CHAPTER 56

Emerging Technology and the Future of Learning

Karl Kapp and Jessica Briskin

The futurist William Gibson said, “The future is already here; it’s just not very evenly distributed yet.” This statement has particular relevance for learning and development professionals because our task is to make learning as efficient and effective as possible. However, continually changing and evolving technologies mean that we need to constantly evaluate and, possibly, change how we are delivering instruction.

If they’re applied properly, we can use emerging technologies to push boundaries and facilitate learning in ways and places that were never possible. If they’re misapplied, on the other hand, we risk wasting time, money, and goodwill chasing the “next big thing.”

Decades ago, field technicians had to know as much as possible about servicing any device they might encounter in the field, which meant hours of training and lugging around massive technical handbooks to help solve problems. As mobile devices became more ubiquitous, mobile learning became possible. The advent of mobile learning, as well as widely available Wi-Fi and microlearning practices, allows field technicians to have massive amounts of training, troubleshooting tips, and content available at their fingertips. They can simply pull up an appropriate troubleshooting video and be walked, step by step, through a piece of equipment they may never have seen before. In the not-so-distant past, allowing technicians to pull up a short video and troubleshoot problems was unheard of—new technology has made it commonplace. This is just one example of how technology has changed learning and development for good.

These technologies can be effective, but they must be adopted carefully. Successful L&D organizations, both large and small, integrate emerging learning technologies into a solid, well-designed program. They do not adopt technologies just because they are new; instead, they adopt emerging technologies because they lead to improved learning outcomes and enhanced performance for the organization.

While it is important for L&D professionals to stay up to date with emerging technologies, they also need to keep a healthy dose of skepticism or risk being seduced by the latest technology. Intelligent and careful use of learning technologies leads to success; haphazard use of and randomly adopting the newest technology leads to confusion and cost overruns.

RISSCI Factors

As we discuss emerging technologies, it’s good to have a list of criteria you can use to determine if the technology is worth adopting. Whether you are part of a large, medium, or small organization, the RISSCI (pronounced risky) method of technology assessment is useful for deciding if a new technology makes sense for your learning initiatives.

The RISSCI methodology looks at six dimensions of new technology—reach, insight, safety, scalability, compatibility, and innovation—and compares them with existing methods of delivering the instruction. If the factors in the RISSCI method outweigh existing approaches, the new technology should be considered for adoption. Let’s look closer at each dimension.

Reach

Does the technology allow the training to reach areas, people, or levels of sophistication that were not possible previously? The use of mobile technologies to deliver content to technicians in the field is a good example of reach. Mobile technology made it possible for training to reach areas where it could not previously.

Insight

Does the technology provide insights into learner performance or behavior that are not available using existing methods? For example, learners can play physical card games and report results, but determining which cards were used, how much time a player spent looking at a card, and what cards were sorted and unsorted by the learner are all difficult to track. However, suppose the card game is digitized. In that case, the system can track every learner’s action and use those actions to provide information about the learning process and learner’s decisions while interacting with the digitized cards.

Safety

Does the technology allow skills to be practiced in a safer manner than currently available methods? An example here would be using virtual reality to allow a worker to explore the inside of a chlorine tank in the safety of a computer-generated environment without any real danger.

Scalability

Does the technology allow training to scale or to be constantly available? For example, does it use a chatbot to answer basic questions about troubleshooting a piece of equipment? The chatbot can respond at any time and never gets frustrated about answering questions.

Compatibility

Is the technology compatible with your current systems, the organization’s culture, and the desired learning goals? An example would be replacing a sales representative’s desktop computer with a tablet or laptop. These people are busy and lead hectic lives, and lugging around a large computer is cumbersome. A tablet is lightweight, can process a great deal of information, and fits into the representative’s workflow.

