MARGIE MEACHAM – CRYSTAL BALLING WITH LEARNNOVATORS (SEASON II)

MARGIE MEACHAM – CRYSTAL BALLING WITH LEARNNOVATORS (SEASON II)

ABOUT MARGIE MEACHAM:

While many folks are jumping onto the AI “bandwagon,” Margie Meacham has been building educational chatbots for clients since 2013. She has spoken at professional events around the world, explaining how modern chatbots deliver scalable adaptive learning experiences using machine learning and generative AI.

• Her book, Brain Matters: How to help anyone learn anything with neuroscience, has been the highest-rated, top-selling book in her category for the past 5 years.
• Her book, AI and Talent Development, was published by ATD Press in 2020, and continues to be one of the highest rated publications on this subject on Amazon.
• She developed the adaptive learning “consultant” for the United Nations, “Blu,” which won the Tech & Learning Excellence Award.
• Her AI and Data Science course won a Brandon Hall award for custom content. 
• Margie served as an advisor on the ATD Research Report, “AI in Learning and Talent Development: Embracing Its Future Potential in the Workplace.”
• She wrote the AI and Emerging Technologies section of ATD’s Talent Development Body of Knowledge (TDBoK).
• She teaches the talent development teams of the U.S. Armed Forces, AI and Emerging Technologies. 
• Margie also offers her own masterclass, AI for Talent Development Professionals, which prepares knowledge workers to use AI to enhance their own productivity by gaining workflow efficiencies.
• Two projects she’s working on now that will be released in 2024 are Adaptive Learning with AI for LinkedIn Learning and an on demand course for ATD called AI and Emerging Technologies.

Important Links:

ABOUT THIS INTERVIEW SERIES:

Crystal Balling with Learnnovators is a thought-provoking interview series that attempts to gaze into the future of e-learning. It comprises stimulating discussions with industry experts and product evangelists on emerging trends in the learning landscape.

Join us on this exciting journey as we engage with thought leaders and learning innovators to see what the future of our industry looks like.

THE INTERVIEW:

LEARNNOVATORS: Welcome back, Margie! It’s a genuine delight to have you with us again. Your expertise as an instructional design and performance improvement consultant has continued to inspire and revolutionize the way we think about creating meaningful learning experiences. We’re thrilled to have the opportunity to delve deeper with you again into the evolving landscape of workplace learning, exploring its past, present, and future.

1. LEARNNOVATORS (THE PAST): Margie, with your rich experience in integrating the principles of neuroscience with learning, you have been at the forefront of transformative educational practices. In the era preceding the advent of Generative AI, the landscape of workplace learning was markedly different. How did you perceive the integration of neuroscience in learning strategies and methodologies during this time, and what were the predominant challenges and opportunities for learning professionals? Additionally, could you share a specific instance or project that exemplifies the approach to learning in the pre-Generative-AI era, highlighting how neuroscience principles were employed to overcome the limitations of the technology and resources available then? Lastly, reflecting on the limitations of yesterday’s technologies and the then-current understanding of neuroscience, do you believe these constraints significantly restricted the impact of our learning solutions, as Marc Zao-Sanders suggests? If so, how did these technological and neurological boundaries influence the design and effectiveness of our educational strategies?

MARGIE MEACHAM: As you point out, our perspective on learning technology changed almost overnight when ChatGPT was released in late 2022. While it’s important to remember that there are many other Generative AI Models available, there’s no doubt that ChatGPT grabbed our attention first and forced us to look at a technology that other industries have been using for well over a decade. It’s not really surprising that the learning profession tends to lag when adopting new technologies because we’ve seen it before. Let’s look at the pre-ChatGPT era you just mentioned, say 2010 through 2021. As Zao-Sanders and others suggested at that time, learning technology had become outdated, predictable, and disappointing to most learners. Quite frankly, learners were far more engaged with the apps on their phones than with their organization’s LMS. Digital natives and many other workers now expect the same compelling experiences that are readily available on their phones, and instead, the pre-GenAI era offered up stale, outdated, one-size-fits-all training. A decade ago, we might have chalked up this gap to limitations of available and affordable learning technologies, but today there is really no excuse. For example, most eLearning today, even if it has been developed more recently, is mostly about a narrator speaking to a series of images or slides. Learners may be asked to click on options presented on the screen or answer multiple-choice questions, but the overall engagement level is very limited. Research tells us that the human brain becomes very passive in this type of environment, which means that far fewer neurons are engaged and far less learning is taking place. But it checks a box, and often, sadly, this has become the goal in many overworked, understaffed, and under-funded training organizations.

While this is indeed the Age of AI in most parts of our lives today, we’re not quite there as a profession. While there is great interest in AI, and there are many new tools that are well adapted to delivering personalized learning experiences, very few instructional designers, trainers, or Chief Learning Officers understand how to leverage these tools ethically and effectively. That’s why I’m on a mission to help my colleagues catch up to the present so they’re prepared for an AI-enabled future.

