Duolingo’s new Video Call feature represents a leap forward in language practice for learners. This AI-powered tool allows Duolingo Max subscribers to engage in spontaneous, realistic conversations with Lily, one of Duolingo’s most popular characters. The technology behind Video Call is designed to simulate natural dialogue and provides a personalized, interactive practice environment. Even beginner learners can converse in a low-pressure environment because Video Call is designed to adapt to their skill level. By offering learners the opportunity to converse in real-time,Video Call builds the confidence needed to communicate effectively in real-world situations. Video Call is available for Duolingo Max subscribers learning English, Spanish, and French.
Ello, the AI reading companion that aims to support kids struggling to read, launched a new product on Monday that allows kids to participate in the story-creation process.
Called “Storytime,” the new AI-powered feature helps kids generate personalized stories by picking from a selection of settings, characters, and plots. For instance, a story about a hamster named Greg who performed in a talent show in outer space.
On Tuesday, Workera announced Sage, an AI agent you can talk with that’s designed to assess an employee’s skill level, goals, and needs. After taking some short tests, Workera claims Sage will accurately gauge how proficient someone is at a certain skill. Then, Sage can recommend the appropriate online courses through Coursera, Workday, or other learning platform partners.Through chatting with Sage, Workera is designed to meet employees where they are, testing their skills in writing, machine learning, or math, and giving them a path to improve.
Disengaged students. Sky-high absenteeism. A disconnect between the typical high school’s academic curriculum and post-graduation life.
These and related complaints about the American high school experience have been gathering steam for some time; the pandemic exacerbated them. State-level policymakers have taken note, and many are now trying to figure out how to give high school students access to a more relevant and engaging experience that prepares them for a future—whether it involves college or doesn’t.
…
After a slow start, the school’s internship program has grown exponentially. In 2019-20, just five students completed internships, mainly due to the logistical challenges the pandemic presented. This past year, it grew to over 180 participating seniors, with more than 200 community organizations agreeing to accept interns.
Even when students have access to high-quality dual-credit programs, they often do not get guidance about the academic and workplace requirements of particular fields until it’s too late, said Julie Lammers, the senior vice president of advocacy and corporate social responsibility for American Student Assistance, a national nonprofit focused on helping young people learn about college and careers.
“We need to start having career conversations with young people much earlier in their trajectory, at the time young people are still open to possibilities,” Lammers said. “If they don’t see themselves in science by 8th grade, STEM careers come off the table.”
Cost plays a big role in the decision to attend and stay in college. The Education Data Initiative finds that on average, students in 2024 racked up nearly$38,000 in debt to pursue a bachelor’s degree, with many expecting to take up to 20 years to pay it off.
What outcomes do we truly desire for young people? Many students feel that their current educational experiences do not prepare them adequately for real-world challenges. Supported by data on attendance, disengagement, and stress, it’s evident that a shift is needed. To move beyond outdated school-centered models, we must embrace a learner-centered paradigm that fosters flexibility, personalization, and authentic community engagement. Innovative approaches like multiage microschools and passion projects are transforming how students learn by fostering real-world skills, confidence, and community engagement.
These learner-centered models—ranging from personalized projects to collaborative problem-solving—provide actionable strategies to create environments where every student can thrive. Schools are moving away from one-size-fits-all systems and embracing approaches like flexible learning pathways, mentorship opportunities, and community-integrated learning. These strategies are not only closing the gap between education and the skills needed for the future but also reshaping public schools into dynamic hubs of innovation.
Key Points
Engaging parents, youth, teachers, principals, district leaders, community members, and industry experts in the co-design process ensures that education systems align with the aspirations and needs of the community.
Transitioning from a traditional school-centered model to a learner-centered approach is critical for preparing students with the skills needed to thrive in the 21st century.
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!
