability at the centre of their strategies – not just for professional courses but across all disciplines – the two universities that were analysed in this research show how they aim to prepare students for the labour market overall. Although the employability strategy is initialled by the universities’ senior leaders, the research showed that realising this employability strategy must be understood and executed by staff at all levels across departments. The complexity of offering insights into industry pathways and building relevant skills involves curricula development, student-centred teaching, careers support, partnership work and employer engagement.
Every student can benefit from an entrepreneurial mindset — from timeshighereducation.com by Nicolas Klotz To develop the next generation of entrepreneurs, universities need to nurture the right mindset in students of all disciplines. Follow these tips to embed entrepreneurial education
This shift demands a radical rethink of how we approach entrepreneurial mindset in higher education. Not as a specialism for a niche group of business students but as a core competency that every student, in every discipline, can benefit from.
At my university, we’ve spent the past several years re-engineering how we embed entrepreneurship into daily student life and learning.
What we’ve learned could help other institutions, especially smaller or resource-constrained ones, adapt to this new landscape.
The first step is recognising that entrepreneurship is not only about launching start-ups for profit. It’s about nurturing a mindset that values initiative, problem-solving, resilience and creative risk-taking. Employers increasingly want these traits, whether the student is applying for a traditional job or proposing their own venture.
Build foundations for university-industry partnerships in 90 days— from timeshighereducation.com by Raul Villamarin Rodriguez and Hemachandran K Graduate employability could be transformed through systematic integration of industry partnerships. This practical guide offers a framework for change in Indian universities
The most effective transformation strategy for Indian universities lies in systematic industry integration that moves beyond superficial partnerships and towards deep curriculum collaboration. Rather than hoping market alignment will occur naturally, institutions must reverse-engineer academic programmes from verified industry needs.
Our six-month implementation at Woxsen University demonstrates this framework’s practical effectiveness, achieving more than 130 industry partnerships, 100 per cent faculty participation in transformation training, and 75 per cent of students receiving industry-validated credentials with significantly improved employment outcomes.
How Do You Teach Computer Science in the A.I. Era? — from nytimes.com by Steve Lohr; with thanks to Ryan Craig for this resource Universities across the country are scrambling to understand the implications of generative A.I.’s transformation of technology.
The future of computer science education, Dr. Maher said, is likely to focus less on coding and more on computational thinking and A.I. literacy. Computational thinking involves breaking down problems into smaller tasks, developing step-by-step solutions and using data to reach evidence-based conclusions.
A.I. literacy is an understanding — at varying depths for students at different levels — of how A.I. works, how to use it responsibly and how it is affecting society. Nurturing informed skepticism, she said, should be a goal.
At Carnegie Mellon, as faculty members prepare for their gathering, Dr. Cortina said his own view was that the coursework should include instruction in the traditional basics of computing and A.I. principles, followed by plenty of hands-on experience designing software using the new tools.
“We think that’s where it’s going,” he said. “But do we need a more profound change in the curriculum?”
The unexpected benefits of academic blogging | THE Campus Learn, Share, Connect
DC: I wish more faculty and staff members would blog and share their knowledge with the public. I greatly appreciate those staff and faculty members that DO blog! https://t.co/CHfHbxICcL
Get yourself unstuck: overthinking is boring and perfectionism is a trap — from timeshighereducation.com by David Thompson The work looks flawless, the student seems fine. But underneath, perfectionism is doing damage. David Thompson unpacks what educators can do to help high-performing students navigate the pressure to succeed and move from stuck to started
That’s why I encourage imperfection, messiness and play and build these ideas into how I teach.
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These moments don’t come from big breakthroughs. They come from removing pressure and replacing it with permission.
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.
Access trusted insights on:
[NEW] Countries leading AI innovation in our AI Maturity Index
Skill proficiency rankings for 100+ countries in business, data, and technology
How people are building essential skills with micro-credentials
Enrollment trends in cybersecurity, critical thinking, and human skills
Women’s learning trends in GenAI, STEM, and Professional Certificates
Staying competitive now depends on fast, effective training and upskilling—not just for business owners themselves, but for their teams, new and existing employees alike. AI agents are poised to change the corporate training landscape, helping businesses close skills gaps created by rapid technological change.
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Traditional corporate training programs, which lean on passive content, often fall short of their goals. Companies like Uplimit are rolling out educational AI agents that promise significantly higher completion rates (upwards of 90 percent) and better results. It boils down to engagement—the active learning, with role playing and personalized feedback, is more stimulating than merely watching a video and completing a quiz. Agents can provide 24/7 assistance, responding to questions as soon as they pop up. What’s more, education and training with agents can be highly personalized.
Agents can train a higher volume of employees in the same amount of time. Employees will gain skills more efficiently, giving them more time to apply what they’ve learned—and likely boosting engagement in the process. They’ll be better prepared to stay competitive.
