When Educators and Employers Work Together, Students Succeed — from hbsp.harvard.edu by Joseph Fuller and Manjari Raman

(Emphasis below from DSC)

Last year, in “The Partnership Imperative,” we put forth a set of more than 40 best practices that employers and educators can use to develop a close collaboration. As part of that effort, we identified three main goals and laid out strategies for achieving each.

  1. Partner with each other to offer training and education that is aligned with industry needs. (DSC: Similar to how Instructional Designers want alignment with learning objectives, learning activities, and assessments of learning.)
  2. Establish relationships with each other that result in the recruitment and hiring of students and graduates.
  3. Make supply-and-demand decisions that are informed by the latest data and trends.

From DSC:
Under #1, their strategies include:

Cocreate and regularly update college curriculums so that they reflect relevant technical and foundational skills based on industry needs. Codesign programs that fit with students’ lives and industry hiring cycles. Incorporate classroom experiences that simulate real-world settings and scenarios.

I see AI being able to identify what those changing, currently sought-after, and foundational skills are based on industry needs (which shouldn’t be hard, and vendors like Microsoft are already doing this by combing through the posted job descriptions on their platforms). These findings/results will help build regularly updated learning playlists and should provide guidance to learning-related organizations/groups/individuals/teams on what content to develop and offer  (i.e., courses/learning modules/micro-learning-based streams of content, other).

 

Instructional Designers as Institutional Change Agents — from er.educause.edu/ by Aaron Bond, Barb Lockee and Samantha Blevins

Systems thinking and change strategies can be used to improve the overall functioning of a system. Because instructional designers typically use systems thinking to facilitate behavioral changes and improve institutional performance, they are uniquely positioned to be change agents at higher education institutions.

In higher education, instructional designers are often seen as “change agents” because they help to facilitate behavioral changes and improve performance at their institutions. Due to their unique position of influence among higher education leaders and faculty and their use of systems thinking, instructional designers can help bridge institutional priorities and the specific needs of various stakeholders. COVID-19 and the switch to emergency remote teaching raised awareness of the critical services instructional designers provide, including preparing faculty to teach—and students to learn—in well-designed learning environments. Today, higher education institutions increasingly rely on the experience and expertise of instructional designers.

Figure 1. How Instructional Designers Employ Systems Thinking
.

 

Why Entrepreneurship Might Save Our Kids—and the Rest of Us. — from gettingsmart.com by Katie Kimbrell

Key Points (emphasis DSC):

  • We need to be asking our students “How did you put your ideas into the world today?”.
  • To be human is to be entrepreneurial.

One of my favorite mom friends asks her young school-aged kids every day, “What did you make today?”

I love how subtly subversive this question is. Not, “How was school today?” “Were you good today?” or, “How’s [insert school subject] going?” But, How did you put your ideas out into the world today?” 

That simple question understands this fundamental truth: to be human is to create, to employ our imaginations and partake in forming the world we want to live in.


Microschool in a Box: Programs Enabling the Microschool Movement — from gettingsmart.com by Nate McClennen

Key Points

  • Microschools are not new. In fact, they are as old as learning itself.
  • Funding and operations can be difficult within a microschool model. Programs and other organizations can support planning, design and implementation.

Microschools are meeting strong market demand for more personalized, more contextualized and more relevant learning for every student. Programs like ASU Prep’s Microschool in a Box make it possible for more learners to become future-ready with access to affordable, relational microschool learning.

Nate McClennen


The Science of Classroom Design — from edutopia.org by Youki Terada and Stephen Merrill
Our comprehensive, all-in, research-based look at the design of effective learning spaces.

Topics include:

  • Light
  • Ventilation and air quality
  • Complexity and color
  • Data walls
  • Nature, plants, and greenery
  • Representation
    • Students can experience representation in classrooms by seeing their own or peers’ artifacts on walls and in shared virtual spaces, or by being exposed to images and references that mirror their interests, passions, and backgrounds.
  • Flexibility
  • Learning differences and neurodivergence
  • Heat
  • Acoustics/noise
  • Seating arrangements
  • Learning Zones

Addendum on 12/1/23:

 

 

9 Tips for Using AI for Learning (and Fun!) — from edutopia.org by Daniel Leonard; via Donna Norton on X/Twitter
These innovative, AI-driven activities will help you engage students across grade levels and subject areas.