However, it is only by continuing to explore our own neural networks – understanding how humans learn – that we can maximize the use of artificial neural networks to encourage curiosity, stimulate critical thinking, and support learner engagement. In this respect, there are few examples of successful deployments of educational AI programs, although at least we are finally at a point where people are interested in having the conversation.

2. LEARNNOVATORS (THE PRESENT – A): We are now in an era where Generative AI technologies, such as ChatGPT, have become integral to many aspects of workplace learning. As an expert who has closely followed and contributed to this transition, how do you perceive the current state of learning and development in organizations with the integration of AI and neuroscience?

MARGIE MEACHAM: The most obvious integration point is really behind the scenes. Like many other knowledge workers, learning professionals can save a lot of time by leveraging Generative AI to streamline their work. Every formal type of learning needs to be created and shaped by a human. Just think for a moment of all the different types of content that we create as instructional designers and trainers: Slide shows, manuals, quizzes, voice-overs, videos, graphic design, infographics, and so much more. Every one of these work products can be produced faster with the use of Generative AI tools that are already available. And, if you know where to look, many of them are free or very low cost. In fact, many of your readers may find that their organization is already using Generative AI in other departments, and all they have to do is tap into those resources and apply them to a learning experience. Most of my clients are looking for ways to leverage these tools to produce learning materials faster while maintaining or even increasing the overall quality and effectiveness of the learner experience.

When we think of AI as something that learning professionals use, it becomes an effective, time-saving tool. But when we think about AI as something that engages and supports learners, it becomes so much more. So today, there is really no excuse for NOT incorporating AI into our work – both as an effective tool and as a new way to deliver the learning experience.

2. LEARNNOVATORS (THE PRESENT – B): What are the most significant shifts you have observed in learning strategies, content delivery, and learner engagement?

MARGIE MEACHAM: What I see is often a tension between the strategy that the learning leader wants to implement and the strategy that is dictated to them by the organization. I often speak with training managers who are interested in deploying a chatbot, for example. But when they begin to pursue it, they run into difficulty selling the idea to the people who control the money and determine organizational access to technology. For forward-thinking leaders, it often feels as though they are pushing against a wall, making slow progress. This lack of control is one of the reasons why the use of AI in learning design is so far behind its uses in other industries. Sometimes, we need to take a step back and secure funding and sponsorship before we can move forward.

But let’s say that you are in a position to develop a strategy and, within reasonable constraints like time and budget, you are free to implement that strategy as you see fit, as the learning leader for your organization. In those fortunate situations, here’s what I’m seeing:

• More emphasis on skills that are valuable across roles, so that employees become more valuable and more willing to stay with your organization.
• More content delivered at the moment of need, as performance support, rather than trying to teach every skill in advance of when you will need it.
• More emphasis on skills that will be needed in the near future, with an eye to partnering workers with AI “assistants” and “colleagues.”

2. LEARNNOVATORS (THE PRESENT – C): Additionally, how do these advancements align with or challenge the principles of neuroscience in creating effective learning experiences? Could you also share insights or examples from your recent work that illustrate the innovative ways AI is being harnessed to technologies, do you see a need for a ‘Universal Coach’ concept, which envisions a personalized, AI-driven coach for every worker, similar in spirit to Donald Clark’s idea of a ‘Universal Teacher’? If so, how might this concept of a ‘Universal Coach’ shape the future of learning and development in organizations? 

MARGIE MEACHAM: I introduced a similar concept of a Learning Coach in my book, AI in Talent Development in 2020 and built one of the first of these AI-enabled coaches for the United Nations in 2021, a full year before the excitement about ChatGPT really took off. Using a chatbot to communicate directly to each learner, delivering a completely personal experience that moves at the pace each learner needs, challenges learner assumptions, and provides review and reinforcement based on individual milestones and problems completely changes the eLearning experience. For example, we can now build chatbots that:

• Guide each employee through the onboarding experience
• Conduct unique, free-flowing conversations with each learner, instead of a point-and-click delivery or passive video presentation
• Generate learning paths based on individual roles, knowledge, and aspirations
• Deliver “oral exams” that test the depth of knowledge needed to conduct a conversation about a topic
• Deliver scenario-based learning, practice, and exams in a realistic, conversational setting

And so much more!

While it was possible to do all these before Generative AI was available, it was generally costly and time-consuming to build, so these use cases were basically ignored, with few exceptions. What Generative AI did was make entry into these applications much less expensive and much easier to learn and use. Now, with a little basic instruction, anyone can convert tired, out-of-date eLearning into a fresh, engaging, and truly personalized experience with simple AI tools.

3. LEARNNOVATORS (THE FUTURE – A): As we look towards a future where multi-modal Large Language Models (LLMs) are becoming increasingly powerful, potentially even powered by Artificial General Intelligence (AGI), the landscape of workplace learning seems poised for profound changes. How do you foresee these advancements impacting workplace learning?

