8 AI Chatbots for Teachers: A Simple Guide and Quick Tips – Class Tech Tips
— from classtechtips.com by Dr. Monica Burns
On 07/01/2025,
in accessibility / UDL, Artificial Intelligence/Agents/LLMs/and Related, communications, education, education reform, education technology, emerging technologies, engagement / engaging students, experimentation, human-computer interaction (HCI), ideas, innovation, instructional design, intelligent systems, intelligent tutoring, K-12 related, leadership, learning, learning agents, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, pedagogy, personalized/customized learning, platforms, strategy, teachers, tools, vendors,
by Daniel Christian
Transform Public Speaking with Yoodli: Your AI Coach — from rdene915.com by Paula Johnson
Yoodli is an AI tool designed to help users improve their public speaking skills. It analyzes your speech in real-time or after a recording and gives you feedback on things like:
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- Filler words (“um,” “like,” “you know”)
- Pacing (Are you sprinting or sedating your audience?)
- Word choice and sentence complexity
- Eye contact and body language (with video)
- And yes, even your “uhhh” to actual word ratio
Yoodli gives you a transcript and a confidence score, plus suggestions that range from helpful to brutally honest. It’s basically Simon Cowell with AI ethics and a smiley face interface.
[What’s] going on with AI and education? — from theneuron.ai by Grant Harvey
With students and teachers alike using AI, schools are facing an “assessment crisis” where the line between tool and cheating has blurred, forcing a shift away from a broken knowledge economy toward a new focus on building human judgment through strategic struggle.
What to do about it: The future belongs to the “judgment economy,” where knowledge is commoditized but taste, agency, and learning velocity become the new human moats. Use the “Struggle-First” principle: wrestle with problems for 20-30 minutes before turning to AI, then use AI as a sparring partner (not a ghostwriter) to deepen understanding. The goal isn’t to avoid AI, but to strategically choose when to embrace “desirable difficulties” that build genuine expertise versus when to leverage AI for efficiency.
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The Alpha-School Program in brief:
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- Students complete core academics in just 2 hours using AI tutors, freeing up 4+ hours for life skills, passion projects, and real-world experiences.
- The school claims students learn at least 2x faster than their peers in traditional school.
- The top 20% of students show 6.5x growth. Classes score in the top 1-2% nationally across the board.
- Claims are based on NWEA’s Measures of Academic Progress (MAP) assessments… with data only available to the school. Hmm…
Austen Allred shared a story about the school, which put it on our radar.
Featured Report: Teaching for Tomorrow: Unlocking Six Weeks a Year With AI — from gallup.com
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In the latest installment of Gallup and the Walton Family Foundation’s research on education, K-12 teachers reveal how AI tools are transforming their workloads, instructional quality and classroom optimism. The report finds that 60% of teachers used an AI tool during the 2024–25 school year. Weekly AI users report reclaiming nearly six hours per week — equivalent to six weeks per year — which they reinvest in more personalized instruction, deeper student feedback and better parent communication.
Despite this emerging “AI dividend,” adoption is uneven: 40% of teachers aren’t using AI at all, and only 19% report their school has a formal AI policy. Teachers with access to policies and support save significantly more time.
Educators also say AI improves their work. Most report higher-quality lesson plans, assessments and student feedback. And teachers who regularly use AI are more optimistic about its benefits for student engagement and accessibility — mirroring themes from the Voices of Gen Z: How American Youth View and Use Artificial Intelligence report, which found students hesitant but curious about AI’s classroom role. As AI tools grow more embedded in education, both teachers and students will need the training and support to use them effectively.
Also see:
- 2-Hour Learning
- What if children could crush academics in 2 hours, 2x faster?
- What if children could get back their most valuable resource, which is time?
- What if children could pursue the things they want during their afternoons and develop life skills?
Amira Learning: Teaching With The AI-Powered Reading Tool — from techlearning.com by Erik Ofgang
Amira Learning is a research-backed AI reading tutor that incorporates the science of reading into its features.
What Is Amira Learning?
Amira Learning’s system is built upon research led by Jack Mostow, a professor at Carnegie Mellon who helped pioneer AI literacy education. Amira uses Claude AI to power its AI features, but these features are different than many other AI tools on the market. Instead of focusing on chat and generative response, Amira’s key feature is its advanced speech recognition and natural language processing capabilities, which allow the app to “hear” when a student is struggling and tailor suggestions to that student’s particular mistakes.
