top of page


9 items found for ""

  • The Future of Learning: Experience

    A 1997 report from Idaho claimed that thousands of Americans have died from accidental ingestion of dihydrogen monoxide (DHMO), a chemical compound that can cause severe burns and other unpleasant side effects. Asking what can be done about this dangerous substance, a 14-year-old student distributed the report to classmates, prompting a vote which resulted in overwhelming agreement to ban DHMO. The punch line? None of the students stopped to consider what dihydrogen monoxide was. Ironically enough, they chose to ban something that is critical to our survival! Turns out, DHMO is simply two molecules of hydrogen to one molecule of oxygen, also known as H2O or water (which can, of course, cause death from drowning, dehydration, or scalds!) The lesson? Trust, but verify. In the age of misinformation, social media overload, and an onslaught of endless news, people need more time, space, and increasingly sophisticated ‘thinking’ capabilities to critically consider the information they’re consuming. And, with AI bots generating content, creating deep fakes, and learning people’s behavior patterns, the noise we experience is only going to get louder. What can be done to help? Develop higher cognitive skills. The World Economic Forum (WEF) recognizes these skills as the most important for individuals to learn. L&Ds can play a crucial role in the essential upskilling of workers by prioritizing capability development and enabling individuals to better navigate the complexities of the future. And let's face it, the pressure to keep up with the latest skills and deliver value is not going away anytime soon. In today’s environment, focusing on developing higher cognitive skills is vital for two reasons. First, reasoning and decision-making are currently the least automated workplace tasks, accounting for just 26% of task automation. Second, higher cognitive skills help individuals cut through the noise of information overload. Noise refers to the constant influx of information and distractions that can cloud our judgment and hinder our ability to think and communicate clearly. In both our personal and professional lives, this can be observed in countless situations, whether it be overwhelm by choice at a supermarket, being bombarded by Slack messages, or mindlessly scrolling social media. By developing higher cognitive skills, we can be more discriminating, elevate decision-making, and better navigate this noisy world. Here comes the harsh reality…As AI advancement intensifies, so does the noise, stress, and pressure. Did you know that according to Nobel laureate Daniel Kahneman, our brain operates using two distinct systems? System 1 is like a fast and automatic brain, responsible for making quick judgments and decisions, while System 2 is like a slower and more analytical brain, which analyzes information and considers solutions. We tend to rely more on System 1 thinking, which can lead us to accept information at face value without much critical thinking. It's important to be aware of this tendency and to consciously engage, develop, and enhance our System 2 thinking so that we are prepared to make important decisions or evaluate information critically. Here comes a glimmer of hope. Decades of research in mental models have shown that the most effective way to cultivate higher cognitive skills is through practice and reflection. We can enhance our innate System 2 thinking abilities by practicing in a practical context that allows us to apply what we have learned. This helps craft new neural pathways and refine our abilities. Plus, it exposes us to different scenarios and challenges, enabling us to adapt and improve our problem-solving skills. And, when we take the time to reflect on our experiences, we gain valuable insights into what worked well and what needs improvement. Reflection enables us to analyze our performance, identify patterns, and recognize opportunities to enhance our understanding or approach, but it also helps us connect the dots, integrate new information, and extract meaningful lessons from our experiences. And, here comes the great news! Experiential learning provides an ideal framework for the practice and reflection individuals need to stay ahead of the game. By engaging directly in real-world experiences, learners apply their knowledge and skills, receive immediate feedback, and reflect on their actions. The combination of hands-on experience with thoughtful reflection maximizes the effectiveness of the learning process, promotes more profound understanding, and enhances the transferability of knowledge and skills to real-life situations. With experiential learning, education’s future is bright, and the key to giving individuals the skills they need to quiet the noise. And you, my L&D friends, play a vital role in making practice and reflection an everyday reality.

