What if you could have a conversation with your notes?That question has consumed a corner of the internet recently, as companies like Dropbox, Box, Notion, and others have built generative AI tools that let you interact with and create new things from the data you already have in their systems.
Google’s version of this is called NotebookLM. It’s an AI-powered research tool that is meant to help you organize and interact with your own notes.
That got me to thinking…
What if the presenter/teacher/professor/trainer/preacher provided a set of notes for the AI to compare to the readers’ notes?
That way, the AI could see the discrepancies between what the presenter wanted their audience to learn/hear and what was actually being learned/heard. In a sort of digital Socratic Method, the AI could then generate some leading questions to get the audience member to check their thinking/understanding of the topic.
The end result would be that the main points were properly communicated/learned/received.
Why it matters: The best AI assistants will be the ones that require the least prompting. They’ll get to know who you are, what you need, and your modus operandi. Profiles are a good starting point, but we believe the game-changer will be larger context windows (that’s nerd-speak for the amount of context ChatGPT can handle). .
From DSC: And how about taking this a step further and remembering — or being able to access — our constantly updated Cloud-Based Learning Profiles?
My hypothesis and research suggest that as bar associations and the ABA begin to recognize the on-going systemic issues of high-cost legal education, growing legal deserts (where no lawyer serves a given population), on-going and pervasive access to justice issues, and a public that is already weary of the legal system – alternative options that are already in play might become more supported.
What might that look like?
The combination of AI-assisted education with traditional legal apprenticeships has the potential to create a rich, flexible, and engaging learning environment. Here are three scenarios that might illustrate what such a combination could look like:
Scenario One – Personalized Curriculum Development
Scenario Two – On-Demand Tutoring and Mentoring
Scenario Three – AI-assisted Peer Networks and Collaborative Learning:
We know that there are challenges – a threat to human jobs, the potential implications for cyber security and data theft, or perhaps even an existential threat to humanity as a whole. But we certainly don’t yet have a full understanding of all of the implications. In fact, a World Economic Forum report recently stated that organizations “may currently underappreciate AI-related risks,” with just four percent of leaders considering the risk level to be “significant.”
A survey carried out by analysts Baker McKenzie concluded that many C-level leaders are over-confident in their assessments of organizational preparedness in relation to AI. In particular, it exposed concerns about the potential implications of biased data when used to make HR decisions.
AI & lawyer training: How law firms can embrace hybrid learning & development — thomsonreuters.com A big part of law firms’ successful adaptation to the increased use of ChatGPT and other forms of generative AI, may depend upon how firmly they embrace online learning & development tools designed for hybrid work environments
Excerpt:
As law firms move forward in using of advanced artificial intelligence such as ChatGPT and other forms of generative AI, their success may hinge upon how they approach lawyer training and development and what tools they enlist for the process.
One of the tools that some law firms use to deliver a new, multi-modal learning environment is an online, video-based learning platform, Hotshot, that delivers more than 250 on-demand courses on corporate, litigation, and business skills.
Ian Nelson, co-founder of Hotshot, says he has seen a dramatic change in how law firms are approaching learning & development (L&D) in the decade or so that Hotshot has been active. He believes the biggest change is that 10 years ago, firms hadn’t yet embraced the need to focus on training and development.
From DSC: Heads up law schools. Are you seeing/hearing this!?
Are we moving more towards a lifelong learning model within law schools?
If not, shouldn’t we be doing that?
Are LLM programs expanding quickly enough? Is more needed?
A few current categories of AI in Edtech particularly jump out:
Teacher Productivity and Joy: Tools to make educators’ lives easier (and more fun?) by removing some of the more rote tasks of teaching, like lesson planning (we counted at least 8 different tools for lesson planning), resource curation and data collection.
Personalization and Learning Delivery: Tools to tailor instruction to the particular interests, learning preferences and preferred media consumption of students. This includes tools that convert text to video, video to text, text to comic books, Youtube to notes, and many more.
Study and Course Creation Tools: Tools for learners to automatically make quizzes, flashcards, notes or summaries of material, or even to automatically create full courses from a search term.