Here are nine AI-based lesson ideas to try across different grade levels and subject areas.

ELEMENTARY SCHOOL

AI-generated Animated Drawing of artwork

Courtesy of Meta AI Research
A child’s drawing (left) and animations created with Animated Drawings.

.

1. Bring Student Drawings to Life: Young kids love to sketch, and AI can animate their sketches—and introduce them to the power of the technology in the process.

HIGH SCHOOL

8. Speak With AI in a Foreign Language: When learning a new language, students might feel self-conscious about making mistakes and avoid practicing as much as they should.


Though not necessarily about education, also see:

How I Use AI for Productivity — from wondertools.substack.com by Jeremy Caplan
In this Wonder Tools audio post I share a dozen of my favorite AI tools

From DSC:
I like Jeremy’s mentioning the various tools that he used in making this audio post:

 

Where a developing, new kind of learning ecosystem is likely headed [Christian]

From DSC:
As I’ve long stated on the Learning from the Living [Class]Room vision, we are heading toward a new AI-empowered learning platform — where humans play a critically important role in making this new learning ecosystem work.

Along these lines, I ran into this site out on X/Twitter. We’ll see how this unfolds, but it will be an interesting space to watch.

Project Chiron's vision: Our vision for education Every child will soon have a super-intelligent AI teacher by their side. We want to make sure they instill a love of learning in children.


From DSC:
This future learning platform will also focus on developing skills and competencies. Along those lines, see:

Scale for Skills-First — from the-job.beehiiv.com by Paul Fain
An ed-tech giant’s ambitious moves into digital credentialing and learner records.

A Digital Canvas for Skills
Instructure was a player in the skills and credentials space before its recent acquisition of Parchment, a digital transcript company. But that $800M move made many observers wonder if Instructure can develop digital records of skills that learners, colleges, and employers might actually use broadly.

Ultimately, he says, the CLR approach will allow students to bring these various learning types into a coherent format for employers.

Instructure seeks a leadership role in working with other organizations to establish common standards for credentials and learner records, to help create consistency. The company collaborates closely with 1EdTech. And last month it helped launch the 1EdTech TrustEd Microcredential Coalition, which aims to increase quality and trust in digital credentials.

Paul also links to 1EDTECH’s page regarding the Comprehensive Learning Record

 


When schools and families go to court over special education, everyone loses — from wfyi.org by Lee Gaines

While federal law mandates public schools provide an appropriate education to students with disabilities, it’s often up to parents to enforce it.

Schwarten did what few people have the resources to do: she hired a lawyer and requested a due process hearing. It’s like a court case. And it’s intended to resolve disputes between families and schools over special education services.

It’s also a traumatic and adversarial process for families and schools that can rack up hundreds of thousands of dollars in legal fees and destroy relationships between parents and district employees. And even when families win, children don’t always get the public education they deserve.


Future of Learning: Native American students have the least access to computer science — from The Hechinger Report by Javeria Salman

But computer science lessons like the ones at Dzantik’i Heeni Middle School are relatively rare. Despite calls from major employers and education leaders to expand K-12 computer science instruction in response to the workforce’s increasing reliance on digital technology, access to the subject remains low — particularly for Native American students.

Only 67 percent of Native American students attend a school that offers a computer science course, the lowest percentage of any demographic group, according to a new study from the nonprofit Code.org. A recent report from the Kapor Foundation and the American Indian Science and Engineering Society, or AISES, takes a deep look at why Native students’ access to computer and technology courses in K-12 is so low, and examines the consequences.


The Case for Andragogy in Educator Development — from Dialogic #341 by Tom Barrett

Understanding the Disconnect
We often find ourselves in professional development sessions that starkly contrast with the interactive and student-centred learning environments we create. We sit as passive recipients rather than active participants, receiving generic content that seldom addresses our unique experiences or teaching challenges.

This common scenario highlights a significant gap in professional development: the failure to apply the principles of adult learning, or andragogy, which acknowledges that educators, like their students, benefit from a learning process that is personalised, engaging, and relevant.