  • The AI Advocate for Learning and Development

    AI is quickly becoming commonplace, and its pervasiveness will only grow. Boosted by AI, stores, transportation, and homes will become smarter, and most apps and applications will employ AI to some degree. It’s only a matter of time before kids' toys join the AI bandwagon and begin teaching us a thing or two. Resistance to AI in organizations prohibiting its use is temporary, as giants like Microsoft and Google lead the way in integrating AI into everyday productivity suites and tools. Following suit, every app and application developer is furiously looking for ways to integrate AI into their products and offerings - making its ubiquitous integration inevitable. Enter the need for an AI Advocate - the champion who supports and promotes AI’s responsible and productive use of AI technology throughout an organization. These AI ambassadors inspire and educate on AI's potential while keeping ethics and data privacy firmly in mind. Their mission is to harness the power of AI to elevate organizational performance and decision-making without compromising on standards, policies, and regulations. What does this role of the AI Advocate in Learning and Development? As an AI Advocate, key responsibilities include: Education: One primary responsibility is to clearly communicate the benefits, risks, ethics, and potential applications of AI technologies to a diverse range of stakeholders. AI Opportunity Identification and Support: Stay up-to-date with the latest trends and developments in AI technology in order to collaborate with various teams to identify opportunities for AI implementation, fostering innovation and problem-solving. Monitoring AI technology: Regularly monitor and assess the functionality and effectiveness of deployed AI systems, recommending any necessary improvements. Cross-Functional Collaboration: Work closely with departments such as IT, legal, and data privacy to ensure AI initiatives align with the organization's goals, policies, and values. Training and support: Facilitate AI-related training for teams across the organization on responsible and effective use of AI tools. Offer ongoing support and address inquiries or concerns that arise. Why is this role essential now? The IBM AI Adoption Index increased from around 31% in 2021 to 35% in 2022 and is expected to reach 52% in 2023. The global AI market is predicted to exceed $1.5 trillion by 2030, with a Compound Annual Growth Rate (CAGR) of 38.1% from 2022 to 2030 (SnapLogic). This growth is largely a result of organizations striving for a competitive edge, with productivity being a primary driver. By offloading tedious, repetitive, and routine tasks to AI, employees can focus on higher-value tasks, leading to higher satisfaction rates, with 68% wanting more AI-based technology in the workplace. AI is also being explored to enhance customer experience, drive sales growth, replace knowledge-level training, optimize supply chains, and reduce staff costs. Consequently, the urgent need for a dedicated AI advocate is critical to empowering organizations to harness the power of AI and maintain a competitive position in the market. Where does this role sit within the organization? The AI advocate role should report to a senior leader within the organization, such as the Chief Technology Officer (CTO) or Chief Information Officer (CIO), to ensure strategic alignment and access to comprehensive support and guidance. Key reasons for this reporting structure include: Technical expertise: Working closely with a senior leader with technical expertise, such as the CTO or CIO, will provide the AI Advocate with technical support and resources, aiding in the effectiveness of their role. Strategic alignment: Aligning the AI Advocate's responsibilities with the organization's strategic goals is crucial. Reporting to a senior leader guarantees that their work remains consistent with the company's vision and mission. Cross-functional collaboration: The implementation of AI technology calls for cooperation among various departments, such as IT, legal, and data privacy. The AI Advocate will benefit from the senior leader's oversight, fostering collaboration across these diverse functions. Budgetary support: Deploying AI technology can entail significant costs. Reporting to a high-level executive will help the AI Advocate secure the necessary budgetary support to effectively execute their role. Why is the AI advocate essential to L&D? An AI advocate is crucial for L&D teams, as they can lead the way in utilizing the vast, sophisticated AI tools at their disposal to accelerate the design and development of training programs, minimize costs connected to subject matter experts, elevate the learner's experience and efficacy, and transform L&D into a data-driven entity. In essence, the AI advocate can establish L&D as a beacon that directs the organization toward ethical AI usage, while simultaneously maintaining a competitive edge in the market. Here are some additional resources: Fast Company: Why every Fortune 500 business needs a chief AI officer: . Worklife: The rise of the chief AI officer . Venture Beat: How to choose the right Chief AI Officer.

  • How AI and Simulations will change L&D.