Innovation

Does the emerging technology truly provide a new and unique way of solving an organizational problem? This is the dimension that helps prevent chasing a “shiny new object.” If the technology is not solving a problem more effectively or efficiently than previous methods, it might not make sense to adopt it. For example, it could use virtual reality to simply mimic the inside of a classroom. The technology has so much potential, but if that’s all you’re doing with it, there are likely better ways to mimic the existing physical classroom.

Overview of Emerging Technologies

It is impossible to review all emerging technologies that might influence the learning and development field in the next 10 to 20 years. In fact, we have already seen sudden, successful applications of technology that we didn’t even initially consider using as learning tools.

The technologies outlined in this chapter were chosen based on their potential for wide-scale impact on learning and development. In addition, many of these technologies are already being used on the fringes of the industry, and we predict they will emerge as valuable tools for our field.

Augmented Reality (AR)

AR is when a computer superimposes images, sound, or items onto the real world. A person holds up a smartphone, puts on glasses, or looks through a car’s windshield and can see items that are not really there but appear to be because of the technology. This causes the reality the person is experiencing to become enhanced, made better, or provide more information. A well-known example is the augmented reality game Pokémon Go. The game uses a smartphone to superimpose an image of a Pokémon onto an actual physical space, like a shopping mall or park.

Augmented reality is already used in factories to help shop-floor workers properly assemble parts and access refresher information when needed quickly. Many factories use glasses that provide AR elements, such as directions for picking materials, information about what materials to pick to fill an order, or the correct internal running temperature of a piece of equipment. Because the glasses are hands free, the workers won’t need to carry items with them and can use both hands on a task while still receiving instructions and information.

In addition, AR can provide location-based information to your smartphone or other device. This could allow you to learn about landmarks or items in your physical environment, such as a statue or building, simply by looking at them through the phone.

Augmented reality provides information that is easily and consistently available and is helping improve employee performance because it gives employees the information they need when they need it. AR’s performance support focus uses instructions and heads-up displays to assist the employee by providing step-by-step instructions for how to perform their job.

Virtual Reality (VR)

Virtual reality (VR) is when a learner is immersed completely in a world or scene simulated by a computer and feels like they’re interacting with it in a real or physical way thanks to special wearable electronic equipment—for example, using goggles with a screen or gloves fitted with sensors that give physical feedback to the wearer.

When a learner “goes into” VR, they put on a headset and are no longer in touch with reality. The only thing they can see is the computer-generated virtual environment displayed on the screen in front of them. Thus, it is a purely virtual experience without any intent to integrate with the real world.

The military uses VR to help war fighters practice their craft and hone their skills in a safe environment. Additionally, they can network the VR environment to allow multiple trainees to interact at the same time. This allows them to practice group maneuvers and gain experience working together under pressure, without putting them in actual danger.

VR environments have also been used to teach bank tellers how to remain calm in a robbery situation or to teach inclusion and diversity topics by immersing the learner in an environment that safely exposes their unconscious biases.

An ideal VR learning experience should create engaging interactions that help the learner assimilate new information, ideas, and concepts, resulting in new transferable skills. VR can be used to teach all types of activities, such as how to run meetings, assemble items, empathize with others, or conduct a sales call. IT tends to have a training or learning focus because users are immersed in a safe but realistic environment.

Mixed Reality (MR)

Mixed reality represents the convergence of different realities—the continuum from the real setting to a completely artificial environment (VR). One example of how this might work is mixing VR and AR. Imagine being fully immersed in a VR environment when, all of a sudden, the goggles’ built-in cameras show your reality through a heads-up display (a form of AR) presenting data about a particular object. This is mixed reality—blending VR, AR, and the physical world.

Another example of MR would be an instructor giving learners a piece of equipment to examine and investigate. If the equipment had codes that a smartphone could read and display more information about, that’s a form of AR. So, the learner could touch and see the actual piece of equipment and then use AR to learn more about it. The classroom might also have VR goggles learners could don to see how the equipment is used in the actual manufacturing setting. This is an example of mixing several types of realities.

