Higher education is in a period of massive transformation and uncertainty. Not only are current events impacting how institutions operate, but technological advancement—particularly in AI and virtual reality—are reshaping how students engage with content, how cognition is understood, and how learning itself is documented and valued.
Our newly released 2025 EDUCAUSE Horizon Report | Teaching and Learning Edition captures the spirit of this transformation and how you can respond with confidence through the lens of emerging trends, key technologies and practices, and scenario-based foresight.
DC: THIS could unfortunately be the ROI companies will get from large investments in #AI — reduced headcount/employees/contract workers. https://t.co/zEWlqCSWzI
Duolingo will “gradually stop using contractors to do work that AI can handle,” according to an all-hands email sent by cofounder and CEO Luis von Ahn announcing that the company will be “AI-first.” The email was posted on Duolingo’s LinkedIn account.
According to von Ahn, being “AI-first” means the company will “need to rethink much of how we work” and that “making minor tweaks to systems designed for humans won’t get us there.” As part of the shift, the company will roll out “a few constructive constraints,” including the changes to how it works with contractors, looking for AI use in hiring and in performance reviews, and that “headcount will only be given if a team cannot automate more of their work.”
Something strange, and potentially alarming, is happening to the job market for young, educated workers.
According to the New York Federal Reserve, labor conditions for recent college graduates have “deteriorated noticeably” in the past few months, and the unemployment rate now stands at an unusually high 5.8 percent. Even newly minted M.B.A.s from elite programs are struggling to find work. Meanwhile, law-school applications are surging—an ominous echo of when young people used graduate school to bunker down during the great financial crisis.
What’s going on? I see three plausible explanations, and each might be a little bit true.
The new workplace trend is not employee friendly. Artificial intelligence and automation technologies are advancing at blazing speed. A growing number of companies are using AI to streamline operations, cut costs, and boost productivity. Consequently, human workers are facing facing layoffs, replaced by AI. Like it or not, companies need to make tough decisions, including layoffs to remain competitive.
Corporations including Klarna, UPS, Duolingo, Intuit and Cisco are replacing laid-off workers with AI and automation. While these technologies enhance productivity, they raise serious concerns about future job security. For many workers, there is a big concern over whether or not their jobs will be impacted.
Key takeaway: Career navigation has remained largely unchanged for decades, relying on personal networks and static job boards. The advent of AI is changing this, offering personalised career pathways, better job matching, democratised job application support, democratised access to career advice/coaching, and tailored skill development to help you get to where you need to be.Hundreds of millions of people start new jobs every year, this transformation opens up a multi-billion dollar opportunity for innovation in the global career navigation market.
…
A.4 How will AI disrupt this segment? Personalised recommendations: AI can consume a vast amount of information (skills, education, career history, even youtube history, and x/twitter feeds), standardise this data at scale, and then use data models to match candidate characteristics to relevant careers and jobs. In theory, solutions could then go layers deeper, helping you position yourself for those future roles. Currently based in Amsterdam, and working in Strategy at Uber and want to work in a Product role in the future? Here are X,Y,Z specific things YOU can do in your role today to align yourself perfectly. E.g. find opportunities to manage cross functional projects in your current remit, reach out to Joe Bloggs also at Uber in Amsterdam who did Strategy and moved to Product, etc.
No matter the school, no matter the location, when I deliver an AI workshop to a group of teachers, there are always at least a few colleagues thinking (and sometimes voicing), “Do I really need to use AI?”
Nearly three years after ChatGPT 3.5 landed in our lives and disrupted workflows in ways we’re still unpacking, most schools are swiftly catching up. Training sessions, like the ones I lead, are springing up everywhere, with principals and administrators trying to answer the same questions: Which tools should we use? How do we use them responsibly? How do we design learning in this new landscape?
But here’s what surprises me most: despite all the advances in AI technology, the questions and concerns from teachers remain strikingly consistent.