AI Tutors, Chatbots and Teachers: There will be no shortage of conversational AI “copilots” (which may take many guises) to support students in almost any learning context. Many Edtech companies launched their own during the conference. Possible differentiators here could be personality, safety, privacy, access to a proprietary or specific data set, or bots built on proprietary LLMs.
Simplifying Complex Processes: One of the most inspiring conversations of the conference for me was with Tiffany Green, founder of Uprooted Academy, about how AI can and should be used to remove bureaucratic barriers to college for underrepresented students (for example, used to autofill FAFSA forms, College Applications, to search for schools and access materials, etc). This is not the only complex bureaucratic process in education.
Educational LLMs: The race is on to create usable large language models for education that are safe, private, appropriate and classroom-ready. Merlyn Mind is working on this, and companies that make LLMs are sprouting up in other sectors…
This week I spent a few days at the ASU/GSV conference and ran into 7,000 educators, entrepreneurs, and corporate training people who had gone CRAZY for AI.
No, I’m not kidding. This community, which makes up people like training managers, community college leaders, educators, and policymakers is absolutely freaked out about ChatGPT, Large Language Models, and all sorts of issues with AI. Now don’t get me wrong: I’m a huge fan of this. But the frenzy is unprecedented: this is bigger than the excitement at the launch of the i-Phone.
Second, the L&D market is about to get disrupted like never before. I had two interactive sessions with about 200 L&D leaders and I essentially heard the same thing over and over. What is going to happen to our jobs when these Generative AI tools start automatically building content, assessments, teaching guides, rubrics, videos, and simulations in seconds?
The answer is pretty clear: you’re going to get disrupted. I’m not saying that L&D teams need to worry about their careers, but it’s very clear to me they’re going to have to swim upstream in a big hurry. As with all new technologies, it’s time for learning leaders to get to know these tools, understand how they work, and start to experiment with them as fast as you can.
Speaking of the ASU+GSV Summit, see this posting from Michael Moe:
Last week, the 14th annual ASU+GSV Summit hosted over 7,000 leaders from 70+ companies well as over 900 of the world’s most innovative EdTech companies. Below are some of our favorite speeches from this year’s Summit…
High-quality tutoring is one of the most effective educational interventions we have – but we need both humans and technology for it to work. In a standing-room-only session, GSE Professor Susanna Loeb, a faculty lead at the Stanford Accelerator for Learning, spoke alongside school district superintendents on the value of high-impact tutoring. The most important factors in effective tutoring, she said, are (1) the tutor has data on specific areas where the student needs support, (2) the tutor has high-quality materials and training, and (3) there is a positive, trusting relationship between the tutor and student. New technologies, including AI, can make the first and second elements much easier – but they will never be able to replace human adults in the relational piece, which is crucial to student engagement and motivation.
ChatGPT, Bing Chat, Google’s Bard—AI is infiltrating the lives of billions.
The 1% who understand it will run the world.
Here’s a list of key terms to jumpstart your learning:
Being “good at prompting” is a temporary state of affairs.The current AI systems are already very good at figuring out your intent, and they are getting better. Prompting is not going to be that important for that much longer. In fact, it already isn’t in GPT-4 and Bing. If you want to do something with AI, just ask it to help you do the thing. “I want to write a novel, what do you need to know to help me?” will get you surprisingly far.
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The best way to use AI systems is not to craft the perfect prompt, but rather to use it interactively. Try asking for something. Then ask the AI to modify or adjust its output. Work with the AI, rather than trying to issue a single command that does everything you want. The more you experiment, the better off you are. Just use the AI a lot, and it will make a big difference – a lesson my class learned as they worked with the AI to create essays.
From DSC: Agreed –> “Being “good at prompting” is a temporary state of affairs.” The User Interfaces that are/will be appearing will help greatly in this regard.
From DSC: Bizarre…at least for me in late April of 2023:
FaceTiming live with AI… This app came across the @ElunaAI Discord and I was very impressed with its responsiveness, natural expression and language, etc…
Feels like the beginning of another massive wave in consumer AI products.
The rise of AI-generated music has ignited legal and ethical debates, with record labels invoking copyright law to remove AI-generated songs from platforms like YouTube.
Tech companies like Google face a conundrum: should they take down AI-generated content, and if so, on what grounds?