The irony is palpable — while we foster environments of inquiry and engagement in our classrooms, our learning experiences often lack these elements.

The disconnect prompts a vital question: If we are to cultivate a culture of lifelong learning among our students, shouldn’t we also embody this within our professional growth? It’s time for the professional development of educators to reflect the principles we hold dear in our teaching practices.

 

A future-facing minister, a young inventor and a shared vision: An AI tutor for every student — from news.microsoft.com by Chris Welsch

The Ministry of Education and Pativada see what has become known as the U.A.E. AI Tutor as a way to provide students with 24/7 assistance as well as help level the playing field for those families who cannot afford a private tutor. At the same time, the AI Tutor would be an aid to teachers, they say. “We see it as a tool that will support our teachers,” says Aljughaiman. “This is a supplement to classroom learning.”

If everything goes according to plan, every student in the United Arab Emirates’ school system will have a personal AI tutor – that fits in their pockets.

It’s a story that involves an element of coincidence, a forward-looking education minister and a tech team led by a chief executive officer who still lives at home with his parents.

In February 2023, the U.A.E.’s education minister, His Excellency Dr. Ahmad Belhoul Al Falasi, announced that the ministry was embracing AI technology and pursuing the idea of an AI tutor to help Emirati students succeed. And he also announced that the speech he presented had been written by ChatGPT. “We should not demonize AI,” he said at the time.



Fostering deep learning in humans and amplifying our intelligence in an AI World — from stefanbauschard.substack.com by Stefan Bauschard
A free 288-page report on advancements in AI and related technology, their effects on education, and our practical support for AI-amplified human deep learning

Six weeks ago, Dr. Sabba Quidwai and I accidentally stumbled upon an idea to compare the deep learning revolution in computer science to the mostly lacking deep learning efforts in education (Mehta & Fine). I started writing, and as these things often go with me, I thought there were many other things that would be useful to think through and for educators to know, and we ended up with this 288-page report.

***

Here’s an abstract from that report:

This report looks at the growing gap between the attention paid to the development of intelligence in machines and humans. While computer scientists have made great strides in developing human intelligence capacities in machines using deep learning technologies, including the abilities of machines to learn on their own, a significant part of the education system has not kept up with developing the intelligence capabilities in people that will enable them to succeed in the 21st century. Instead of fully embracing pedagogical methods that place primary emphasis on promoting collaboration, critical thinking, communication, creativity, and self-learning through experiential, interdisciplinary approaches grounded in human deep learning and combined with current technologies, a substantial portion of the educational system continues to heavily rely on traditional instructional methods and goals. These methods and goals prioritize knowledge acquisition and organization, areas in which machines already perform substantially better than people.

Also from Stefan Bauschard, see:

  • Debating in the World of AI
    Performative assessment, learning to collaborate with humans and machines, and developing special human qualities

13 Nuggets of AI Wisdom for Higher Education Leaders — from jeppestricker.substack.com by Jeppe Klitgaard Stricker
Actionable AI Guidance for Higher Education Leaders

Incentivize faculty AI innovation with AI. 

Invest in people first, then technology. 

On teaching, learning, and assessment. AI has captured the attention of all institutional stakeholders. Capitalize to reimagine pedagogy and evaluation. Rethink lectures, examinations, and assignments to align with workforce needs. Consider incorporating Problem-Based Learning, building portfolios and proof of work, and conducting oral exams. And use AI to provide individualized support and assess real-world skills.

Actively engage students.


Some thoughts from George Siemens re: AI:

Sensemaking, AI, and Learning (SAIL), a regular look at how AI is impacting learning.

Our education system has a uni-dimensional focus: learning things. Of course, we say we care about developing the whole learner, but the metrics that matter (grade, transcripts) that underpin the education system are largely focused on teaching students things that have long been Google-able but are now increasingly doable by AI. Developments in AI matters in ways that calls into question large parts of what happens in our universities. This is not a statement that people don’t need to learn core concepts and skills. My point is that the fulcrum of learning has shifted. Knowing things will continue to matter less and less going forward as AI improves its capabilities. We’ll need to start intentionally developing broader and broader attributes of learners: metacognition, wellness, affect, social engagement, etc. Education will continue to shift toward human skills and away from primary assessment of knowledge gains disconnected from skills and practice and ways of being.