    The learning industry is going through the most significant shift since the Netscape browser was introduced in 1994. Advances in AI natural language processing models, such as ChatGPT, gives “knowledge at your fingertips” an entirely new meaning. When you need to fix something, improve team performance, or understand a complex topic, AI-enabled digital personal agents will have it ready for you within minutes. AI is ready What’s different about AI now than a few years ago? Finally, AI models are trained on enough data to make what they generate useful. For example, GPT-3, a neural network machine learning model by OpenAI, is trained on 175 billion parameters, significantly outperforming prior models trained on only 10- 20 billion parameters. This massive amount of training helps make AI-enabled apps, like ChatGPT, smarter and produce more human-like text. Unlike a search engine, where you weed through links to articles, videos, and websites, the AI generates curated content. What’s even more remarkable? The AI is teachable, learning what you like, how you like it, and when you want it. Imagine any topic that you’re interested in learning more about, and waking up to an AI-generated course just for you. For example, suppose you want to take a deep dive into history, learn the steps to perform a specific job, or how to become more self-aware, customized learning is just a click away. AI and Simulations Simulations have long been the most effective way of learning. For decades, pilots, astronauts, and business leaders have used simulations to practice complex skills and navigate evolving situations. Despite their proven efficacy, the drawback has always been that simulations are expensive, time-consuming, and labor-intensive to create. Fortunately, this is no longer the case. With AI-enabled simulation authoring tools, simulations can be produced faster than eLearning. The marriage of AI content generation and simulation authoring tools is set to revolutionize the industry, making the most effective and fun forms of learning accessible to everyone. So, how will AI and simulations change adult learning? Here are ten predictions: The days of eLearning catalogs are numbered. Today, learners go to sites like LinkedIn Learning, Udemy, or Skillsoft to watch reams of videos, consume pages of eLearning, or are constrained to a predetermined agenda. This will rapidly change. AI apps that generate just-in-time learning and content will deal a significant blow to this traditional catalog model. Why pay for off-the-shelf when you can learn what you want, when you want, by asking an AI app? Skill practice (upskilling/reskilling) will continue to gain momentum. Similar to eLearning and just-in-time learning, most AI-enabled technologies are only able to provide knowledge-level content that adds to an individual’s knowledge base. While good for building foundational skills, the training is not enough to upskill a global workforce. Therefore, to upskill and reskill with greater impact and speed, organizations will look for more experiential ways of learning - providing practice and application that helps employees grow their skills and careers. Content is no longer king; experience is. Creating a seamless and engaging customer or user experience has been the central focus in many industries this last decade. It’s finally hitting the learning industry. When considering the efficacy that simulations have shown in studies, and combining that with AI, then learners have an experience that is far better than any other online learning - and that’s game-changing. Future of work, meets the future of learning. Imagine acquiring the skills you need without ever getting the ‘real-world’ experience. Similar to how an airline pilot practices (learn, fail, repeat) in different situations in a flight simulator, an aspiring employee or leader can log hours in a skill simulation to gain the practice and coaching they need to perform a function or role. Massive university disruption is here. AI-enabled learning apps, content, and instruction - add this to the discontent many young people feel toward costly universities and you have a university system poised for massive disruption. The saving grace will be experiential learning. The universities that truly ‘flip the classroom’ by leaving knowledge-level training to AI, and making in-person time an emotionally charged practice using skill simulations, will not only survive but thrive. Minimize biases, widen perspectives. We all have biases, and when designing courses or writing content, those biases filter and shape what is included. With AI, you’ll be able to ask it to provide multiple perspectives spanning several generations on any topic. This content can be loaded into a simulation to challenge the learner to explore alternative perspectives. And, it can point out where the learner may exhibit certain behavioral tendencies in certain situations - now that’s insightful. One tool covers all learning needs. It has been costly for organizations to learn, create, and maintain multiple platforms that generate eLearnings, just-in-time learnings, tutors, and skill simulations. Now, with AI-enabled simulation authoring tools, they can switch to one tool, which covers all of their experiential learning needs, eliminating the burdensome requirement of varied platforms and solutions. Democratization of experiential learning. Due to the cost, high-end experiential learning has only been available to large corporations. However, AI-enabled simulation tools will continue to drive the cost down, allowing any organization or university to have access to the best learning experiences available. In the near future, even high school students will learn through simulations (and their personal AI teacher). People will actually like compliance training. That may be a stretch… but the days of people clicking through boring eLearnings on one screen while working on another are ending. With AI-enabled skill simulations, compliance training will be engaging - learners will want to take the training as it will captivate their hearts and minds, not just feed them reams of content. Behavioral analytics finally join the party. Behavioral data provides insights into how people think, how their thinking changes, and how their thinking aligns with others. Yet, training organizations have been the slowest to embrace analytics. One of the main reasons is that most delivered training lacks the design and technology required to capture useful learner data. Fortunately, simulations capture a massive amount of data. By leveraging analytics tools and AI, the data becomes practical for the learner and invaluable for the organization.