Some artists, like Grimes, are embracing the change, proposing new revenue-sharing models and utilizing blockchain-based smart contracts for royalties.
The future of AI-generated music presents both challenges and opportunities, with the potential to create new platforms and genres, democratize the industry, and redefine artist compensation.
The Need for AI PD — from techlearning.com by Erik Ofgang Educators need training on how to effectively incorporate artificial intelligence into their teaching practice, says Lance Key, an award-winning educator.
“School never was fun for me,” he says, hoping that as an educator he could change that with his students. “I wanted to make learning fun.” This ‘learning should be fun’ philosophy is at the heart of the approach he advises educators take when it comes to AI.
At its 11th annual conference in 2023, educational company Coursera announced it is adding ChatGPT-powered interactive ed tech tools to its learning platform, including a generative AI coach for students and an AI course-building tool for teachers. It will also add machine learning-powered translation, expanded VR immersive learning experiences, and more.
Coursera Coach will give learners a ChatGPT virtual coach to answer questions, give feedback, summarize video lectures and other materials, give career advice, and prepare them for job interviews. This feature will be available in the coming months.
From DSC: Yes…it will be very interesting to see how tools and platforms interact from this time forth. The term “integration” will take a massive step forward, at least in my mind.
A New Era for Education — from linkedin.com by Amit Sevak, CEO of ETS and Timothy Knowles, President of the Carnegie Foundation for the Advancement of Teaching
Excerpt (emphasis DSC):
It’s not every day you get to announce a revolution in your sector. But today, we’re doing exactly that. Together, we are setting out to overturn 117 years of educational tradition. … The fundamental assumption [of the Carnegie Unit] is that time spent in a classroom equals learning. This formula has the virtue of simplicity. Unfortunately, a century of research tells us that it’s woefully inadequate.
From DSC: It’s more than interesting to think that the Carnegie Unit has outlived its usefulness and is breaking apart. In fact, the thought is very profound.
If that turns out to be the case, the ramifications will be enormous and we will have the opportunity to radically reinvent/rethink/redesign what our lifelong learning ecosystems will look like and provide.
So I appreciate what Amit and Timothy are saying here and I appreciate their relaying what the new paradigm might look like. It goes with the idea of using design thinking to rethink how we build/reinvent our learning ecosystems. They assert:
It’s time to change the paradigm. That’s why ETS and the Carnegie Foundation have come together to design a new future of assessment.
Whereas the Carnegie Unit measures seat time, the new paradigm willmeasureskills—with a focus on the ones we know are most important for success in career and in life.
Whereas the Carnegie Unit never leaves the classroom, the new paradigm willcapture learning wherever it takes place—whether that is in after-school activities, during a work-experience placement, in an internship, on an apprenticeship, and so on.
Whereas the Carnegie Unit offers only one data point—pass or fail—the new paradigm willgenerate insights throughout the learning process, the better to guide students, families, educators, and policymakers.
I could see this type of information being funneled into peoples’ cloud-based learner profiles — which we as individuals will own and determine who else can access them. I diagrammed this back in January of 2017 using blockchain as the underlying technology. That may or may not turn out to be the case. But the concept will still hold I think — regardless of the underlying technology(ies).
For example, we are seeing a lot more articles regarding things like Comprehensive Learner Records (CLR) or Learning and Employment Records (LER; examplehere), and similar items.
Speaking of reinventing our learning ecosystems, also see:
Learning happens throughout life and is not isolated to the K-12 or higher education sectors. Yet, often, validations of learning only happen in these specific areas. The system of evaluation based on courses, grades, and credit serves as a poor proxy for communicating skills given the variation in course content, grade inflation, and inclusion of participation and extra credit within course grades.
Credentialed learning provides a way to accurately document human capability for all learners throughout their life. A lifetime credentialed learning ecosystem provides better granularity around learning, better documentation of the learning, and more relevance for both the credential recipient and reviewer. This improves the match between higher education and/or employment with the individual, while also providing a more clear and accurate lifetime learning pathway.
With a fully-credentialed system, individuals can own well-documented evidence of a lifetime of learning and choose what and when to share this data. This technology enables every learner to have more opportunities for finding the best career match without today’s existing barriers around cost, access, and proxies.