AI, the Next Chapter for College Librarians — from insidehighered.com by Lauren Coffey
Librarians have lived through the disruptions of fax machines, websites and Wikipedia, and now they are bracing to do it again as artificial intelligence tools go mainstream: “Maybe it’s our time to shine.”

A few months after ChatGPT launched last fall, faculty and students at Northwestern University had many questions about the building wave of new artificial intelligence tools. So they turned to a familiar source of help: the library.

“At the time it was seen as a research and citation problem, so that led them to us,” said Michelle Guittar, head of instruction and curriculum support at Northwestern University Libraries.

In response, Guittar, along with librarian Jeanette Moss, created a landing page in April, “Using AI Tools in Your Research.” At the time, the university itself had yet to put together a comprehensive resource page.


From Dr. Nick Jackson’s recent post on LinkedIn: 

Last night the Digitech team of junior and senior teachers from Scotch College Adelaide showcased their 2023 experiments, innovation, successes and failures with technology in education. Accompanied by Student digital leaders, we saw the following:

  •  AI used for languagelearning where avatars can help with accents
  • Motioncapture suits being used in mediastudies
  • AI used in assessment and automatic grading of work
  • AR used in designtechnology
  • VR used for immersive Junior school experiences
  • A teacher’s AI toolkit that has changed teaching practice and workflow
  • AR and the EyeJack app used by students to create dynamic art work
  • VR use in careers education in Senior school
  • How ethics around AI is taught to Junior school students from Year 1
  • Experiments with MyStudyWorks

Almost an Agent: What GPTs can do — from oneusefulthing.org by Ethan Mollick

What would a real AI agent look like? A simple agent that writes academic papers would, after being given a dataset and a field of study, read about how to compose a good paper, analyze the data, conduct a literature review, generate hypotheses, test them, and then write up the results, all without intervention. You put in a request, you get a Word document that contains a draft of an academic paper.

A process kind of like this one:


What I Learned From an Experiment to Apply Generative AI to My Data Course — from edsurge.com by Wendy Castillo

As an educator, I have a duty to remain informed about the latest developments in generative AI, not only to ensure learning is happening, but to stay on top of what tools exist, what benefits and limitations they have, and most importantly, how students might be using them.

However, it’s also important to acknowledge that the quality of work produced by students now requires higher expectations and potential adjustments to grading practices. The baseline is no longer zero, it is AI. And the upper limit of what humans can achieve with these new capabilities remains an unknown frontier.


Artificial Intelligence in Higher Education: Trick or Treat? — from tytonpartners.com by Kristen Fox and Catherine Shaw
.

Two components of AI -- generative AI and predictive AI

 

What happens to teaching after Covid? — from chronicle.com by Beth McMurtrie

It’s an era many instructors would like to put behind them: black boxes on Zoom screens, muffled discussions behind masks, students struggling to stay engaged. But how much more challenging would teaching during the pandemic have been if colleges did not have experts on staff to help with the transition? On many campuses, teaching-center directors, instructional designers, educational technologists, and others worked alongside professors to explore learning-management systems, master video technology, and rethink what and how they teach.

A new book out this month, Higher Education Beyond Covid: New Teaching Paradigms and Promise, explores this period through the stories of campus teaching and learning centers. Their experiences reflect successes and failures, and what higher education could learn as it plans for the future.

Beth also mentioned/link to:


How to hold difficult discussions online — from chronicle.com by Beckie Supiano

As usual, our readers were full of suggestions. Kathryn Schild, the lead instructional designer in faculty development and instructional support at the University of Alaska at Anchorage, shared a guide she’s compiled on holding asynchronous discussions, which includes a section on difficult topics.

In an email, Schild also pulled out a few ideas she thought were particularly relevant to Le’s question, including:

  • Set the ground rules as a class. One way to do this is to share your draft rules in a collaborative document and ask students to annotate it and add suggestions.
  • Plan to hold fewer difficult discussions than in a face-to-face class, and work on quality over quantity. This could include multiweek discussions, where you spiral through the same issue with fresh perspectives as the class learns new approaches.
  • Start with relationship-building interactions in the first few weeks, such as introductions, low-stakes group assignments, or peer feedback, etc.
 