…
In this article, I want to pull back the curtain on those conversations. These concerns aren’t signs of reluctance – they reflect sincere feelings. And they deserve thoughtful, honest answers.
This week, in advance of major announcements from us and other vendors, I give you a good overview of the AI Agent market, and discuss the new role of AI governance platforms, AI agent development tools, AI agent vendors, and how AI agents will actually manifest and redefine what we call an “application.”
I discuss ServiceNow, Microsoft, SAP, Workday, Paradox, Maki People, and other vendors. My goal today is to “demystify” this space and explain the market, the trends, and why and how your IT department is going to be building a lot of the agents you need. And prepare for our announcements next week!
DeepSeek has quietly launched Prover V2, an open-source model built to solve math problems using Lean 4 assistant, which ensures every step of a proof is rigorously verified.
What’s impressive about it?
Massive scale: Based on DeepSeek-V3 with 671B parameters using a mixture-of-experts (MoE) architecture, which activates only parts of the model at a time to reduce compute costs.
Theorem solving: Uses long context windows (32K+ tokens) to generate detailed, step-by-step formal proofs for a wide range of math problems — from basic algebra to advanced calculus theorems.
Research grade: Assists mathematicians in testing new theorems automatically and helps students understand formal logic by generating both Lean 4 code and readable explanations.
New benchmark: Introduces ProverBench, a new 325-question benchmark set featuring problems from recent AIME exams and curated academic sources to evaluate mathematical reasoning.
The need for deep student engagement became clear at Dartmouth Geisel School of Medicine when a potential academic-integrity issue revealed gaps in its initial approach to artificial intelligence use in the classroom, leading to significant revisions to ensure equitable learning and assessment.
From George Siemens “SAIL: Transmutation, Assessment, Robots e-newsletter on 5/2/25
All indications are that AI, even if it stops advancing, has the capacity to dramatically change knowledge work. Knowing things matters less than being able to navigate and make sense of complex environments. Put another way, sensemaking, meaningmaking, and wayfinding (with their yet to be defined subelements) will be the foundation for being knowledgeable going forward.
That will require being able to personalize learning to each individual learner so that who they are (not what our content is) forms the pedagogical entry point to learning.(DSC: And I would add WHAT THEY WANT to ACHIEVE.)LLMs are particularly good and transmutation. Want to explain AI to a farmer? A sentence or two in a system prompt achieves that. Know that a learner has ADHD? A few small prompt changes and it’s reflected in the way the LLM engages with learning. Talk like a pirate. Speak in the language of Shakespeare. Language changes. All a matter of a small meta comment send to the LLM. I’m convinced that this capability to change, transmute, information will become a central part of how LLMS and AI are adopted in education.
… Speaking of Duolingo– it took them 12 years to develop 100 courses. In the last year, they developed an additional 148. AI is an accelerant with an impact in education that is hard to overstate. “Instead of taking years to build a single course with humans the company now builds a base course and uses AI to quickly customize it for dozens of different languages.”
FutureHouse is launching our platform, bringing the first publicly available superintelligent scientific agents to scientists everywhere via a web interface and API. Try it out for free at https://platform.futurehouse.org.
Over the course of the next 10 years, AI-powered institutions will rise in the rankings. US News & World Report will factor a college’s AI capabilities into its calculations. Accrediting agencies will assess the degree of AI integration into pedagogy, research, and student life. Corporations will want to partner with universities that have demonstrated AI prowess. In short, we will see the emergence of the AI haves and have-nots.
…
What’s happening in higher education today has a name: creative destruction. The economist Joseph Schumpeter coined the term in 1942 to describe how innovation can transform industries. That typically happens when an industry has both a dysfunctional cost structure and a declining value proposition. Both are true of higher education.
…
Out of the gate, professors will work with technologists to get AI up to speed on specific disciplines and pedagogy. For example, AI could be “fed” course material on Greek history or finance and then, guided by human professors as they sort through the material, help AI understand the structure of the discipline, and then develop lectures, videos, supporting documentation, and assessments.