Addendum on 4/28/23 — speaking of credentials:
First Rung — from the-job.beehiiv.com by Paul Fain New research shows stacking credentials pays off for low-income learners.
Stacking credentials pays off for many low-income students, new research finds, but only if learners move up the education ladder. Also, Kansas is hoping a new grant program will attract more companies to participate in microinternships.
Artificial Intelligence is now taking the world of learning by storm. Here are 5 ways you can successfully incorporate AI in online learning.
Let’s say you’re training sales reps on handling different customer personalities. You can use this technology to diversify your branching scenarios so that trainees can also speak and not only type. This way, not only will the training become more realistic, but you’ll also be able to assess and work on additional elements, such as tone of voice, volume, speech tempo, etc.
A learner who is aware of their unique strengths and development needs, as well as their preferred approach for gaining new skills, is often able to find the learning opportunities that they need more effectively and efficiently.
A global language for skills While we might be tempted to focus within, looking for ways to address our own company’s talent challenges in isolation, this common concern invites a more global solution. We would all be better off if we could build a global language for skills. It’s at least one step toward achieving global processes for evaluating and developing them.
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The top three challenges with skills and skill-based practices, as cited by McKinsey’s 2021 state of hiring survey, are: the ability to validate skills, sourcing job seekers with the right skills and scaling this approach.
Having a validated “chain” of skills for an employee helps not only in the selection process, but also as L&D departments seek to personalize learning. Blockchain creates a more valid approach to personalizing learning based on each employee’s competencies and skills gathered across their career, rather than just the skills they are demonstrating in their current organization and role.
Modeled on research demonstrating that the most effective form of learning is one-on-one tutoring1, Q-Chat offers students the experience of interacting with a personal AI tutor in an effective and conversational way. Whether they’re learning French vocabulary or Roman History, Q-Chat engages students with adaptive questions based on relevant study materials delivered through a fun chat experience. Pulling from Quizlet’s massive educational content library and using the question-based Socratic method to promote active learning, Q-Chat has the ability to test a student’s knowledge of educational content, ask in-depth questions to get at underlying concepts, test reading comprehension, help students learn a language and encourage students on healthy learning habits.
Micro-tutoring platform PhotoStudy has unveiled a new chatbot built on OpenAI’s ChatGPT APIs that can teach a complete elementary algebra textbook with “extremely high accuracy,” the company said.
“Textbook publishers and teachers can now transform their textbooks and teaching with a ChatGPT-like assistant that can teach all the material in a textbook, assess student progress, provide personalized help in weaker areas, generate quizzes with support for text, images, audio, and ultimately a student customized avatar for video interaction,” PhotoStudy said in its news release.
Some sample questions the MathGPT tool can answer:
“I don’t know how to solve a linear equation…”
“I have no idea what’s going on in class but we are doing Chapter 2. Can we start at the top?”
“Can you help me understand how to solve this mixture of coins problem?”
“I need to practice for my midterm tomorrow, through Chapter 6. Help.”
Streamlined and personalized teaching Some examples of how we’ve seen educators exploring how to teach and learn with tools like ChatGPT:
Drafting and brainstorming for lesson plans and other activities
Help with design of quiz questions or other exercises
Experimenting with custom tutoring tools
Customizing materials for different preferences (simplifying language, adjusting to different reading levels, creating tailored activities for different interests)
Providing grammatical or structural feedback on portions of writing
Use in upskilling activities in areas like writing and coding (debugging code, revising writing, asking for explanations)
Critique AI generated text
While several of the above draw on ChatGPT’s potential to be explored as a tool for personalization, there are risks associated with such personalization as well, including student privacy, biased treatment, and development of unhealthy habits. Before students use tools that offer these services without direct supervision, they and their educators should understand the limitations of the tools outlined below.
Also relevant/see:
Designing Assignments in the ChatGPT Era — from insidehighered.com by Susan D’Agostino Some instructors seek to craft assignments that guide students in surpassing what AI can do. Others see that as a fool’s errand—one that lends too much agency to the software.