Though it’s not meant to replace a teacher, Amira provides real-time feedback and also helps teachers pinpoint where a student is struggling. For these reasons, Amira Learning is a favorite of education scientists and advocates for science of reading-based literacy instruction. The tool currently is used by more than 4 million students worldwide and across the U.S.
On 06/25/2025,
in 21st century, Artificial Intelligence/Agents/LLMs/and Related, business, emerging technologies, entrepreneurship, experimentation, innovation, law schools, Legal operations, legal reform, legal technologies, legislatures / government / legal, platforms, productivity / tips and tricks, tools, trends, United States, vendors, vision/possibilities,
by Daniel Christian
Agentic AI use cases in the legal industry — from legal.thomsonreuters.com
What legal professionals need to know now with the rise of agentic AI
While GenAI can create documents or answer questions, agentic AI takes intelligence a step further by planning how to get multi-step work done, including tasks such as consuming information, applying logic, crafting arguments, and then completing them.? This leaves legal teams more time for nuanced decision-making, creative strategy, and relationship-building with clients—work that machines can’t do.
The AI Legal Landscape in 2025: Beyond the Hype — from akerman.com by Melissa C. Koch
What we’re witnessing is a profession in transition where specific tasks are being augmented or automated while new skills and roles emerge.
The data tells an interesting story: approximately 79% of law firms have integrated AI tools into their workflows, yet only a fraction have truly transformed their operations. Most implementations focus on pattern recognition tasks such as document review, legal research, contract analysis. These implementations aren’t replacing lawyers; they’re redirecting attention to higher-value work.
This technological shift doesn’t happen in isolation. It’s occurring amid client pressure for efficiency, competition from alternative providers, and the expectations of a new generation of lawyers who have never known a world without AI assistance.
LexisNexis and Harvey team up to revolutionize legal research with artificial intelligence — from abajournal.com by Danielle Braff
Lawyers using the Harvey artificial intelligence platform will soon be able to tap into LexisNexis’ vast legal research capabilities.
Thanks to a new partnership announced Wednesday, Harvey users will be able to ask legal questions and receive fast, citation-backed answers powered by LexisNexis case law, statutes and Shepard’s Citations, streamlining everything from basic research to complex motions. According to a press release, generated responses to user queries will be grounded in LexisNexis’ proprietary knowledge graphs and citation tools—making them more trustworthy for use in court or client work.
10 Legal Tech Companies to Know — from builtin.com
These companies are using AI, automation and analytics to transform how legal work gets done.
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Four months after a $3B valuation, Harvey AI grows to $5B — from techcrunch.com by Marina Temkin
Harvey AI, a startup that provides automation for legal work, has raised $300 million in Series E funding at a $5 billion valuation, the company told Fortune. The round was co-led by Kleiner Perkins and Coatue, with participation from existing investors, including Conviction, Elad Gil, OpenAI Startup Fund, and Sequoia.
The billable time revolution — from jordanfurlong.substack.com by Jordan Furlong
Gen AI will bring an end to the era when lawyers’ value hinged on performing billable work. Grab the coming opportunity to re-prioritize your daily activities and redefine your professional purpose.
Because of Generative AI, lawyers will perform fewer “billable” tasks in future; but why is that a bad thing? Why not devote that incoming “freed-up” time to operating, upgrading, and flourishing your law practice? Because this is what you do now: You run a legal business. You deliver good outcomes, good experiences, and good relationships to clients. Humans do some of the work and machines do some of the work and the distinction that matters is not billable/non-billable, it’s which type of work is best suited to which type of performer.
On 06/23/2025,
in 21st century, 24x7x365 access, adjunct faculty, adult learning, Artificial Intelligence/Agents/LLMs/and Related, bots, change, cloud-based computing / apps / other cloud-related, CMS/LMS, colleges, communities of practice, community colleges, content development, aggregation, repositories, corporate / business world, corporate universities / corporate training, digital learning, digital video, education, education technology, emerging technologies, engagement / engaging students, experimentation, face-to-face, faculty/staff, game-changing environment, Google, heutagogy, higher education, homeschooling/homeschoolers, human-computer interaction (HCI), ideas, innovation, instructional design, intelligent systems, intelligent tutoring, IT in HE, K-12 related, law schools, learning, learning agents, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, liberal arts, library / librarians, lifelong learning, memory, metacognition / metacognitive skills, multimedia, Natural Language Processing (NLP), new media literacies, online learning, online media, Open AI, personalized/customized learning, platforms, productivity / tips and tricks, professional development, student-related, teachers, teaching & learning, teaching online, technologies for your home, tools, traditional / tradition, training / L&D, trends, United States, universities, user experience (UX), vendors, vision/possibilities, writing,
by Daniel Christian
Thoughts on thinking — from dcurt.is by Dustin Curtis
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.