…
In the near future, if a student misses class, they will be able watch a recording that an AI bot captured. Or the AI bot will find a similar lecture from another professor at another accredited university. If you need tutoring, an AI bot will be ready to help any time, day or night. Similarly, if you are going on a trip and wish to take an exam on the plane, a student will be able to log on and complete the AI-designed and administered exam. Students will no longer be bound by a rigid class schedule. Instead, they will set the schedule that works for them.
Early and mid-career professors who hope to survive will need to adapt and learn how to work with AI. They will need to immerse themselves in research on AI and pedagogy and understand its effect on the classroom.
From DSC: I had a very difficult time deciding which excerpts to include. There were so many more excerpts for us to think about with this solid article. While I don’t agree with several things in it, EVERY professor, president, dean, and administrator working within higher education today needs to read this article and seriously consider what Scott Latham is saying.
Change is already here, but according to Scott, we haven’t seen anything yet. I agree with him and, as a futurist, one has to consider the potential scenarios that Scott lays out for AI’s creative destruction of what higher education may look like. Scott asserts that some significant and upcoming impacts will be experienced by faculty members, doctoral students, and graduate/teaching assistants (and Teaching & Learning Centers and IT Departments, I would add). But he doesn’t stop there. He brings in presidents, deans, and other members of the leadership teams out there.
There are a few places where Scott and I differ.
The foremost one is the importance of the human element — i.e., the human faculty member and students’ learning preferences. I think many (most?) students and lifelong learners will want to learn from a human being. IBM abandoned their 5-year, $100M ed push last year and one of the key conclusions was that people want to learn from — and with — other people:
To be sure, AI can do sophisticated things such as generating quizzes from a class reading and editing student writing. But the idea that a machine or a chatbot can actually teach as a human can, he said, represents “a profound misunderstanding of what AI is actually capable of.”
Nitta, who still holds deep respect for the Watson lab, admits, “We missed something important. At the heart of education, at the heart of any learning, is engagement. And that’s kind of the Holy Grail.”
— Satya Nitta, a longtime computer researcher at
IBM’s Watson Research Center in Yorktown Heights, NY .
By the way, it isn’t easy for me to write this. As I wanted AI and other related technologies to be able to do just what IBM was hoping that it would be able to do.
Also, I would use the term learning preferences where Scott uses the term learning styles.
Scott also mentions:
“In addition, faculty members will need to become technologists as much as scholars. They will need to train AI in how to help them build lectures, assessments, and fine-tune their classroom materials. Further training will be needed when AI first delivers a course.”
It has been my experience from working with faculty members for over 20 years that not all faculty members want to become technologists. They may not have the time, interest, and/or aptitude to become one (and vice versa for technologists who likely won’t become faculty members).
That all said, Scott relays many things that I have reflected upon and relayed for years now via this Learning Ecosystems blog and also via The Learning from the Living [AI-Based Class] Room vision — the use of AI to offer personalized and job-relevant learning, the rising costs of higher education, the development of new learning-related offerings and credentials at far less expensive prices, the need to provide new business models and emerging technologies that are devoted more to lifelong learning, plus several other things.
So this article is definitely worth your time to read, especially if you are working in higher education or are considering a career therein!
Google Public Sector and the University of Michigan’s Ross School of Business have launched an advanced Virtual Teaching Assistant pilot program aimed at improving personalized learning and enlightening educators on artificial intelligence in the classroom.
The AI technology, aided by Google’s Gemini chatbot, provides students with all-hours access to support and self-directed learning. The Virtual TA represents the next generation of educational chatbots, serving as a sophisticated AI learning assistant that instructors can use to modify their specific lessons and teaching styles.
The Virtual TA facilitates self-paced learning for students, provides on-demand explanations of complex course concepts, guides them through problem-solving, and acts as a practice partner. It’s designed to foster critical thinking by never giving away answers, ensuring students actively work toward solutions.