Excerpt (emphasis DSC):
David Wiley wrote a thoughtful post on the ways in which AI and Large Language Models (LLMs) can “provide instructional designers with first drafts of some of the work they do.” He says “imagine you’re an instructional designer who’s been paired with a faculty member to create a course in microeconomics. These tools might help you quickly create first drafts of” learning outcomes, discussion prompts, rubrics, and formative assessment items. The point is that LLMs can quickly generate rough drafts that are mostly accurate drafts, that humans can then “review, augment, and polish,” potentially shifting the work of instructional designers from authors to editors. The post is well worth your time.
… The question that I’d like to spend some time thinking about is the following: What new knowledge, capacities, and skills do instructional designers need in their role as editors and users of LLMs?
This resonated with me. Instructional Designer positions are starting to require AI and ML chops. I’m introducing my grad students to AI and ChatGPT this semester. I have an assignment based on it.
In this publication, we articulate the critical steps needed to unbundle the learning ecosystem, build core competencies, design learning experiences, curate new opportunities, and rebundle these experiences into coherent pathways. .
Vision
Every learner deserves an unlimited number of unbundled opportunities to explore, engage, and define experiences that advance their progress along a co-designed educational pathway. Each pathway provides equitable and personalized access to stacked learning experiences leading to post-secondary credentials and secure family-sustaining employment. Throughout the journey, supportive coaches focus on helping learners build skills to navigate with agency. In parallel, learners develop foundational skills (literacy, math), technical skills, and durable skills and connect these to challenging co-designed experiences. The breadth and depth of experiences increase over time, and, in partnership, learners and coaches map progress towards reaching community-defined goals. This vision is only enabled by an unbundled learning ecosystem.
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Recommendations
Solutions already exist in the ecosystem and need to be combined and scaled. Funding models (like My Tech High), badging/credentialing at the competency level (like VLACS), coaching models (like Big Thought), and open ecosystems (like NH Learn Everywhere) provide an excellent foundation. Thus, building unbundled systems has already begun but needs systemic changes to become widely available and accepted.
Build a robust competency-based system.
Create a two-way marketplace for unbundled learning.
Implement policy to support credit for out-of-system experiences.
Invest in technology infrastructure for Learning and Employment Records.
Design interoperable badging systems that connect to credentials.
Now is the ideal time for a flexible and competent market leader to emerge and seize this opportunity, delivering personalized and lifelong educational solutions and experiences that meet the needs of a learning-hungry populace.
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Edtech businesses can address this widening skills gap and need for frequent job-switching through those same data-driven ecosystems, which can support the user through their career and leisure activities. For example, a user could sync their profile with their work’s employee portal to receive further professional development. Simultaneously, the technology would support the user during their spare time as they take courses or watch video content ranging from Adobe InDesign to gardening, further refining their skills. And, when it comes time to retire, the user’s trusted ecosystem has a backlog of data to recommend applicable hobbies and community events.
For example, a user could sync their profile with their work’s employee portal to receive further professional development.
A learning ecosystem is composed of people, tools, technologies, content, processes, culture, strategies, and any other resource that helps one learn. Learning ecosystems can be at an individual level as well as at an organizational level.
Some example components:
Subject Matter Experts (SMEs) such as faculty, staff, teachers, trainers, parents, coaches, directors, and others
Fellow employees
L&D/Training professionals
Managers
Instructional Designers
Librarians
Consultants
Types of learning
Active learning
Adult learning
PreK-12 education
Training/corporate learning
Vocational learning
Experiential learning
Competency-based learning
Self-directed learning (i.e., heutagogy)
Mobile learning
Online learning
Face-to-face-based learning
Hybrid/blended learning
Hyflex-based learning
Game-based learning
XR-based learning (AR, MR, and VR)
Informal learning
Formal learning
Lifelong learning
Microlearning
Personalized/customized learning
Play-based learning
Cloud-based learning apps
Coaching & mentoring
Peer feedback
Job aids/performance tools and other on-demand content
Websites
Conferences
Professional development
Professional organizations
Social networking
Social media – Twitter, LinkedIn, Facebook/Meta, other
Communities of practice
Artificial Intelligence (AI) — including ChatGPT, learning agents, learner profiles,