Teaching writing in the age of AI — from the Future of Learning (a Hechinger Report newsletter) by Javeria Salman

ChatGPT can produce a perfectly serviceable writing “product,” she said. But writing isn’t a product per se — it’s a tool for thinking, for organizing ideas, she said.

“ChatGPT and other text-based tools can’t think for us,” she said. “There’s still things to learn when it comes to writing because writing is a form of figuring out what you think.”

When students could contrast their own writing to ChatGPT’s more generic version, Levine said, they were able to “understand what their own voice is and what it does.”




Grammarly’s new generative AI feature learns your style — and applies it to any text — from techcrunch.com by Kyle Wiggers; via Tom Barrett

But what about text? Should — and if so, how should — writers be recognized and remunerated for AI-generated works that mimic their voices?

Those are questions that are likely to be raised by a feature in Grammarly, the cloud-based typing assistant, that’s scheduled to launch by the end of the year for subscribers to Grammarly’s business tier. Called “Personalized voice detection and application,” the feature automatically detects a person’s unique writing style and creates a “voice profile” that can rewrite any text in the person’s style.


Is AI Quietly Weaving the Fabric of a Global Classroom Renaissance? — from medium.com by Robert the Robot
In a world constantly buzzing with innovation, a silent revolution is unfolding within the sanctuaries of learning—our classrooms.

From bustling metropolises to serene hamlets, schools across the globe are greeting a new companion—Artificial Intelligence (AI). This companion promises to redefine the essence of education, making learning a journey tailored to each child’s unique abilities.

The advent of AI in education is akin to a gentle breeze, subtly transforming the academic landscape. Picture a classroom where each child, with their distinct capabilities and pace, embarks on a personalized learning path. AI morphs this vision into reality, crafting a personalized educational landscape that celebrates the unique potential harbored within every learner.


AI Books for Educators — from aiadvisoryboards.wordpress.com by Barbara Anna Zielonka

Books have always held a special place in my heart. As an avid reader and AI enthusiast, I have curated a list of books on artificial intelligence specifically tailored for educators. These books delve into the realms of AI, exploring its applications, ethical considerations, and its impact on education. Share your suggestions and let me know which books you would like to see included on this list.


SAIL: ELAI recordings, AI Safety, Near term AI/learning — by George Siemens

We held our fourth online Empowering Learners for the Age of AI conference last week. We sold out at 1500 people (a Whova and budget limit). The recordings/playlist from the conference can now be accessed here.

 

60+ Ideas for ChatGPT Assignments — from stars.library.ucf.edu by Kevin Yee, Kirby Whittington, Erin Doggette, and Laurie Uttich

60+ ideas for using ChatGPT in your assignments today


Artificial intelligence is disrupting higher education — from itweb.co.za by Rennie Naidoo; via GSV
Traditional contact universities need to adapt faster and find creative ways of exploring and exploiting AI, or lose their dominant position.

Higher education professionals have a responsibility to shape AI as a force for good.


Introducing Canva’s biggest education launch — from canva.com
We’re thrilled to unveil our biggest education product launch ever. Today, we’re introducing a whole new suite of products that turn Canva into the all-in-one classroom tool educators have been waiting for.

Also see Canva for Education.
Create and personalize lesson plans, infographics,
posters, video, and more. 
100% free for
teachers and students at eligible schools.


ChatGPT and generative AI: 25 applications to support student engagement — from timeshighereducation.com by Seb Dianati and Suman Laudari
In the fourth part of their series looking at 100 ways to use ChatGPT in higher education, Seb Dianati and Suman Laudari share 25 prompts for the AI tool to boost student engagement


There are two ways to use ChatGPT — from theneurondaily.com

  1. Type to it.
  2. Talk to it (new).


Since then, we’ve looked to it for a variety of real-world business advice. For example, Prof Ethan Mollick posted a great guide using ChatGPT-4 with voice as a negotiation instructor.