Extended Reality (XR)

Labeling these categories of reality is cumbersome and constantly changes—this is where our final term, extended reality (XR), comes into play. XR was created to be an all-encompassing term developers could use to address all experiences, regardless of reality. The X represents any term placed in front of the word reality. As technology continues to evolve, more terms will inevitably arise to describe various forms of computer-enhanced environments.

Deep Fakes

In a deep fake video, a person’s likeness, mannerisms, and voice are manipulated to make it appear they are saying something or doing something they never did. Recent technological advances and specialized techniques have made deep fakes highly realistic, complex, and more challenging to distinguish from the actual video.

While fake news and false statement attributions have been around since the dawn of time, the ability and technology necessary to create a convincing, realistic fake video are increasingly available to the masses. Over the next few years, it is predicted that the concept of deep fake videos will be perfected.

Simultaneously, another related development that’s occurring is the creation of “artificial humans.” Many companies are actively working to combine artificial intelligence with photo-realistic images. Their goal is to create a computer that mimics human-like interactions in a highly realistic fashion by combining natural speech, natural eye movement, realistic gestures, and a natural appearance.

For an L&D professional, this means being able to orchestrate a cast of thousands. You will be able to create the perfect cast by entering a description, facial expressions, gender, race, and so on. Type actions and scenes into the program, and the artificial humans will play out the scene in much the same way actual humans will. When you need to update clothes, hairstyles, and other elements that age quickly in a video, you’ll simply type in a few commands, upload a few clothing styles, and have a brand-new, refreshed video.

Don’t have an entire video, just pictures of a person in multiple poses and locations? No problem. Type in what you need and the location, screen capture the image, and import it into your e-learning module.

Suppose you need your organization’s CEO to make an impactful statement about diversity and inclusion or a critical safety initiative. No problem—just record a few moments of her talking and then let the deep fake technology finish her speech. You never have to do more than one take. If it’s wrong, you can fix it in post-production. This will dramatically reduce production costs and decrease the time it will take to create training videos.

As magical and ideal as this may sound, deep fakes also come with a caution. You need to recognize the powerful control that could be used inappropriately to, for example, make your CEO deliver messages without their knowledge or consent.

Virtual Worlds

Virtual worlds are three-dimensional environments in which you can interact with others and create objects as part of that interaction. Virtual worlds are most familiar in their use in multiplayer online video games, but a range of enterprises are beginning to adopt them for day-to-day applications. Examples of virtual worlds include multiuser virtual environments (for example, World of Warcraft) and virtual learning environments (like Second Life and Active Worlds; Girvan 2018). Most virtual worlds are populated by avatars representing players and virtual representations of virtual world characters. They can mimic environments learners are familiar with or include completely different inhabitants and rules of nature (such as people who can fly).

For an L&D professional, the virtual world can supplement traditional face-to-face training and replace uninspiring two-dimensional presentations. Constructing a virtual world that includes AR/VR technology is becoming more popular because it creates a fully immersive training experience that enables facilitators to see a participant’s authentic, natural reactions to scenarios. Learners can be tested and observed on a range of soft skills, including empathy, communication, collaboration, innovation, and continuous improvement. For example, in a leadership training program, managers can privately and without risk practice giving performance evaluations, delivering bad news, and dealing with difficult employees. They can experiment in the virtual world with different leadership approaches to see how they can lead more effectively in the real world. Learners experience the what, how, and why of the initiatives, making learning exciting and compelling (Greene 2018).

Chatbots

A chatbot is a computer program designed to simulate conversations with humans through websites, mobile apps, wearable devices, or home appliances (Lee 2019). Chatbots are powerful tools for performance support because when employees ask the bot questions it returns possible answers. In L&D, chatbots have been rising in popularity.

Learners can use chatbots for just-in-time training support thanks to their ability to find information across multiple sources. Chatbots can be used as performance support by anticipating questions, proactively pushing out performance support material, and further refining and personalizing the information for individual users. The ability to provide consistent answers is helpful, because they are available 24/7. Additionally, chatbots can be used to scale personalized coaching and mentoring sessions, and they help learners feel less judged when they ask questions and seek clarifications (Lee 2019).