The urgent task facing those of us who teach and advise students, whether they be degree program or certificate seeking, is to ensure that they are prepared to enter (or re-enter) the workplace with skills and knowledge that are relevant to 2025 and beyond. One of the first skills to cultivate is an understanding of what kinds of services this emerging technology can provide to enhance the worker’s productivity and value to the institution or corporation.
…
Given that short period of time, coupled with the need to cover the scheduled information in the syllabus, I recommend that we consider merging AI use into authentic assignments and assessments, supplementary modules, and other resources to prepare for AI.
Learning Design in the Era of Agentic AI— from drphilippahardman.substack.com by Dr Philippa Hardman Aka, how to design online async learning experiences that learners can’t afford to delegate to AI agents
The point I put forward was that the problem is not AI’s ability to complete online async courses, but that online async courses courses deliver so little value to our learners that they delegate their completion to AI.
The harsh reality is that this is not an AI problem — it is a learning design problem.
However, this realisation presents us with an opportunity which we overall seem keen to embrace. Rather than seeking out ways to block AI agents, we seem largely to agree that we should use this as a moment to reimagine online async learning itself.
While fears of AI replacing educators swirl in the public consciousness, a cohort of pioneering institutions is demonstrating a far more nuanced reality. These eight universities and schools aren’t just experimenting with AI, they’re fundamentally reshaping their educational ecosystems. From personalized learning in K-12 to advanced research in higher education, these institutions are leveraging Google’s AI to empower students, enhance teaching, and streamline operations.
Essential AI tools for better work — from wondertools.substack.com by Jeremy Caplan My favorite tactics for making the most of AI — a podcast conversation
AI tools I consistently rely on (areas covered mentioned below)
Research and analysis
Communication efficiency
Multimedia creation
AI tactics that work surprisingly well
1. Reverse interviews Instead of just querying AI, have it interview you. Get the AI to interview you, rather than interviewing it. Give it a little context and what you’re focusing on and what you’re interested in, and then you ask it to interview you to elicit your own insights.”
This approach helps extract knowledge from yourself, not just from the AI. Sometimes we need that guide to pull ideas out of ourselves.
2025 College Hopes & Worries Survey Report — from princetonreview.com We surveyed 9,317 college applicants and parents about their dream schools and their biggest college admission and financial aid challenges. .
Employers and opportunity seekers are excited about the possibilities of a skills-based ecosystem, but this new process for codifying a person’s experiences and abilities into skills requires one significant, and missing, piece: Trust.Employers need to trust that the credentials they receive from opportunity seekers are valid representations of their skills. Jobseekers need to trust that their digital credentials are safe, accurate, and will lead to employment and advancement.
Our hypothesis
We posit that the trust needed for the validation of skills to be brought into a meaningful reality is established through a network of skills validation methods and opportunities. We also recognize that the routes through which an individual can demonstrate skills are as varied as the individuals themselves. Therefore, in order to equitably create a skills-based employment ecosystem, the routes by which skills are validated must be held together with common standards and language, but flexible enough to accommodate a multitude of validation practices.
The California State University system has partnered with OpenAI to launch the largest deployment of AI in higher education to date.
The CSU system, which serves nearly 500,000 students across 23 campuses, has announced plans to integrate ChatGPT Edu, an education-focused version of OpenAI’s chatbot, into its curriculum and operations. The rollout, which includes tens of thousands of faculty and staff, represents the most significant AI deployment within a single educational institution globally.
We’re still in the early stages of AI adoption in education, and it is critical that the entire ecosystem—education systems, technologists, educators, and governments—work together to ensure that all students globally have access to AI and develop the skills to use it responsibly
Leah Belsky, VP and general manager of education at OpenAI.
As you read through these use cases, you’ll notice that each one addresses multiple tasks from our list above.
1. Researching a topic for a lesson
2. Creating Tasks For Practice
3. Creating Sample Answers
4. Generating Ideas
5. Designing Lesson Plans
6. Creating Tests
7. Using AI in Virtual Classrooms
8. Creating Images
9. Creating worksheets
10. Correcting and Feedback
The number of colleges that close each year is poised to significantly increase as schools contend with a slowdown in prospective students.