Using AI Right Now: A Quick Guide — from oneusefulthing.org by Ethan Mollick
Which AIs to use, and how to use them
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.
Also see:
Student Voice, Socratic AI, and the Art of Weaving a Quote — from elmartinsen.substack.com by Eric Lars Martinsen
How a custom bot helps students turn source quotes into personal insight—and share it with others
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.
The Disappearance of the Unclear Question — from jeppestricker.substack.com Jeppe Klitgaard Stricker
New Piece for UNESCO Education Futures
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.
Leveraging GenAI to Transform a Traditional Instructional Video into Engaging Short Video Lectures — from er.educause.edu by Hua Zheng
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.
This AI Model Never Stops Learning — from link.wired.com by Will Knight
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.
On 06/20/2025,
in business, career development, change, changing business models, collaboration, corporate / business world, corporate universities / corporate training, Daniel S. Christian, education, education reform, experimentation, face-to-face, ideas, innovation, instructional design, K-12 related, leadership, learning, learning ecosystem, Learning Experience Design, love of learning, participation, passions, project-based learning, real-world assignments, skills, society, strategy, student-related, teachers, United States, youth,
by Daniel Christian
How Do You Build a Learner-Centered Ecosystem? — from gettingsmart.com by Bobbi Macdonald and Alin Bennett
Key Points
- It’s not just about redesigning public education—it’s about rethinking how, where and with whom learning happens. Communities across the United States are shaping learner-centered ecosystems and gathering insights along the way.
- What does it take to build a learner-centered ecosystem? A shared vision. Distributed leadership. Place-based experiences. Repurposed resources. And more. This piece unpacks 10 real-world insights from pilots in action.
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We believe the path forward is through the cultivation of learner-centered ecosystems — adaptive, networked structures that offer a transformed way of organizing, supporting, and credentialing community-wide learning. These ecosystems break down barriers between schools, communities, and industries, creating flexible, real-world learning experiences that tap into the full range of opportunities a community has to offer.
Last year, we announced our Learner-Centered Ecosystem Lab, a collaborative effort to create a community of practice consisting of twelve diverse sites across the country — from the streets of Brooklyn to the mountains of Ojai — that are demonstrating or piloting ecosystemic approaches. Since then, we’ve been gathering together, learning from one another, and facing the challenges and opportunities of trying to transform public education. And while there is still much more work to be done, we’ve begun to observe a deeper pattern language — one that aligns with our ten-point Ecosystem Readiness Framework, and one that, we hope, can help all communities start to think more practically and creatively about how to transform their own systems of learning.
So while it’s still early, we suspect that the way to establish a healthy learner-centered ecosystem is by paying close attention to the following ten conditions:
On 06/09/2025,
in Adobe, Artificial Intelligence/Agents/LLMs/and Related, colleges, communities of practice, community colleges, content development, aggregation, repositories, digital learning, disruption, education, education technology, emerging technologies, engagement / engaging students, entrepreneurship, experimentation, Facebook / Meta Platforms, faculty/staff, future, future of higher education, game-changing environment, global / globalization, Google, higher education, human-computer interaction (HCI), ideas, innovation, instructional design, intelligent systems, K-12 related, leadership, learning, learning ecosystem, Learning Experience Design, Microsoft, online media, Open AI, organizational change, pace of change, pedagogy, platforms, student-related, teachers, teaching & learning, teaching online, technologies for your home, the downsides of how people use tech, tools, trends, United States, universities, vendors, workplace, writing,
by Daniel Christian
Mary Meeker AI Trends Report: Mind-Boggling Numbers Paint AI’s Massive Growth Picture — from ndtvprofit.com
Numbers that prove AI as a tech is unlike any other the world has ever seen.
Here are some incredibly powerful numbers from Mary Meeker’s AI Trends report, which showcase how artificial intelligence as a tech is unlike any other the world has ever seen.
- AI took only three years to reach 50% user adoption in the US; mobile internet took six years, desktop internet took 12 years, while PCs took 20 years.