  • AI vs. Human: How do ‘we’ ensure our relevance

    As technology continues to transform industries and the human-AI convergence rapidly gains speed, it’s becoming increasingly apparent that we must hone in on what makes us individually unique and leverage innate abilities to maintain relevance. Luckily, there are several uniquely human skills that AI, hopefully, will never be allowed to develop: Emotional intelligence: Humans can understand, express, and regulate emotions, as well as perceive and respond to the feelings of others in ways that AI cannot. Complex problem-solving: Humans can solve complex and ambiguous problems that require reasoning, judgment, and decision-making based on context, intuition, and experience. AI is helpful in decision-making, but is limited by the availability of large amounts of referenceable data. Interpersonal communication: Humans can communicate with others in a nuanced and empathetic way, using body language, tone, and nonverbal cues, which AI cannot yet fully grasp. Adapting to new situations: Humans can adapt to new and unfamiliar situations by applying their knowledge, experience, and creativity in ways AI struggles to do. Judgment and decision-making: Humans can make judgments and decisions based on ethical, moral, and social considerations that are difficult for AI to ‘reason’. Empathy: Humans can empathize with others, showing understanding, compassion, and concern for their feelings and experiences, all emotions that are unattainable by AI. Situational awareness: Humans can understand the context of a situation and adjust their behavior accordingly, whereas AI struggles to understand the context. Leadership and teamwork: Humans can lead and work effectively in teams by building relationships, managing conflict, inspiring others, and adapting to the team's needs. AI is a solo show, incapable of leading or interpersonally connecting. Strategic thinking: Humans can analyze complex situations, make connections, and develop long-term strategies; AI is undoubtedly helpful in analyzing data and identifying patterns and trends, but it lacks the creativity and intuition needed for strategic thinking. Creativity: Humans can think creatively and develop original ideas; AI responds to a prompt and is only as creative as the data it's sourced from. Not only is this list great news but it points us directly to a “relevance recipe.” Essentially, if humans can build and enhance these imperative skills, they can ensure relevancy. Now that we have our list of ingredients, how do we go about sourcing and strengthening them? If you ask ChatGPT, it says the most effective way to learn new skills is to practice. However, humans know that not all practice is the same, nor equally productive. Effective practice requires a safe environment that gradually becomes more challenging and evolves based on the individual's decisions and results. Learners need to receive targeted feedback along the way, reflect on their performance, and then be given the chance to apply their learning. This is called Experiential Learning. Experiential learning is a process of learning by doing. It involves all the elements effective practice requires: taking action, reflecting on the experience, and receiving feedback, all within a contextually relevant, safe environment. Rather than just reading or listening to information, learners actively engage in the learning process. They can take risks, make mistakes, and experiment with new ways of thinking. Learners improve their skills and gain self-confidence by reflecting on their mistakes and adjusting their actions. This active engagement in learning creates "muscle memory," increasing the likelihood of applying the new skill effectively on the job and in daily life. The increasing prevalence of technology and AI has highlighted the importance of developing uniquely human skills. Ironically enough, thanks to AI, we now have a cost-effective way to develop experiential learning programs to strengthen and build these uniquely human-skills. AI has driven down development costs, making the most effective form of learning widely accessible. Previously, creating simulations for experiential learning was expensive, challenging to deploy, and difficult to adapt. But, with the help of AI and new authoring platforms, such as SimGate, experiential learning can now be created as quickly as eLearning and at a cost similar to VILT. Aiding humans in developing essential skills and confidence is critical to navigating the future of work. Amidst the excitement and uncertainty of the great human-AI convergence, it’s comforting to know that we have the key to the “relevance recipe”: experiential learning.

  • Use AI to amplify your superpowers.