The XQ Institute shares this mindset as part of our mission to reimagine the high school learning experience so it’s more relevant and engaging for today’s learners, while better preparing them for the future.We see AI as a tool with transformative potential for educators and makers to leverage — but only if it’s developed and implemented with ethics, transparency and equity at the forefront. That’s why we’re building partnerships between educators and AI developers to ensure that products are shaped by the real needs and challenges of students, teachers and schools. Here’s how we believe all stakeholders can embrace the Department’s recommendations through ongoing collaborations with tech leaders, educators and students alike.
…lead me to the XQ Institute, and I very much like what I’m initially seeing! Here are some excerpts from their website:
Transforming high school isn’t easy, but it is possible. ? Educator @nwallacecxh from XQ’s @CrosstownHigh shares real-world strategies to make learning relevant and meaningful. Ready to see how it’s done? ? https://t.co/xD8hkP33TH
As we navigate the rapidly evolving landscape of artificial intelligence in education, a troubling trend has emerged. What began as cautious skepticism has calcified into rigid opposition. The discourse surrounding AI in classrooms has shifted from empirical critique to categorical rejection, creating a chasm between the potential of AI and its practical implementation in education.
This hardening of attitudes comes at a significant cost. While educators and policymakers debate, students find themselves caught in the crossfire. They lack safe, guided access to AI tools that are increasingly ubiquitous in the world beyond school walls. In the absence of formal instruction, many are teaching themselves to use these tools, often in less than productive ways. Others live in a state of constant anxiety, fearing accusations of AI reliance in their work. These are just a few symptoms of an overarching educational culture that has become resistant to change, even as the world around it transforms at an unprecedented pace.
Yet, as this calcification sets in, I find myself in a curious position: the more I thoughtfully integrate AI into my teaching practice, the more I witness its potential to enhance and transform education
The urgency to integrate AI competencies into education is about preparing students not just to adapt to inevitable changes but to lead the charge in shaping an AI-augmented world. It’s about equipping them to ask the right questions, innovate responsibly, and navigate the ethical quandaries that come with such power.
AI in education should augment and complement their aptitude and expertise, to personalize and optimize the learning experience, and to support lifelong learning and development. AI in education should be a national priority and a collaborative effort among all stakeholders, to ensure that AI is designed and deployed in an ethical, equitable, and inclusive way that respects the diversity and dignity of all learners and educators and that promotes the common good and social justice. AI in education should be about the production of AI, not just the consumption of AI, meaning that learners and educators should have the opportunity to learn about AI, to participate in its creation and evaluation, and to shape its impact and direction.
Legal budgets will get an AI-inspired makeover in 2025: survey— from legaldive.com by Justin Bachman Nearly every general counsel is budgeting to add generative AI tools to their departments – and they’re all expecting to realize efficiencies by doing so.
Dive Brief:
Nearly all general counsel say their budgets are up slightly after wrestling with widespread cuts last year. And most of them, 61%, say they expect slightly larger budgets next year as well, an average of 5% more, according to the 2025 In-House Legal Budgeting Report from Axiom and Wakefield Research. Technology was ranked as the top in-house investment priority for both 2024 and 2025 for larger companies.
Legal managers predict their companies will boost investment on technology and real estate/facilities in 2025, while reducing outlays for human resources and mergers and acquisition activity, according to the survey. This mix of changing priorities might disrupt legal budgets.
Among the planned legal tech spending, the top three areas for investment are virtual legal assistants/AI-powered chatbots (35%); e-billing and spend-management software (31%); and contract management platforms (30%).
Today we rolled out OpenAI o1-preview and o1-mini to all ChatGPT Plus/Team users & Tier 5 developers in the API.
o1 marks the start of a new era in AI, where models are trained to “think” before answering through a private chain of thought. The more time they take to think, the…
This is @Google‘s wow moment in AI.
Notebook LM can generate engaging podcasts on your uploaded material for FREE.
I tested it with uploading the latest issue of the Tensor, it generated a podcast for me within 2 mins.