- ChatGPT reached 800 million users in 17 months and 100 million in only two months, vis-à-vis Netflix’s 100 million (10 years), Instagram (2.5 years) and TikTok (nine months).
- ChatGPT hit 365 billion annual searches in two years (2024) vs. Google’s 11 years (2009)—ChatGPT 5.5x faster than Google.
Above via Mary Meeker’s AI Trend-Analysis — from getsuperintel.com by Kim “Chubby” Isenberg
How AI’s rapid rise, efficiency race, and talent shifts are reshaping the future.
The TLDR
Mary Meeker’s new AI trends report highlights an explosive rise in global AI usage, surging model efficiency, and mounting pressure on infrastructure and talent. The shift is clear: AI is no longer experimental—it’s becoming foundational, and those who optimize for speed, scale, and specialization will lead the next wave of innovation.
Also see Meeker’s actual report at:
Trends – Artificial Intelligence — from bondcap.com by Mary Meeker / Jay Simons / Daegwon Chae / Alexander Krey
The Rundown: Meta aims to release tools that eliminate humans from the advertising process by 2026, according to a report from the WSJ — developing an AI that can create ads for Facebook and Instagram using just a product image and budget.
The details:
- Companies would submit product images and budgets, letting AI craft the text and visuals, select target audiences, and manage campaign placement.
- The system will be able to create personalized ads that can adapt in real-time, like a car spot featuring mountains vs. an urban street based on user location.
- The push would target smaller companies lacking dedicated marketing staff, promising professional-grade advertising without agency fees or skillset.
- Advertising is a core part of Mark Zuckerberg’s AI strategy and already accounts for 97% of Meta’s annual revenue.
Why it matters: We’re already seeing AI transform advertising through image, video, and text, but Zuck’s vision takes the process entirely out of human hands. With so much marketing flowing through FB and IG, a successful system would be a major disruptor — particularly for small brands that just want results without the hassle.
On 06/09/2025,
in 21st century, 24x7x365 access, 3D, A/V -- audio/visual, active learning, adjunct faculty, adult learning, architecture, Augmented Reality (AR), bots, business, career development, change, colleges, communities of practice, community colleges, computer science, content development, aggregation, repositories, corporate / business world, corporate universities / corporate training, creativity, curriculum, data related items, design, digital learning, education technology, emerging technologies, engagement / engaging students, experimentation, faculty/staff, future, future of higher education, game-changing environment, generational differences, global / globalization, Google, health, higher education, homeschooling/homeschoolers, human-computer interaction (HCI), ideas, innovation, instructional design, intelligent systems, K-12 related, law schools, leadership, learning, learning agents, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, learning hubs & pods & microschooling, legal technologies, legislatures / government / legal, library / librarians, lifelong learning, online learning, online media, online tutoring, pedagogy, personalized/customized learning, platforms, podcasting, policy, productivity / tips and tricks, professional development, skills, society, student-related, teachers, teaching & learning, teaching online, technologies for your home, tools, traditional / tradition, training / L&D, trends, United States, universities, vendors, vision/possibilities, visualizing information, workplace, XR - extended reality,
by Daniel Christian
The AI-enhanced learning ecosystem: A case study in collaborative innovation — from chieflearningofficer.com by Kevin Jennings
How artificial intelligence can serve as a tool and collaborative partner in reimagining content development and management.
Learning and development professionals face unprecedented challenges in today’s rapidly evolving business landscape. According to LinkedIn’s 2025 Workplace Learning Report, 67 percent of L&D professionals report being “maxed out” on capacity, while 66 percent have experienced budget reductions in the past year.
Despite these constraints, 87 percent agree their organizations need to develop employees faster to keep pace with business demands. These statistics paint a clear picture of the pressure L&D teams face: do more, with less, faster.
This article explores how one L&D leader’s strategic partnership with artificial intelligence transformed these persistent challenges into opportunities, creating a responsive learning ecosystem that addresses the modern demands of rapid product evolution and diverse audience needs. With 71 percent of L&D professionals now identifying AI as a high or very high priority for their learning strategy, this case study demonstrates how AI can serve not merely as a tool but as a collaborative partner in reimagining content development and management.
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How we use GenAI and AR to improve students’ design skills — from timeshighereducation.com by Antonio Juarez, Lesly Pliego and Jordi Rábago who are professors of architecture at Monterrey Institute of Technology in Mexico; Tomas Pachajoa is a professor of architecture at the El Bosque University in Colombia; & Carlos Hinrichsen and Marietta Castro are educators at San Sebastián University in Chile.