That’s the finding of a new working paper published by the Federal Reserve Bank of Philadelphia, where researchers created predictive models of schools’ financial distress using metrics like enrollment and staffing patterns, sources of revenue and liquidity data. They overlayed those models with simulations to estimate the likely increase of future closures.
Excerpt from the working paper:
We document a high degree of missing data among colleges that eventually close and show that this is a key impediment to identifying at risk institutions. We then show that modern machine learning techniques, combined with richer data, are far more effective at predicting college closures than linear probability models, and considerably more effective than existing accountability metrics. Our preferred model, which combines an off-the-shelf machine learning algorithm with the richest set of explanatory variables, can significantly improve predictive accuracy even for institutions with complete data, but is particularly helpful for predicting instances of financial distress for institutions with spotty data.
From DSC: Questions that come to my mind here include:
Shouldn’t the public — especially those relevant parents and students — be made more aware of these types of papers and reports? .
How would any of us like finishing up 1-3 years of school and then being told that our colleges or universities were closing, effective immediately? (This has happened many times already.) and with the demographic cliff starting to hit higher education, this will happen even more now. . Adding insult to injury…when we transfer to different institutions, we’re told that many of our prior credits don’t transfer — thus adding a significant amount to the overall cost of obtaining our degrees. .
Would we not be absolutely furious to discover such communications from our prior — and new — colleges and universities? .
Will all of these types of closures move more people tothis vision here?
Relevant excerpts from Ray Schroeder’s recent articles out at insidehighered.com:
A number of factors are converging to create a huge storm. Generative AI advances, massive federal policy shifts, broad societal and economic changes, and the demographic cliff combine to create uncertainty today and change tomorrow.
The anticipated enrollment cliff, reductions in federal and state funding, increased inflation, and dwindling public support for tuition increases will combine to put even greater pressure on university budgets.
On the positive side of things, the completion rates have been getting better:
National college completion rate ticks up to 61.1% — from highereddive.com by Natalie Schwartz Those who started at two-year public colleges helped drive the overall increase in students completing a credential.
Dive Brief:
Completion rates ticked up to 61.1% for students who entered college in fall 2018, a 0.5 percentage-point increase compared to the previous cohort, according to data released Wednesday by the National Student Clearinghouse Research Center.
The increase marks the highest six-year completion rate since 2007 when the clearinghouse began tracking the data. The growth was driven by fewer students stopping out of college, as well as completion gains among students who started at public two-year colleges.
“Higher completion rates are welcome news for colleges and universities still struggling to regain enrollment levels from before the pandemic,” Doug Shapiro, the research center’s executive director, said in a statement dated Wednesday.
The stakes are huge, because the concern is that maybe the social contract between students and professors is kind of breaking down. Do students believe that all this college lecturing is worth hearing? Or, will this moment force a change in the way college teaching is done?
The Edtech Insiders Generative AI Map — from edtechinsiders.substack.com by Ben Kornell, Alex Sarlin, Sarah Morin, and Laurence Holt A market map and database featuring 60+ use cases for GenAI in education and 300+ GenAI powered education tools.
Used thoughtfully, ChatGPT can be a powerful tool to help students develop skills of rigorous thinking and clear writing, assisting them in thinking through ideas, mastering complex concepts, and getting feedback on drafts.
There are also ways to use ChatGPT that are counterproductive to learning—like generating an essay instead of writing it oneself, which deprives students of the opportunity to practice, improve their skills, and grapple with the material.
For students committed to becoming better writers and thinkers, here are some ways to use ChatGPT to engage more deeply with the learning process.
The Big Idea: As employers increasingly seek out applicants with AI skills, community colleges are well-positioned to train up the workforce. Partnerships with tech companies, like the AI Incubator Network, are helping some colleges get the resources and funding they need to overhaul programs and create new AI-focused ones.