“Who to follow in AI” in 2024? — from ai-supremacy.com by Michael Spencer Part III – #35-55 – I combed the internet, I found the best sources of AI insights, education and articles. LinkedIn | Newsletters | X | YouTube | Substack | Threads | Podcasts
This list features both some of the best Newsletters on AI and people who make LinkedIn posts about AI papers, advances and breakthroughs. In today’s article we’ll be meeting the first 19-34, in a list of 180+.
Newsletter Writers
YouTubers
Engineers
Researchers who write
Technologists who are Creators
AI Educators
AI Evangelists of various kinds
Futurism writers and authors
I have been sharing the list in reverse chronological order on LinkedIn here.
Inside Google’s 7-Year Mission to Give AI a Robot Body — from wired.com by Hans Peter Brondmo As the head of Alphabet’s AI-powered robotics moonshot, I came to believe many things. For one, robots can’t come soon enough. For another, they shouldn’t look like us.
Learning to Reason with LLMs — from openai.com We are introducing OpenAI o1, a new large language model trained with reinforcement learning to perform complex reasoning. o1 thinks before it answers—it can produce a long internal chain of thought before responding to the user.
As a preview of the upcoming Summit interview, here are Khan’s views on two critical questions, edited for space and clarity:
What are the enduring human work skills in a world with ever-advancing AI? Some people say students should study liberal arts. Others say deep domain expertise is the key to remaining professionally relevant. Others say you need to have the skills of a manager to be able to delegate to AI. What do you think are the skills or competencies that ensure continued relevance professionally, employability, etc.?
A lot of organizations are thinking about skills-based approaches to their talent. It involves questions like, ‘Does someone know how to do this thing or not?’ And what are the ways in which they can learn it and have some accredited way to know they actually have done it? That is one of the ways in which people use Khan Academy. Do you have a view of skills-based approaches within workplaces, and any thoughts on how AI tutors and training fit within that context?
As Americans live and work longer, many now find themselves needing to change jobs and careers several times within their lifetimes.
Now, Georgia Institute of Technology has created a new college to serve just these learners. Georgia Tech last week launched its College of Lifetime Learning, which will combine degree programs with non-degree programs, and seeks to educate 114K students by 2030. That would enable the university to double the current number of degrees granted and nondegree students served.
“What we’re hearing is that with the advancing pace of digitization taking place, changing demographics, people working longer, for example, higher ed needs to do something in addition to what it already has been doing” says Nelson Baker, interim dean of the new college.
Now a quarter way through the 21st century, higher education is again in need of a reboot. Post Covid, colleges are closing one per week. More than 40M U.S. learners have started college but never finished. Nearly two-thirds of those learners would complete their degree but can’t afford to. Student debt now sits at almost $2T. Americans are losing faith in higher education.
Enter the apprenticeship degree, where students can earn a debt-free, four-year degree entirely embedded within a full-time, paid job. In the U.K., with government tax incentives, the apprenticeship-to-degree model has surged in eight years from zero to 50K new enrollments, making progress toward an expected 20% of postsecondary starts within the decade. As I have previously written, I believe the apprenticeship degree is just what American higher education needs to meet the moment.
This week, as I kick off the 20th cohort of my AI-Learning Design bootcamp, I decided to do some analysis of the work habits of the hundreds of amazing AI-embracing instructional designers who I’ve worked with over the last year or so.
My goal was to answer the question: which AI tools do we use most in the instructional design process, and how do we use them?
Here’s where we are in September, 2024:
…
Developing Your Approach to Generative AI — from scholarlyteacher.com by Caitlin K. Kirby, Min Zhuang, Imari Cheyne Tetu, & Stephen Thomas (Michigan State University)
As generative AI becomes integrated into workplaces, scholarly work, and students’ workflows, we have the opportunity to take a broad view of the role of generative AI in higher education classrooms. Our guiding questions are meant to serve as a starting point to consider, from each educator’s initial reaction and preferences around generative AI, how their discipline, course design, and assessments may be impacted, and to have a broad view of the ethics of generative AI use.