    Have you ever considered enhancing your natural abilities and strengthening them for greater success? Or perhaps you're curious about how AI can help you tackle challenging tasks, freeing up time to focus on your unique strengths and what you do best? As our workplaces continue to evolve, it's becoming increasingly important to explore AI’s potential to amplify our capabilities and experiment with tools that can augment our skillsets. Created specifically for learning and development professionals, The Thinking Effect is a site dedicated to providing valuable AI tool recommendations + ratings, informative blog posts + videos, and a variety of curated resources to help you leverage AI in your work and personal life. Whether you're new to AI or an experienced user, The Thinking Effect can help you unlock your full potential. As human beings, we're incredibly skilled at many things and we possess unique strengths that set us apart. Some are artistically gifted, while others excel in math, sports, sales, or leadership. Some enjoy solitary work, while others thrive within teams. But let's face it, we all have limitations and could use a boost to excel in specific tasks that once seemed insurmountable. What if we could bypass common obstacles by developing our abilities in synergy with today's most advanced technologies? Imagine taking those innate talents and amplifying them even further with the help of AI. Or have the AI take care of tasks that robs you of the precious time needed to tap into your strengths and unleash your full individual potential. The result? Your individual superpowers. So, how can you use AI to amplify your superpowers? Here are a few ways: Enhance your creativity If you're an artist or designer, leverage AI tools like generative adversarial networks (GAN) to create unique and compelling visuals or a natural language processing (NLP) tool to generate compelling stories or narratives. Improve your athletic performance. Athletes use AI to analyze their movements and optimize their training routines. Wearable technology like smartwatches and fitness trackers collect heart rate, speed, and distance data, which AI uses to analyze and identify areas to increase your athletic performance. Boost your productivity AI-powered personal assistants help you manage your schedule, set reminders, and automate routine tasks like sending emails and scheduling appointments. They can even sythesize large amounts of data to help you make important decisions and provide recommendations based on your preferences. Sharpen your problem-solving skills AI-powered chatbots and virtual assistants quickly answer complex questions, helping you solve problems more efficiently. Additionally, you can use AI-powered tools to identify patterns and evaluate data to help inform your decisions. Expand your knowledge Discover new books, music, and movies based on your interests and preferences through AI-powered recommendation engines. You can also find information on any topic quickly and easily with AI-powered search engines like Bing. From simple tasks to more complex assignments, AI has proven itself to be an invaluable tool for enhancing performance and amplifing our natural abilities. By harnessing the power of AI, we can become more creative, productive, and efficient, ultimately leading to greater success and fulfillment in our personal and professional lives. So, don't be afraid to explore the fascinating and revolutionizing world of AI to unleash your own superpowers. Start that journey now - check out The Thinking Effect.

  • Neural Coding System: Framework for improving thinking and behaving

    Our underlying driver as a learning community is to create positive and lasting behavior change that helps people and, ultimately, organizations perform and treat one another better. Therefore, we aim to have learners transfer what they learn to their lives, careers, and communities. “Transfer,” to the training professional, is the Holy Grail. It refers to how much of what is learned within the learning arena can be applied back to the workplace. We define two levels of transfer: Situational transfer occurs when learners can apply what they learned to similar situations. Adaptive transfer occurs when learners can adapt what they have learned to a variety of situations. There is another dimension to transfer, which we call capacity. Capacity correlates with the amount of information that an individual retains and is capable of applying after the learning program. If, for example, an individual can apply many newly learned skills to different situations, then the program is said to have a high adaptive transfer capacity. Part of the goal for training, then, is to increase each learner’s capacity for transfer. Organizations that are trying to get the most out of their employees should note that learning that has a high adaptive transfer capacity is the most desirable for improving each worker’s value potential. When it comes to developing how-to-think workers, this level of transfer is also imperative. With this in mind, my colleagues and I have spent a considerable amount of time and resources rethinking traditional principles and evaluating assessment techniques focused on improving experiential learning. As a result, we created the Neural Coding System (NCS). Our underlying philosophy for developing the NCS is that the training must cause learners to stop and think, reevaluate their mental models, and reach their own insights into how to modify their own thinking or behavior. It must balance learning and practice, leaving students the opportunity to fill gaps and reinforce new skills. It must provide informative and relevant feedback regarding their limiting beliefs within dynamic and complex systems. And, it must be fun and not a waste of time. Neural coding is a neuroscience-related field concerned with how sensory and other information is represented in the brain by networks of neurons. Neural coding describes the process of neural network formation in the brain in response to a stimulus. The formation of these neural networks determines how people respond to future stimuli. With repetition of any stimuli and response, neural pathways are etched deeply and become the default “programming” for how people behave or respond to similar types of stimuli. As noted earlier, this is the neurological basis for habits and mental models. Neural Coding System Self-generated insights The NCS is a design framework that consists of four cognitive conditions that create an optimal learning environment for developing how-to-think workers when the conditions exist together. It is not a step-by-step methodology or series of discrete events. Rather, it is an interconnected system of mental conditions that are created through the artful implementation of various design principles. As the image in Figure 27 illustrates, the NCS is more like a funnel. Simulation participants are placed in the middle of new situations that are evolving in response to their decisions and actions. This spiral approach, moving between the various conditions, is critical to engaging workers as they evaluate their mental models and seek to resolve gaps. Through trial-and-error and reflective dialogue, this approach allows them to work toward that sudden moment of convergence. In summary, insights create motivation and, in turn, the energy necessary to grow and improve.