In a similar fashion, you can consult ChatGPT with voice for feedback on:

  • Job interviews.
  • Team meetings.
  • Business presentations.



Via The Rundown: Google is using AI to analyze the company’s Maps data and suggest adjustments to traffic light timing — aiming to cut driver waits, stops, and emissions.


Google Pixel’s face-altering photo tool sparks AI manipulation debate — from bbc.com by Darren Waters

The camera never lies. Except, of course, it does – and seemingly more often with each passing day.
In the age of the smartphone, digital edits on the fly to improve photos have become commonplace, from boosting colours to tweaking light levels.

Now, a new breed of smartphone tools powered by artificial intelligence (AI) are adding to the debate about what it means to photograph reality.

Google’s latest smartphones released last week, the Pixel 8 and Pixel 8 Pro, go a step further than devices from other companies. They are using AI to help alter people’s expressions in photographs.



From Digital Native to AI-Empowered: Learning in the Age of Artificial Intelligence — from campustechnology.com by Kim Round
The upcoming generation of learners will enter higher education empowered by AI. How can institutions best serve these learners and prepare them for the workplace of the future?

Dr. Chris Dede, of Harvard University and Co-PI of the National AI Institute for Adult Learning and Online Education, spoke about the differences between knowledge and wisdom in AI-human interactions in a keynote address at the 2022 Empowering Learners for the Age of AI conference. He drew a parallel between Star Trek: The Next Generation characters Data and Picard during complex problem-solving: While Data offers the knowledge and information, Captain Picard offers the wisdom and context from on a leadership mantle, and determines its relevance, timing, and application.


The Near-term Impact of Generative AI on Education, in One Sentence — from opencontent.org by David Wiley

This “decreasing obstacles” framing turned out to be helpful in thinking about generative AI. When the time came, my answer to the panel question, “how would you summarize the impact generative AI is going to have on education?” was this:

“Generative AI greatly reduces the degree to which access to expertise is an obstacle to education.”

We haven’t even started to unpack the implications of this notion yet, but hopefully just naming it will give the conversation focus, give people something to disagree with, and help the conversation progress more quickly.


How to Make an AI-Generated Film — from heatherbcooper.substack.com by Heather Cooper
Plus, Midjourney finally has a new upscale tool!


Eureka! NVIDIA Research Breakthrough Puts New Spin on Robot Learning — from blogs.nvidia.com by Angie Lee
AI agent uses LLMs to automatically generate reward algorithms to train robots to accomplish complex tasks.

From DSC:
I’m not excited about this, as I can’t help but wonder…how long before the militaries of the world introduce this into their warfare schemes and strategies?


The 93 Questions Schools Should Ask About AI — from edweek.org by Alyson Klein

The toolkit recommends schools consider:

  • Purpose: How can AI help achieve educational goals?
  • Compliance: How does AI fit with existing policies?
  • Knowledge: How can schools advance AI Literacy?
  • Balance: What are the benefits and risks of AI?
  • Integrity: How does AI fit into policies on things like cheating?
  • Agency: How can humans stay in the loop on AI?
  • Evaluation: How can schools regularly assess the impact of AI?
 
 

Regional Colleges Saw Biggest Application Gains After Tuition Resets — from insidehighered.com by Kathryn Palmer
A new report compared post-reset application growth at nationally known and regional institutions. 

Dozens of colleges and universities have dropped their sticker prices for tuition over the past decade, even as research has shown that tuition resets have a nominal influence on long-term enrollment increases. But a report released this week shows that regional colleges were more likely than nationally known institutions to see increases in applications after a reset.

“Students are more focused now on return on investment than they used to be,” said Devon McGee, a principal at Kennedy & Company, the higher education consulting firm that produced the report. Compared to bigger-name colleges, “A lot of these regional institutions are great liberal arts–type institutions, but they are less associated—fairly or unfairly—with preparing students for a job.”