Along these lines also see:
Practical AI Training — from the-job.beehiiv.com by Paul Fain Community colleges get help from Big Tech to prepare students for applied AI roles at smaller companies.
Miami Dade and other two-year colleges try to be nimble by offering training for AI-related jobs while focusing on local employers. Also, Intel’s business struggles while the two-year sector wonders if Republicans will cut funds for semiconductor production.
In this conversation, Josh Bersin discusses the evolving landscape of AI platforms, particularly focusing on Microsoft’s positioning and the challenges of creating a universal AI agent. He delves into the complexities of government efficiency, emphasizing the institutional challenges faced in re-engineering government operations.
The conversation also highlights the automation of work tasks and the need for businesses to decompose job functions for better efficiency.
Bersin stresses the importance of expertise in HR, advocating for a shift towards full stack professionals who possess a broad understanding of various HR functions.
Finally, he addresses the impending disruption in Learning and Development (L&D) due to AI advancements, predicting a significant transformation in how L&D professionals will manage knowledge and skills.
Doing the Best You Can With the Time You Have — by Jay Schauer These strategies can help overwhelmed teachers prioritize tasks and find a balance between perfectionism and efficiency. .
How to Support Teachers’ Emotional Health — by Hedreich Nichols Emotional well-being plays a major role in teachers’ job satisfaction, and it’s essential that they have effective resources for support.
Teachers cannot be expected to teach SEL effectively without first being intentional about their own emotional health. If we want educators to guide students through emotional regulation, they must have the time, space, and support to do that work themselves. This goes beyond surface-level wellness initiatives—teachers need opportunities to reflect on their emotional triggers, manage their own stresses, and receive genuine support from their schools. Only when teachers are empowered to process their own emotional challenges can they truly foster a healthy social and emotional environment for their students.
In Praise of the Humble Document Camera — by Emily Rankin Revisiting a simple edtech tool can help you introduce rigor and engage students more deeply in their lessons.
4 Ways to Use a Document Camera in Your Classroom— by Emily Rankin If a document camera is gathering dust in a classroom, its lack of impact is probably linked to the user, not what the gadget is capable of. Case in point, I wasn’t using mine regularly because I didn’t know the value it could add to my teaching and learning. Here are some of the practices I now know are possible:
One factor to consider is the subject. In math, students need opportunities to work on rich tasks and solve problems in ways that make sense to them. However, that doesn’t mean direct instruction is totally absent from math time. The questions below can guide you in deciding whether to use direct instruction, when it would be appropriate, and who else in the classroom you might involve.
Increasing Talk Time in World Language Classes— by Kate Good Teachers can experiment with a variety of strategies to build and assess students’ ability to converse in the target language.
To capitalize on my students’ (seemingly inexhaustible) desire to chat, I work to increase student talk time in our Spanish immersion classes. I use several strategies to build and assess students’ oral language.
This Article explores an innovative approach to assessment in legal education: an AI-assisted quiz system implemented in an AI & the Practice of Law course. The system employs a Socratic method-inspired chatbot to engage students in substantive conversations about course materials, providing a novel method for evaluating student learning and engagement. The Article examines the structure and implementation of this system, including its grading methodology and rubric, and discusses its benefits and challenges. Key advantages of the AI-assisted quiz system include enhanced student engagement with course materials, practical experience in AI interaction for future legal practice, immediate feedback and assessment, and alignment with the Socratic method tradition in law schools. The system also presents challenges, particularly in ensuring fairness and consistency in AI-generated questions, maintaining academic integrity, and balancing AI assistance with human oversight in grading.