  • Engage System 2 Thinking: How to improve debriefs and increase awareness and retention

    Drive the company into bankruptcy? No problem. Fuel mass angst among your subordinates with your take-no-prisoners style of leadership? No worries. Hit “reset.” But no matter how complex, how flashy, how mind-boggling a learning may be, one of the most powerful learning experiences comes from debriefs. A well-facilitated debrief is crucial to bringing about change, whether the goal is increasing strategic alignment or optimizing talent. A well-led debrief has an all-important goal: help participants become aware of their mental models — deeply rooted beliefs, assumptions or behaviors that may limit their performance. We all have mental models, but often we don’t even think about them. But deeply rooted beliefs are just that: deeply rooted. It takes skill to expose those roots, let alone understand them, untangle them, and adjust them so that they are healthy and helpful rather than a hindrance. When leading a debrief, it’s important to have a clear understanding of how to engage deeper thinking in order to expose and improve mental models. It’s helpful to understand that the human brain can be seen as functioning with two different systems. Nobel Prize winner Daniel Kahneman, in his book “Thinking Fast and Slow,” calls them System 1 (fast) and System 2 (slow). He describes System 1 as our “automatic” brain. It’s the part that allows us to react instinctively to situations and questions. Think about driving: Have you ever become engrossed in thought, only to realize that you are several blocks (and traffic lights) down the road — and you are in awe how you got there? That was your System 1 brain in action, doing what it knows how to do thanks to plenty of repetition. System 2, on the other hand, is the kind of thinking that requires the “deep” brain and/or long-term memory. A simple example involves math. If you are asked to give the answer to 2+2, you’ll barely pause before giving an answer (that is System 1 at work again). But what if you are asked to give the answer to 17x19? More than likely, that will require pencil and paper — if you don’t have access to the calculator. In order to find that answer, System 2 thinking must be engaged; there’s no “automatic” answer that jumps to mind. The process of pausing and engaging in the “slow” thinking that Kahneman describes allows the brain to access information that is not commonly used in everyday life. He also talks about how System 2 thinking is harder work; it can actually be tiring. These two systems are important because an effective learning experience includes a balance of questions and experiences that call on both System 1 and System 2 thinking. Often it can be a challenge to engage System 2. Because it is more taxing, the human brain often defaults to System 1 responses. But with skillful help, System 2 can be encouraged to monitor System 1, creating new, analytical insights where old assumptions try to hold fast. In the debrief setting, it is vital that participants engage System 2 in order to come to controlled, deliberate reflection. One of the best ways to do this is to ask questions that tend to trigger System 2 thinking. Questions that get a leaner to think about their thinking would fall into to the realm of System 2 questions. For example, here are a few pertaining to performing tasks yourself or delegating them. What was the rationale behind your decision? What are the similarities between your experience and the simulation decision? Are there any differences? Let’s assume your personal experience didn’t exist; how would you make this decision? Has anyone on the team had a similar real-world experience? What was the outcome? If you were to face the consequences of this decision without the ability to decide, how would you react? Imagine your decision led to a complete failure. What were the causes of failure? How would you communicate this decision to your leadership? To the client? Was there disagreement when making this decision? If so, what was it and how did you resolve the conflict? What are your predictions regarding the outcomes of this decisions? Any additional consequences? What kind of feelings did you experience while making this decision? What kind of feelings did you experience once you got the answer? How would you apply this decision to a different project, scenario, or client? Throughout the process, when did you feel the most comfortable/confident? When were you the most frustrated? The important thing to remember is that debriefs can be the linchpin of a meaningful, lasting learning experience. So, thinking through these questions and creating the space for learners to process them is critical.