MARGIE MEACHAM: While I’ve focused my attention on the practical uses of AI to accelerate learning and support performance, I’m keeping a very interested eye on the incremental development of AGI. There are many different definitions of Artificial General Intelligence, and portrayals in the general media are often unrealistic or confusing. The basic concept is simple enough. The people working on AGI want to create what science fiction calls a sentient being. A silicon-based lifeform that feels and thinks just like humans. A few years ago, I was much more skeptical that we would see this in our lifetimes, but now I’m intrigued by the possibilities. Elon Musk believes it will happen very soon, perhaps in just a few years. While I can’t put a timeline on it, I understand how human intelligence evolved, and I recognize that there is a sweeping spectrum of different degrees of forms of intelligence in our animal cousins – and perhaps even in plants and microorganisms. So, as I think about what the workplace might look like in a world with AGI, I see a few possibilities:

1. Emerging sentience that sneaks up on us and causes all sort of ethical, logistical, and moral problems because we weren’t prepared.
2. Learning professionals who understand neural networks and machine learning and plan well for a hybrid world, where robots, chatbots, and other models work side-by-side with humans. These AI team members might be assistants or colleagues, and humans will need to be taught how to interact with them. Leaders will need to know how to manage hybrid teams, and so on.

Since option two is much more attractive, I think we should all accept the fact that AI is going to continue to evolve, and AGI is a distinct possible outcome of that evolutionary path. It is up to us, the human professionals, to be as aware and prepared as possible, and the time to begin is NOW.

3. LEARNNOVATORS (THE FUTURE – B): With LLMs capable of processing and generating not only text but also images, audio, and other forms of data on the fly, what transformations do you anticipate in learning methodologies, content creation, and learner interaction?

MARGIE MEACHAM: The transformation has already begun.

As I mentioned before, content creation and learner interaction are already changing. For example, I just finished this pro bono project for a schoolteacher. She wanted a compelling way for a teacher to give her 4th-grade class about the plight of honeybees and how they could help. I used AI for the entire project. I collaborated with Anthropic’s Claude for the initial outline, created an introductory video with text commands and Pictory, developed a cute little bee avatar with Midjourney, made an interactive game to let students become “bees for a day,” and then taught a chatbot how to ask questions about the content as a final review. By the way, the “bee for a day” idea came from a prompt I gave my Generative AI assistant: “Please design a fun way to learn about bees for 4th-grade students using the content I have uploaded. Provide five suggestions of an interactive game to include in the course.”

So, you see, all these things are available and happening now. And there are courses available to help you learn how to do all these things. Of course, if you don’t understand how the brain learns, you may also need a refresher on the science of human learning, to help you guide your AI assistants and curate their responses into an effective design.

The methodology for generating learning content in the age of AI is what’s missing right now, but that will gradually change. I tell my students that designing with AI uses the same methodologies they already know, like ADDIE and Agile. What’s new is AI-specific knowledge, so that you can apply these methodologies to new tools and new ethical considerations.

3. LEARNNOVATORS (THE FUTURE – C): Furthermore, considering the intersection of these advanced LLMs, AGI, and neuroscience, what new opportunities or challenges might arise for learning professionals in developing and implementing learning experiences that are both innovative and ethically responsible?

MARGIE MEACHAM: This is truly an exciting time to be alive – for those with the courage to embrace the opportunity and disruption that it offers us. Perhaps the simplest opportunity we’re offered is the chance to become more relevant, valuable, and employable than ever before, as learning professionals who are skilled in the use of AI for Talent Development. And, if you want to put it negatively, we all have the opportunity to avoid being replaced by a robot.

But there is another opportunity. I’ve found renewed excitement in my work by embracing AI. It always surprises me. And being surprised is a wonderful way to stay alive. The neurotransmitters in my brain are keeping me interested, challenged, and fulfilled. And I highly recommend it.

3. LEARNNOVATORS (THE FUTURE – D): Finally, in light of these exponentially powerful technologies, do you think we are moving towards ‘The End of Knowing’ as proposed by Professor Sugata Mitra (details available here and here)? If so, how might this concept influence our approach to learning and knowledge acquisition in the future? 

MARGIE MEACHAM: I thoroughly enjoyed these videos and I love these challenging ideas. As a lover of science, it is hard for me to imagine an end to knowing about our world, but perhaps I’m just too literal of a thinker to see it. A line that did resonate with me is when Mitra said that we are currently preparing our children for the past in our educational system. I’d say that is also true for workplace education. A common theme in my book is that much of what most of us consider the future is already happening in the present. So, if we accept this fact, we should be trying to get out of the past and reach our present as soon as possible. Many non-human species are thought to live in a constant state of the present. With AI and neuroscience changing our view of the world on a daily basis, maybe we should try to develop that mindset.

LEARNNOVATORS: Margie, as we conclude this engaging conversation, our second opportunity to benefit from your insights, we extend our sincerest gratitude for the time and expertise you’ve generously shared with us today. Your perspectives have been not only enlightening but also instrumental in deepening our understanding and commitment to continuous learning and innovation in the workplace. Inspired by your contributions, both in this and our previous dialogue, we eagerly anticipate future collaborations and the exciting possibilities they hold. Thank you once again for enriching our series with your invaluable insights!

MARGIE MEACHAM: Thanks so much for your interest in my work! I invite your readers to continue the conversation on LinkedIn and X.

Click HERE to read Season I.

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