The Article further explores the pedagogical implications of this innovation, including a shift from memorization to conceptual understanding, the encouragement of critical thinking through AI interaction, and the preparation of students for AI-integrated legal practice. It also considers future directions for this technology, such as integration with other law school courses, potential for longitudinal assessment of student progress, and implications for bar exam preparation and continuing legal education. Ultimately, this Article argues that AI-assisted assessment systems can revolutionize legal education by providing more frequent, targeted, and effective evaluation of student learning. While challenges remain, the benefits of such systems align closely with the evolving needs of the legal profession. The Article concludes with a call for further research and broader implementation of AI-assisted assessment in law schools to fully understand its impact and potential in preparing the next generation of legal professionals for an AI-integrated legal landscape.
Keywords: Legal Education, Artificial Intelligence, Assessment, Socratic Method, Chatbot, Law School Innovation, Educational Technology, Legal Pedagogy, AI-Assisted Learning, Legal Technology, Student Engagement, Formative Assessment, Critical Thinking, Legal Practice, Educational Assessment, Law School Curriculum, Bar Exam Preparation, Continuing Legal Education, Legal Ethics, Educational Analytics
Genie AI, a London-based legal tech startup, was founded in 2017 by Rafie Faruq and Nitish Mutha. The company has been at the forefront of revolutionizing the legal industry by leveraging artificial intelligence to automate and enhance legal document drafting and review processes. The recent funding round, led by Google Ventures and Khosla Ventures, marks a significant milestone in Genie AI’s growth trajectory.
Law firms are adopting generative artificial intelligence tools at a higher rate than in-house legal departments, but both report similar levels of concerns about data security and ethical implications, according to a report on legal tech usage released Wednesday.
Legal tech company Appara surveyed 443 legal professionals in Canada across law firms and in-house legal departments over the summer, including lawyers, paralegals, legal assistants, law clerks, conveyancers, and notaries.
Twenty-five percent of respondents who worked at law firms said they’ve already invested in generative AI tools, with 24 percent reporting they plan to invest within the following year. In contrast, only 15 percent of respondents who work in-house have invested in these tools, with 26 percent planning investments in the future.
The end of courts?— from jordanfurlong.substack.com by Jordan Furlong Civil justice systems aren’t serving the public interest. It’s time to break new ground and chart paths towards fast and fair dispute resolution that will meet people’s actual needs.
We need to start simple. System design can get extraordinarily complex very quickly, and complexity is our enemy at this stage. Tom O’Leary nicely inverted Deming’s axiom with a question of his own: “We want the system to work for [this group]. What would need to happen for that to be true?”
If we wanted civil justice systems to work for the ordinary people who enter them seeking solutions to their problems — as opposed to the professionals who administer and make a living off those systems — what would those systems look like? What would be their features? I can think of at least three:
New Era ADR CEO Rich Lee makes a return appearance to Technically Legal to talk about the company’s cutting-edge platform revolutionizing dispute resolution. Rich first came on the podcast in 2021 right as the company launched. Rich discusses the company’s mission to provide a faster, more efficient, and cost-effective alternative to traditional litigation and arbitration, the company’s growth and what he has learned from a few years in.
Key takeaways:
New Era ADR offers a unique platform for resolving disputes in under 100 days, significantly faster than traditional methods.
The platform leverages technology to streamline processes, reduce costs, and enhance accessibility for all parties involved.
New Era ADR boasts a diverse pool of experienced and qualified neutrals, ensuring fair and impartial resolutions.
The company’s commitment to innovation is evident in its use of data and technology to drive efficiency and transparency.
Emphasizing the use of AI, VR, and simulation games, the methods in this article enhance the evaluation of durable skills, making them more accessible and practical for real-world applications.
The integration of educational frameworks and workplace initiatives highlights the importance of partnerships in developing reliable systems for assessing transferable skills.
The core problem, witnesses at the hearing said, is that teacher-preparation programs treat all teachers—and, by extension, students—the same, asking teachers to be “everything to everybody.”
“The current model of teaching where one teacher works individually with a group of learners in a classroom—or a small box inside of a larger box that we call school—promotes unrealistic expectations by assuming individual teachers working in isolation can meet the needs of all students,” said Greg Mendez, the principal of Skyline High School in Mesa, Ariz.