Chatbots can create individualized learning experiences and support performance. Most important, chatbots are not just delivering learning content; they also provide information about how people learn and what they need to learn. They record data (and conversations) from their interactions, which can be analyzed to see what people are learning and when. This allows learning to become a continuous process rather than an episodic event for L&D.

It is important to remember, however, that chatbots cannot replace humans; they can only work alongside humans to enhance the overall workplace learning experience. Humans converse in a way that chatbots cannot; we incorporate emotional intelligence in our decision-making process, understand context, and make connections or draw inferences.

Artificial Intelligence

Artificial intelligence (AI) refers to machines performing tasks that require or use a human-like intelligence or that humans would otherwise perform. This behavior is built on algorithms, which are a set of rules or processes that an AI-powered machine uses to guide its performance of a task (Belhassen and Hogle 2020). It’s also possible to set up AI to recommend content that similar learners (or those in the same job role or who are enrolled in the same courses) have also completed or to find and explore content libraries.

AI can gauge an individual’s ability and progression, tailoring course content based on the results assessments and shortening the learning process by suggesting only the specific modules the employee needs to improve the skills they need for their job. Furthermore, AI can predict whether a learner will correctly answer their assessment questions based on their behaviors, cognition, and engagement. For an L&D professional creating digital content, AI can generate narration from the text (that is, text-to-speech). For example, authoring tools like Storyline can autogenerate narration in various “voices,” which can reduce the cost and logistics of recording narration.

It is predicted that the combination of AI within VR will create realistic, dynamic characters that speak in more natural ways. This would help make scenarios more real; for example, generating environments and characters that appear and move more naturally increases the realism of immersive games and simulations. Learning games that incorporate machine learning (which is discussed in the next section) and adaptive learning could adjust as players learn and advance, including dynamic character development, changes in the content, and challenges based on their performance and feedback. These games could offer a unique, targeted experience to each learner (Belhassen and Hogle 2020).

For AI to be utilized fully, organizations need to harness vast amounts of data, which is typically done through the organization’s learning management system (LMS). L&D departments can use this data to gain insights into the learner journey and create training programs that drive value and promote adaptive learning. Breakthroughs like this are changing L&D. AI is transforming how learning content is delivered, leading to greater alignment with business values.

Machine Learning

Machine learning (ML) provides data to a computer, which uses that information to analyze future data. ML is a type of artificial intelligence that automates data processing using algorithms without necessitating new programs (Gold, Nichol, and Harrison 2020). It is often used in web searches, email spam filters, marketing personalization, product recommendations, and chatbots.

In L&D specifically, ML can be used to solve challenges in analytics and reporting, feedback and assessment, and personalization (Walsh 2019):

•  Enhance analytics and reporting. Companies can use this information to develop reports on the effectiveness and return on investment of learning within an organization. This helps learners, trainers, and the organization better understand how learning functions in the organization to help identify trends and take protective action (for example, by supporting learners at risk of not completing a course). Organizations can also use this data to improve employee retention by identifying development needs and proactively supporting employees (Walsh 2019).

•  Provide dynamic and effective assessment strategies. For example, ML helps create more intuitive, intelligent tests and quizzes. Some systems can even automatically formulate appropriate test questions in reaction to learner activity. L&D can use this information to determine skills gaps.

•  Provide personalization and recommend learning resources. ML can support adaptive learning, which is the personalization of learning experiences through computer-based technology. ML interprets vast amounts of data to match appropriate content for learners’ needs based on their past learning experiences and assessments (Gold, Nichol, and Harrison 2020). This allows for more targeted planning and development using learning behaviors, performance indicators, and emerging patterns to personalize online training.

Adaptive Learning

Adaptive learning technologies are computer-based e-learning systems that alter the sequence, difficulty, or nature of the material in response to a learner’s performance or responses. In addition, many adaptive learning technology systems capture data—such as the length of time hovering over an answer or the time it takes to automatically respond—and use it to adjust the content shared with the learner. This is also known as personalized learning.