AI technology tools hold remarkable promise for providing more accessible, equitable, and inclusive learning experiences for students with disabilities.
The Voice of the Online Learner report highlights the journey of online learners, and the vital role education plays in their personal and professional growth and development. This year’s report compiled responses from over 3,400 prospective, current, and recently graduated online learners.
Key findings from this year’s Voice of the Online Learner report include:
Decision Factors for Online Students: When evaluating online programs, the key decision for students is cost, with 86% saying it’s extremely or very important. After cost, 84% said accreditation is most important, 75% said program concentrations, followed by 68% of respondents who said it was the time it took to achieve a degree. 38% selected the lowest cost program they evaluated (up from 29% in 2023).
Perception of Online Programs: Students see online programs as equally valid or better at meeting their needs than on-campus degree programs. 83% of respondents prefer the flexibility of online programs over hybrid or on-campus options, while 90% feel online programs are comparable to or better than an on-campus degree. 83% (up from 71% last year) want no on campus requirement.
Degree ROI: 92% of students who graduated from online degree programs reported tangible benefits to their career, including 44% who received a salary increase.
Value of the Degree: Career outcomes continue to be very important for students pursuing their degree.86% felt their degrees were important in achieving their career goals, and 61% of online undergraduates are likely to enroll in additional online degree programs to stay competitive.
Importance of Local Programs: Attending a university or college in the state where the student lives and works is also an important decision factor, with 70% enrolled at a higher education institution in the state where they live and/or work. These students say that local proximity creates greater trust, and that they also want to ensure the programs meet local licensing or accreditation requirements, when relevant.
Demographics: The average age for online students enrolled in undergraduate programs is 36 years old, while the average age for students enrolled in graduate programs is 38 years old. Of the students enrolled in undergraduate programs, 40% are first-generation college students.
Upskilling is lifelong: 86% of graduated and currently enrolled students are likely to do another online program in the future to upskill.
Generative AI is a concern: Students want guidance on generative AI, but 75% reported they have received none. 40% of students think it will affect their career positively and 40% believe it will impact them negatively. Nearly half (48%) have used it to help them study.
AI can help educators focus more on human interaction and critical thinking by automating tasks that consume time but don’t require human empathy or creativity.
Encouraging students to use AI as a tool for learning and creativity can significantly boost their engagement and self-confidence, as seen in examples from student experiences shared in the discussion.
The speakers discuss various aspects of AI, including its potential to augment human intelligence and the need to focus on uniquely human competencies in the face of technological advancements. They also emphasize the significance of student agency, with examples of student-led initiatives and feedback sessions that reveal how young learners are already engaging with AI in innovative ways. The episode underscores the necessity for educators and administrators to stay informed and actively participate in the ongoing dialogue about AI to ensure its effective and equitable implementation in schools.
AI can be a powerful tool to break down language, interest, and accessibility barriers in the classroom, making learning more inclusive and engaging.
Incorporating AI tools in educational settings can help build essential skills that AI can’t replace, such as creativity and problem-solving, preparing students for future job markets.
86% of students globally are regularly using AI in their studies, with 54% of them using AI on a weekly basis, the recent Digital Education Council Global AI Student Survey found.
ChatGPT was found to be the most widely used AI tool, with 66% of students using it, and over 2 in 3 students reported using AI for information searching.
Despite their high rates of AI usage, 1 in 2 students do not feel AI ready. 58% reported that they do not feel that they had sufficient AI knowledge and skills, and 48% do not feel adequately prepared for an AI-enabled workplace.
The Post-AI Instructional Designer— from drphilippahardman.substack.com by Dr. Philippa Hardman How the ID role is changing, and what this means for your key skills, roles & responsibilities
Specifically, the study revealed that teachers who reported most productivity gains were those who used AI not just for creating outputs (like quizzes or worksheets) but also for seeking input on their ideas, decisions and strategies.