Why hybrid learning needs hybrid faculties — from timeshighereducation.com by An Jacobs & Norma Rossi
Online courses should be integrated into everyday faculty functions to improve remote and in-person classes as well as the overall student experience


 

The Learning & Employment Records (LER) Ecosystem Map — with thanks to Melanie Booth on LinkedIn for this resource
Driving Opportunity and Equity Through Learning & Employment Records

The Learning & Employment Records (LER) Ecosystem Map

Imagine A World Where…

  • Everyone is empowered to access learning and earning opportunities based on what they know and can do, whether those skills and abilities are obtained through degrees, work experiences, or independent learning.
  • People can capture and communicate the skills and competencies they’ve acquired across their entire learning journey — from education, experience and service — with more ease, confidence, and clarity than a traditional resume.
  • Learners and earners control their information and can curate their skills to take advantage of every opportunity they are truly qualified to pursue, opening up pathways that help address systemic inequities.
  • Employers can tap into a wider talent pool and better match applicants to opportunities with verifiable credentials that represent skills, competencies, and achievements.

This is the world that we believe can be created by Learning and Employment Records (LERs), i.e. digital records of learning and work experiences that are linked to and controlled by learners and earners. An interoperable, well-governed LER ecosystem has the potential to transform the future of work so that it is more equitable, efficient, and effective for everyone involved— individuals, training and education providers, employers, and policymakers.


Also per Melanie Booth, see:

 

Thinking with Colleagues: AI in Education — from campustechnology.com by Mary Grush
A Q&A with Ellen Wagner

Wagner herself recently relied on the power of collegial conversations to probe the question: What’s on the minds of educators as they make ready for the growing influence of AI in higher education? CT asked her for some takeaways from the process.

We are in the very early days of seeing how AI is going to affect education. Some of us are going to need to stay focused on the basic research to test hypotheses. Others are going to dive into laboratory “sandboxes” to see if we can build some new applications and tools for ourselves. Still others will continue to scan newsletters like ProductHunt every day to see what kinds of things people are working on. It’s going to be hard to keep up, to filter out the noise on our own. That’s one reason why thinking with colleagues is so very important.

Mary and Ellen linked to “What Is Top of Mind for Higher Education Leaders about AI?” — from northcoasteduvisory.com. Below are some excerpts from those notes:

We are interested how K-12 education will change in terms of foundational learning. With in-class, active learning designs, will younger students do a lot more intensive building of foundational writing and critical thinking skills before they get to college?

  1. The Human in the Loop: AI is built using math: think of applied statistics on steroids. Humans will be needed more than ever to manage, review and evaluate the validity and reliability of results. Curation will be essential.
  2. We will need to generate ideas about how to address AI factors such as privacy, equity, bias, copyright, intellectual property, accessibility, and scalability.
  3. Have other institutions experimented with AI detection and/or have held off on emerging tools related to this? We have just recently adjusted guidance and paused some tools related to this given the massive inaccuracies in detection (and related downstream issues in faculty-elevated conduct cases)

Even though we learn repeatedly that innovation has a lot to do with effective project management and a solid message that helps people understand what they can do to implement change, people really need innovation to be more exciting and visionary than that.  This is the place where we all need to help each other stay the course of change. 


Along these lines, also see:


What people ask me most. Also, some answers. — from oneusefulthing.org by Ethan Mollick
A FAQ of sorts

I have been talking to a lot of people about Generative AI, from teachers to business executives to artists to people actually building LLMs. In these conversations, a few key questions and themes keep coming up over and over again. Many of those questions are more informed by viral news articles about AI than about the real thing, so I thought I would try to answer a few of the most common, to the best of my ability.

I can’t blame people for asking because, for whatever reason, the companies actually building and releasing Large Language Models often seem allergic to providing any sort of documentation or tutorial besides technical notes. I was given much better documentation for the generic garden hose I bought on Amazon than for the immensely powerful AI tools being released by the world’s largest companies. So, it is no surprise that rumor has been the way that people learn about AI capabilities.

Currently, there are only really three AIs to consider: (1) OpenAI’s GPT-4 (which you can get access to with a Plus subscription or via Microsoft Bing in creative mode, for free), (2) Google’s Bard (free), or (3) Anthropic’s Claude 2 (free, but paid mode gets you faster access). As of today, GPT-4 is the clear leader, Claude 2 is second best (but can handle longer documents), and Google trails, but that will likely change very soon when Google updates its model, which is rumored to be happening in the near future.

 
© 2024 | Daniel Christian