  • The Trifecta that Unlocks Great Experiential Learning

    Think about one course that changed your life. How did it make you feel, think, and act differently? If you had to put it into a few words, how would you describe that great learning? Typically, it’s hard for people to narrow life-changing experiences down to a word or two. More often, in response, we hear emotionally rich stories such as: I learned a lot about myself; how my thinking and style impact and inhibit my strengths. My learnings and self-commitments came in handy this morning during a client conversation - I found myself much more effective and skilled in areas I usually stumble. We pulled together. The decisions we needed to make were so realistic that many perspectives surfaced - many were things I’ve never thought about, and I’ve been doing this role for a long time. I feel like we created a meaningful friendship, and that will go a long way in helping each of us cope in these turbulent times. It [simulation] started simple, and then it wasn’t. Decisions had tradeoffs and long-term consequences, and often there was no clear path forward. The wonderful thing is we learned from each other, we built on each other’s experiences, and as a result, we finished strong. What made these learning experiences so influential? If we examine these personal descriptions and others like them, they reveal a direct connection to three powerful learning strategies: Reflective, Social, and Generative learning. We call them the trifecta of great learning. Why? Because when properly designed into a training experience, it creates learning conditions which are life-changing for many participants. Reflective Learning. The first participant description above illustrates reflective learning. This involves more than just looking back on what’s been done or said and how it made you feel. Rather, Reflective Learning is an active process of identifying patterns in thinking, understanding their impacts, and outlining changes to improve future outcomes. This essential strategy helps individuals become aware of limiting mindsets and beliefs so that they can transform them into ways of thinking that serve themselves and others. Imagine working with a team of individuals who took the time to reflect on their behaviors and actively explore more inclusive and productive ways of thinking? Social Learning. The second participant description above describes what many people are desperately looking for - healthy, educational, and social connections. Quite frankly, the last few years have taken their toll, and the need to make learning social is critical to reducing attrition and improving engagement. Going a step further, from a neuroscience perspective, social learning has several more benefits. It activates the brain’s reward system, which reinforces learning and encourages us to repeat behaviors associated with positive outcomes. Learning socially also enhances the formation of long-term memories, making it more likely that they will be able to perform a behavior in the future. All fancy words aside, making learning more fun and engaging is just the right thing to do. Generative Learning. Lastly, the third participant description above exhibits an active process of connecting and constructing innovative ideas during a team-based learning. For the instructional designers reading this, generative learning is based on the constructivism theory. Many essential skills are practiced when teams generate ideas while working on problems together - asking questions, seeking out information, making connections, and thinking systematically and critically. The best part, like social learning, is that multiple brain regions are activated, leading to improved retention and retrieval. Participants learn, retrain, and apply more through practicing together. Reflective, social, and generative learning makes that possible through team-based experiential learning programs. Similar to great athletic teams, individuals become better through learning from other each, growing their perspectives, and evaluating results together. This virtuous cycle not only lifts the performance of the entire team, but participants also have more fun -- something we could all use more of.