Guidance on using generative AI and augmented reality to enhance student creativity, spatial awareness and interdisciplinary collaboration
Blend traditional skills development with AI use
For subjects that require students to develop drawing and modelling skills, have students create initial design sketches or models manually to ensure they practise these skills. Then, introduce GenAI tools such as Midjourney, Leonardo AI and ChatGPT to help students explore new ideas based on their original concepts. Using AI at this stage broadens their creative horizons and introduces innovative perspectives, which are crucial in a rapidly evolving creative industry.
Provide step-by-step tutorials, including both written guides and video demonstrations, to illustrate how initial sketches can be effectively translated into AI-generated concepts. Offer example prompts to demonstrate diverse design possibilities and help students build confidence using GenAI.
Integrating generative AI and AR consistently enhanced student engagement, creativity and spatial understanding on our course.
How Texas is Preparing Higher Education for AI — from the74million.org by Kate McGee
TX colleges are thinking about how to prepare students for a changing workforce and an already overburdened faculty for new challenges in classrooms.
“It doesn’t matter if you enter the health industry, banking, oil and gas, or national security enterprises like we have here in San Antonio,” Eighmy told The Texas Tribune. “Everybody’s asking for competency around AI.”
It’s one of the reasons the public university, which serves 34,000 students, announced earlier this year that it is creating a new college dedicated to AI, cyber security, computing and data science. The new college, which is still in the planning phase, would be one of the first of its kind in the country. UTSA wants to launch the new college by fall 2025.
But many state higher education leaders are thinking beyond that. As AI becomes a part of everyday life in new, unpredictable ways, universities across Texas and the country are also starting to consider how to ensure faculty are keeping up with the new technology and students are ready to use it when they enter the workforce.
In the Room Where It Happens: Generative AI Policy Creation in Higher Education — from er.educause.edu by Esther Brandon, Lance Eaton, Dana Gavin, and Allison Papini
To develop a robust policy for generative artificial intelligence use in higher education, institutional leaders must first create “a room” where diverse perspectives are welcome and included in the process.
Q&A: Artificial Intelligence in Education and What Lies Ahead — from usnews.com by Sarah Wood
Research indicates that AI is becoming an essential skill to learn for students to succeed in the workplace.
Q: How do you expect to see AI embraced more in the future in college and the workplace?
I do believe it’s going to become a permanent fixture for multiple reasons. I think the national security imperative associated with AI as a result of competing against other nations is going to drive a lot of energy and support for AI education. We also see shifts across every field and discipline regarding the usage of AI beyond college. We see this in a broad array of fields, including health care and the field of law. I think it’s here to stay and I think that means we’re going to see AI literacy being taught at most colleges and universities, and more faculty leveraging AI to help improve the quality of their instruction. I feel like we’re just at the beginning of a transition. In fact, I often describe our current moment as the ‘Ask Jeeves’ phase of the growth of AI. There’s a lot of change still ahead of us. AI, for better or worse, it’s here to stay.
AI-Generated Podcasts Outperform Textbooks in Landmark Education Study — form linkedin.com by David Borish
A new study from Drexel University and Google has demonstrated that AI-generated educational podcasts can significantly enhance both student engagement and learning outcomes compared to traditional textbooks. The research, involving 180 college students across the United States, represents one of the first systematic investigations into how artificial intelligence can transform educational content delivery in real-time.
What can we do about generative AI in our teaching? — from linkedin.com by Kristina Peterson
So what can we do?
- Interrogate the Process: We can ask ourselves if we I built in enough checkpoints. Steps that can’t be faked. Things like quick writes, question floods, in-person feedback, revision logs.
- Reframe AI: We can let students use AI as a partner. We can show them how to prompt better, revise harder, and build from it rather than submit it. Show them the difference between using a tool and being used by one.
- Design Assignments for Curiosity, Not Compliance: Even the best of our assignments need to adapt. Mine needs more checkpoints, more reflective questions along the way, more explanation of why my students made the choices they did.
Teachers Are Not OK — from 404media.co by Jason Koebler
The response from teachers and university professors was overwhelming. In my entire career, I’ve rarely gotten so many email responses to a single article, and I have never gotten so many thoughtful and comprehensive responses.
One thing is clear: teachers are not OK.