From DSC: I’ve long thought teacher education programs could and should evolve (that’s why I have a “student teacher/teacher education” category on this blog). For example, they should inform their future teachers about the science of learning and how to leverage edtech/emerging technologies into their teaching methods.
But regardless of what happens in our teacher prep programs, the issues about the current PreK-12 learning ecosystem remain — and THOSE things are what we need to address. Or we will continue to see teachers leave the profession.
Are we straight-jacketing our teachers and administrators by having them give so many standardized tests and then having to teach to those tests? (We should require our legislators to teach in a classroom before they can draft any kind of legislation.)
Do teachers have the joy they used to have? The flexibility they used to have? Do students?
Do students have choice and voice?
etc.
Also, I highlighted the above excerpt because we can’t expect a teacher to do it all. They can’t be everything to everybody. It’s a recipe for burnout and depression. There are too many agendas coming at them.
We need to empower our current teachers and listen very carefully to the changes that they recommend. We should also listen very carefully to what our STUDENTS are recommending as well!
People started discussing what they could do with Notebook LM after Google launched the audio overview, where you can listen to 2 hosts talking in-depth about the documents you upload. Here are what it can do:
Summarization: Automatically generate summaries of uploaded documents, highlighting key topics and suggesting relevant questions.
Question Answering: Users can ask NotebookLM questions about their uploaded documents, and answers will be provided based on the information contained within them.
Idea Generation: NotebookLM can assist with brainstorming and developing new ideas.
Source Grounding: A big plus against AI chatbot hallucination, NotebookLM allows users to ground the responses in specific documents they choose.
…plus several other items
The posting also lists several ideas to try with NotebookLM such as:
Idea 2: Study Companion
Upload all your course materials and ask NotebookLM to turn them into Question-and-Answer format, a glossary, or a study guide.
Get a breakdown of the course materials to understand them better.
“Google’s AI note-taking app NotebookLM can now explain complex topics to you out loud”
With more immersive text-to-video and audio products soon available and the rise of apps like Suno AI, how we “experience” Generative AI is also changing from a chatbot of 2 years ago, to a more multi-modal educational journey. The AI tools on the research and curation side are also starting to reflect these advancements.
1. Upload a variety of sources for NotebookLM to use.
You can use …
websites
PDF files
links to websites
any text you’ve copied
Google Docs and Slides
even Markdown
You can’t link it to YouTube videos, but you can copy/paste the transcript (and maybe type a little context about the YouTube video before pasting the transcript).
2. Ask it to create resources. 3. Create an audio summary. 4. Chat with your sources.
5. Save (almost) everything.
I finally tried out Google’s newly-announced NotebookLM generative AI application. It provides a set of LLM-powered tools to summarize documents. I fed it my dissertation, and am surprised at how useful the output would be.
The most impressive tool creates a podcast episode, complete with dual hosts in conversation about the document. First – these are AI-generated hosts. Synthetic voices, speaking for synthetic hosts. And holy moly is it effective. Second – although I’d initially thought the conversational summary would be a dumb gimmick, it is surprisingly powerful.
4 Tips for Designing AI-Resistant Assessments — from techlearning.com by Steve Baule and Erin Carter As AI continues to evolve, instructors must modify their approach by designing meaningful, rigorous assessments.
As instructors work through revising assessments to be resistant to generation by AI tools with little student input, they should consider the following principles:
Incorporate personal experiences and local content into assignments
Ask students for multi-modal deliverables
Assess the developmental benchmarks for assignments and transition assignments further up Bloom’s Taxonomy
He added that he wants to avoid a global “AI divide” and that Google is creating a $120 million Global AI Opportunity Fund through which it will “make AI education and training available in communities around the world” in partnership with local nonprofits and NGOs.
Google on Thursday announced new updates to its AI note-taking and research assistant, NotebookLM, allowing users to get summaries of YouTube videos and audio files and even create sharable AI-generated audio discussions…