Those who engaged with AI as a thought partner throughout their workflow, using it to generate ideas, define problems, refine approaches, develop strategies and gain confidence in their decisions gained significantly more from their collaboration with AI than those who only delegated functional tasks to AI.
Leveraging Generative AI for Inclusive Excellence in Higher Education — from er.educause.edu by Lorna Gonzalez, Kristi O’Neil-Gonzalez, Megan Eberhardt-Alstot, Michael McGarry and Georgia Van Tyne Drawing from three lenses of inclusion, this article considers how to leverage generative AI as part of a constellation of mission-centered inclusive practices in higher education.
The hype and hesitation about generative artificial intelligence (AI) diffusion have led some colleges and universities to take a wait-and-see approach.Footnote1 However, AI integration does not need to be an either/or proposition where its use is either embraced or restricted or its adoption aimed at replacing or outright rejecting existing institutional functions and practices. Educators, educational leaders, and others considering academic applications for emerging technologies should consider ways in which generative AI can complement or augment mission-focused practices, such as those aimed at accessibility, diversity, equity, and inclusion. Drawing from three lenses of inclusion—accessibility, identity, and epistemology—this article offers practical suggestions and considerations that educators can deploy now. It also presents an imperative for higher education leaders to partner toward an infrastructure that enables inclusive practices in light of AI diffusion.
An example way to leverage AI:
How to Leverage AI for Identity Inclusion Educators can use the following strategies to intentionally design instructional content with identity inclusion in mind.
Provide a GPT or AI assistant with upcoming lesson content (e.g., lecture materials or assignment instructions) and ask it to provide feedback (e.g., troublesome vocabulary, difficult concepts, or complementary activities) from certain perspectives. Begin with a single perspective (e.g., first-time, first-year student), but layer in more to build complexity as you interact with the GPT output.
Gen AI’s next inflection point: From employee experimentation to organizational transformation — from mckinsey.com by Charlotte Relyea, Dana Maor, and Sandra Durth with Jan Bouly As many employees adopt generative AI at work, companies struggle to follow suit. To capture value from current momentum, businesses must transform their processes, structures, and approach to talent.
To harness employees’ enthusiasm and stay ahead, companies need a holistic approach to transforming how the whole organization works with gen AI; the technology alone won’t create value.
Our research shows that early adopters prioritize talent and the human side of gen AI more than other companies (Exhibit 3). Our survey shows that nearly two-thirds of them have a clear view of their talent gaps and a strategy to close them, compared with just 25 percent of the experimenters. Early adopters focus heavily on upskilling and reskilling as a critical part of their talent strategies, as hiring alone isn’t enough to close gaps and outsourcing can hinder strategic-skills development.Finally, 40 percent of early-adopter respondents say their organizations provide extensive support to encourage employee adoption, versus 9 percent of experimenter respondents.
Change blindness — from oneusefulthing.org by Ethan Mollick 21 months later
I don’t think anyone is completely certain about where AI is going, but we do know that things have changed very quickly, as the examples in this post have hopefully demonstrated. If this rate of change continues, the world will look very different in another 21 months. The only way to know is to live through it.
Over the subsequent weeks, I’ve made other adjustments, but that first one was the one I asked myself:
What are you doing?
Why are you doing it that way?
How could you change that workflow with AI?
Applying the AI to the workflow, then asking, “Is this what I was aiming for? How can I improve the prompt to get closer?”
Documenting what worked (or didn’t). Re-doing the work with AI to see what happened, and asking again, “Did this work?”
So, something that took me WEEKS of hard work, and in some cases I found impossible, was made easy. Like, instead of weeks, it takes 10 minutes. The hard part? Building the prompt to do what I want, fine-tuning it to get the result. But that doesn’t take as long now.