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In addition, universities are contracting with companies like Microsoft, Adobe, and Google for digital services, and those companies are constantly pushing their AI tools. So a student might hear “don’t use generative AI” from a prof but then log on to the university’s Microsoft suite, which then suggests using Copilot to sum up readings or help draft writing. It’s inconsistent and confusing.
I am sick to my stomach as I write this because I’ve spent 20 years developing a pedagogy that’s about wrestling with big ideas through writing and discussion, and that whole project has been evaporated by for-profit corporations who built their systems on stolen work. It’s demoralizing.
On 06/04/2025,
in Asia, biology, dangers of the status quo, emerging technologies, engineering, entrepreneurship, ethics, experimentation, health, human-computer interaction (HCI), ideas, innovation, morals/values, society, technology (general), the downsides of how people use tech,
by Daniel Christian
Scientific breakthrough: artificial blood for all blood groups — from getsuperintel.com by Kim “Chubby” Isenberg
Japan’s universal artificial blood could revolutionize emergency medicine and global healthcare resilience.
They all show that we are on the threshold of a new era – one in which technological systems are no longer just tools, but independent players in medical, cognitive and infrastructural change.
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This paradigm shift means that AI will no longer be limited to static training data, but will learn through open exploration, similar to biological organisms. This is nothing less than the beginning of an era of autonomous cognition.
From DSC:
While there are some promising developments involving AI these days, we need to look at what the potential downsides might be of AI becoming independent players, don’t you think? Otherwise, what could possibly go wrong?
On 05/22/2025,
in 21st century, 24x7x365 access, adult learning, Artificial Intelligence/Agents/LLMs/and Related, career development, changing business models, colleges, community colleges, content development, aggregation, repositories, corporate / business world, corporate universities / corporate training, experimentation, future, game-changing environment, higher education, human-computer interaction (HCI), personalized/customized learning, platforms, productivity / tips and tricks, professional development, skills, tools, traditional / tradition, training / L&D, trends, United States, universities, workplace,
by Daniel Christian
Making AI Work: Leadership, Lab, and Crowd — from oneusefulthing.org by Ethan Mollick
A formula for AI in companies
How do we reconcile the first three points with the final one? The answer is that AI use that boosts individual performance does not naturally translate to improving organizational performance. To get organizational gains requires organizational innovation, rethinking incentives, processes, and even the nature of work. But the muscles for organizational innovation inside companies have atrophied. For decades, companies have outsourced this to consultants or enterprise software vendors who develop generalized approaches that address the issues of many companies at once. That won’t work here, at least for a while. Nobody has special information about how to best use AI at your company, or a playbook for how to integrate it into your organization.
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Galileo Learn™ – A Revolutionary Approach To Corporate Learning — from joshbersin.com
Today we are excited to launch Galileo Learn™, a revolutionary new platform for corporate learning and professional development.
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How do we leverage AI to revolutionize this model, doing away with the dated “publishing” model of training?
The answer is Galileo Learn, a radically new and different approach to corporate training and professional development.
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What Exactly is Galileo Learn™?
Galileo Learn is an AI-native learning platform which is tightly integrated into the Galileo agent. It takes content in any form (PDF, word, audio, video, SCORM courses, and more) and automatically (with your guidance) builds courses, assessments, learning programs, polls, exercises, simulations, and a variety of other instructional formats.
Designing an Ecosystem of Resources to Foster AI Literacy With Duri Long — from aialoe.org
Centering Public Understanding in AI Education
In a recent talk titled “Designing an Ecosystem of Resources to Foster AI Literacy,” Duri Long, Assistant Professor at Northwestern University, highlighted the growing need for accessible, engaging learning experiences that empower the public to make informed decisions about artificial intelligence. Long emphasized that as AI technologies increasingly influence everyday life, fostering public understanding is not just beneficial—it’s essential. Her work seeks to develop a framework for AI literacy across varying audiences, from middle school students to adult learners and journalists.
A Design-Driven, Multi-Context Approach
Drawing from design research, cognitive science, and the learning sciences, Long presented a range of educational tools aimed at demystifying AI. Her team has created hands-on museum exhibits, such as Data Bites, where learners build physical datasets to explore how computers learn. These interactive experiences, along with web-based tools and support resources, are part of a broader initiative to bridge AI knowledge gaps using the 4As framework: Ask, Adapt, Author, and Analyze. Central to her approach is the belief that familiar, tangible interactions and interfaces reduce intimidation and promote deeper engagement with complex AI concepts.