  • Making sense of AI-enabled tools in L&D

    AI is here… and in a big way. Seven years ago, Sundar Pichai, CEO of Google claimed "We are moving from a mobile-first to an AI-first world" and he couldn't have been more right. Its rapid expansion and mass-scale adoption is today’s hottest topic and its momentum continues to gain speed. AI is quickly being integrated into apps and applications, making software personal and reaching mainstream audiences where it matters to them most - on an individual level. From personalized help with daily activities and Google assistants, to increasing productivity with Microsoft Office and tools that create bespoke learning content, AI’s impact is evident and its role in the future of learning and development will be game-changing. Yet, in the midst of rapid change and opportunity, how do we begin to comprehend it all? This article aims to help learning and development professionals not only make sense of AI-enabled tools and their potential in the L&D space, but also to share our ever-expanding compilation of “AI-enabled tools for L&D” so you can accelerate your design and development of learning content and improve your learner experiences. By the way, we continually update this list and recently added a new tab with some cool personal productivity tools. Feel free to bookmark it to get regular updates! Types of L&D AI-enabled tools Our list includes the following types of tools and how they help build experiential learning: Content tools accelerate the creation of content (text, images, videos, podcasts, animations, graphics) to create learning materials. Optimization tools straddle content creation and user experience. Experience tools make the learner experience more engaging and effective. Content tools This category is where we have seen the most growth. We anticipate that the number of tools within this category will increase significantly, making it hard to know where to focus resources or budgets. Here’s our take on a few types of tools: Image creation tools - Many tools create amazing images based on a simple prompt (e.g., /imagine two diverse professionals having a heated discussion near a water cooler) and their capabilities have come a long way. Our favorite, MidJourney, generates beautiful images and provides several options you can add to your learning content. AI video tools - There are three types of AI video tools: tools used to improve video and audio quality, tools that are used to create video scripts by analyzing provided photos and images, and tools that read a script and produce videos complete with images, music, and text animation. From our tests, the first two types of tools are solid. Improved video quality is fantastic and videos from photos have been available for some time now, so the tech is good. The text-to-video tools still need work. Some cool things you can do to enhance video quality include changing a video by editing the text script; you simply delete filler words, and the AI will regenerate the video. We believe the most helpful AI video tool for L&D today is You provide the script, and it generates a realistic avatar. We found that avatars are a cost-effective technique for introducing storylines, giving feedback, serving as guides, and setting up situations. Content creation tools - As you can imagine, with OpenAI and soon LaMDA (Language Model for Dialogue Applications from Google), the number of tools used to create content for blogs, websites, articles, homework, and learning are exploding. What we’ve found with tools like ChatGPT is the quality of the content it generates depends on the thoughtfulness of the prompt it is given. With a good prompt (a natural language description that acts as input for the AI), the tools do a great job generating creative content for writing cases, scenarios, or storylines. If you’re looking for referenceable and factual content, we highly recommend having a subject matter expert review what it creates. Regardless of creative or referenceable content, we see considerable cost savings by streamlining the costly and time-consuming efforts of back and forth iterations. One of the best tools in this category is a tool we (The Regis Company) created called ReX Co-Pilot. This isn't available for sale as a standalone offering, but we’d love to show you a demo of how it produces everything from assessments to eLearning and sim content, while working in concert with our SimGate design platform. Optimization tools This is a crossover category, which includes AI-enabled tools that analyze data to create or optimize the learner experience. Currently, there are only a few tools in the category, but I predict we will see some great examples by mid-year, with Learning Management Systems (LMS) and Learning Experience Platforms (LXP) vendors leading the way. Personalized learning - This has been talked about for a long time and implemented in different forms with varied success. Now, with the assistance of AI, large data sets for each learner can be analyzed and used to generate customized learning paths and significantly advance just-in-time or in-the-flow-of-work learning. ‘Large data sets’ are the imperative, as truly personalized learning, and I would argue good adaptive and just-in-time learning, requires that more data be captured as learners engage in learning. For this reason, we (The Regis Company) have made intentional advances in Behavioral Analytics. In every simulation we create, we capture all the decisions, rationale, branches, resources, and perspectives learners select. This data serves as the basis for personalized learning, adaptive learning, and just-in-time learning. Another great tool that captures practical data is from ETU, who offers video-branching simulations. Experience tools This category of tools are great for enhancing the learner experience. As with optimization tools, they’re limited, but we anticipate some really cool apps coming from Google and others in the near future. We’ll keep you posted, but here are a few to expect. Text-to-speed and Translation tools - We are excited about these types of AI-enabled tools because they both improve the learner’s experience and keeps costs down. For example, having a text-to-speech icon anywhere content is presented and when clicked, reads the text to the learner in their language of choice. Equally cool is having AI translators convert screen text on-the-fly to another language, providing context and more culturally relevant examples. One of our favorite text-to-speech tools is from Eleven Labs. Not only does it not sound robotic, but it also mimics other voices (e.g., Steve Jobs) by providing it a short sample. It’s a bit freaky too. Coaching assistants - These are going to be (not ready for prime time in our opinion) great. AI coaches can challenge the learner to think more deeply with Socratic questions by responding to text entries, open-ended responses, and decision rationale. For instance, we (Regis) created a prototype which we trained the AI to help learners think through the ‘human’ way to fire an employee. We anticipate Google Bard revolutionizing AI assistants and coaches, so keep your eye out for their upcoming public release. Conclusion…for now AI’s impact on L&D is just getting started and no doubt new tools are coming. In fact, we believe AI-enabled apps could see the same growth this year as when the App stores were open for business in 2008. So, don’t forget to continually visit our AI-enabled tools for L&D list as we’ll be on top of these updates and want to share them with you. And, please let us know if you have any tools to add, or suggestions for another category. In addition, if you want to schedule a meeting to learn more or set up a lunch-and-learn for your team please let us know.

bottom of page