Using generative artificial intelligence (GenAI) tools such as ChatGPT, Gemini, or CoPilot as intelligent assistants in instructional design can significantly enhance the scalability of course development. GenAI can significantly improve the efficiency with which institutions develop content that is closely aligned with the curriculum and course objectives. As a result, institutions can more effectively meet the rising demand for flexible and high-quality education, preparing a new generation of future professionals equipped with the knowledge and skills to excel in their chosen fields.1 In this article, we illustrate the uses of AI in instructional design in terms of content creation, media development, and faculty support. We also provide some suggestions on the effective and ethical uses of AI in course design and development. Our perspectives are rooted in medical education, but the principles can be applied to any learning context.
… Table 1 summarizes a few low-hanging fruits in AI usage in course development. .
Table 1. Types of Use of GenAI in Course Development
Practical Use of AI
Use Scenarios and Examples
Inspiration
Exploring ideas for instructional strategies
Exploring ideas for assessment
Course mapping
Lesson or unit content planning
Supplementation
Text to audio
Transcription for audio
Alt text auto-generation
Design optimization (e.g., using Microsoft PPT Design)
10 Ways Artificial Intelligence Is Transforming Instructional Design — from er.educause.edu by Rob Gibson Artificial intelligence (AI) is providing instructors and course designers with an incredible array of new tools and techniques to improve the course design and development process. However, the intersection of AI and content creation is not new.
I have been telling my graduate instructional design students that AI technology is not likely to replace them any time soon because learning and instruction are still highly personalized and humanistic experiences. However, as these students embark on their careers, they will need to understand how to appropriately identify, select, and utilize AI when developing course content. Examples abound of how instructional designers are experimenting with AI to generate and align student learning outcomes with highly individualized course activities and assessments. Instructional designers are also using AI technology to create and continuously adapt the custom code and power scripts embedded into the learning management system to execute specific learning activities.Footnote1 Other useful examples include scripting and editing videos and podcasts.
Here are a few interesting examples of how AI is shaping and influencing instructional design. Some of the tools and resources can be used to satisfy a variety of course design activities, while others are very specific.
The world of a medieval stone cutter and a modern instructional designer (ID) may seem separated by a great distance, but I wager any ID who upon hearing the story I just shared would experience an uneasy sense of déjà vu. Take away the outward details, and the ID would recognize many elements of the situation: the days spent in projects that fail to realize the full potential of their craft, the painful awareness that greater things can be built, but are unlikely to occur due to a poverty of imagination and lack of vision among those empowered to make decisions.
Finally, there is the issue of resources. No stone cutter could ever hope to undertake a large-scale enterprise without a multitude of skilled collaborators and abundant materials. Similarly, instructional designers are often departments of one, working in scarcity environments, with limited ability to acquire resources for ambitious projects and — just as importantly — lacking the authority or political capital needed to launch significant initiatives. For these reasons, instructional design has long been a profession caught in an uncomfortable stasis, unable to grow, evolve and achieve its full potential.
That is until generative AI appeared on the scene. While the discourse around AI in education has been almost entirely about its impact on teaching and assessment, there has been a dearth of critical analysis regarding AI’s potential for impacting instructional design.
We are at a critical juncture for AI-augmented learning. We can either stagnate, missing opportunities to support learners while educators continue to debate whether the use of generative AI tools is a good thing, or we can move forward, building a transformative model for learning akin to the industrial revolution’s impact.
Too many professional educators remain bound by traditional methods. The past two years suggest that leaders of this new learning paradigm will not emerge from conventional educational circles. This vacuum of leadership can be filled, in part, by instructional designers, who are prepared by training and experience to begin building in this new learning space.
Staying current and relevant is essential for institutions in today’s rapidly evolving higher education landscape. However, innovative work cannot be accomplished in isolation.
On this episode, Bryan Benjamin, Executive Director of The Ivey Academy and Amrit Ahluwalia, Executive Director of Continuing Studies at Western University, discusses the importance of institutional collaboration and creating a scalable lifelong learning ecosystem.