One mistake made with early e-learning programs was that we didn’t take advantage of the computer’s ability to deliver different content to different people based on their inputs. We simply took what was done in the classroom and modified it for online learning. Fortunately, designers and vendors are starting to realize that they can save time and accelerate learning by first diagnosing what a person knows when they log in to an e-learning course and then only delivering content based on what the learner doesn’t know.

Adaptive learning can be done simply by providing different levels of content, but it will eventually progress to an adaptive learning presentation through the learning process. This will require a detailed breakdown of content and learning outcomes into enabling objectives from the design perspective.

Digitization of Analog

There is a growing movement toward taking traditional face-to-face activities commonly found in workshops or classroom settings and converting them into a digital version. While the trend accelerated during the COVID-19 pandemic, it was already under way before the pandemic began.

Many activities have become digitized. You will find links to each of these digital tools on the handbook website, ATDHandbook3.org. These activities and examples of their associated tools include:

•  Digitizing the physical act of placing sticky notes on a whiteboard in a conference room (Miro, Padlet, and Stormboard).

•  Digitization of raising your hand and interacting in a classroom with audience response tools (Kahoot and Poll Everywhere).

•  Digitization of traditional card games for sorting competencies, conducting role plays, or including other learning activities. The goal of digital card games is to give the learners the feeling of sitting around a virtual card table—they still draw, discard, and sort cards like they would in a physical game (Enterprise Game Stack).

Digitization of the traditional tools used in the face-to-face classroom experience will only continue to grow and diversify. This will allow digital experiences to become closer and closer to actual physical experiences. It also means that live and virtual learning experiences can be designed to mimic more traditional in-person experiences. It is possible to brainstorm sessions with a virtual whiteboard, conduct a role-playing game with virtual cards, or even run a business simulation using a virtual board game.

Final Thoughts

What can you do as an L&D professional? We’ve included a tool to help you measure the viability of implementing an emerging technology by using the RISSCI methodology. This assessment—which is available on the handbook website, ATDHandbook3.org—can help you to determine if the technology is appropriate for your L&D organization.

There has been a shift in how technology is influencing the way L&D professionals design learning and training. With new advances in augmented reality, virtual reality, mixed reality, extended reality, deep fakes, virtual worlds, chatbots, artificial intelligence, machine learning, adaptive learning, and digitization of analog, technology is transforming the training industry at an accelerated pace.

Organizations that embrace these technologies will see dramatic results. With advances in technology, an extensive number of options, and not-unlimited budgets, L&D departments must determine what technology is best for their needs. Technology has the power to redefine work, performance standards, and leadership responsibilities over the next decade. Leveraging the right emerging technology will not only improve employee performance and affect skill development but also save time and money.

About the Authors

Karl Kapp, EdD, is a professor of instructional technology at Bloomsburg University, in Bloomsburg, Pennsylvania, where he teaches instructional game design, gamification classes, and online learning design. He keeps busy with both academic and more corporate pursuits. Karl is a senior researcher on a grant sponsored by the National Institutes of Health that involves applying microlearning and gamification to help childcare workers identify child abuse. He is co-founder of Enterprise Game Stack, a company that created a digital card game tool for instructional designers. Karl keeps busy writing and researching; he has authored or co-authored eight books, including The Gamification of Learning and Instruction and Microlearning: Short and Sweet. His current passion project is creating a series on YouTube called “The Unauthorized, Unofficial History of Learning Games.” He is also a LinkedIn Learning author and has created several courses on the platform. You can reach Karl at [email protected].

Jessica Briskin, PhD, is an assistant professor and a graduate coordinator in the department of instructional technology at Bloomsburg University, in Bloomsburg, Pennsylvania. She teaches e-learning and multimedia development courses, authoring tools, visual design for learning, and online learning. Her research focuses on design frameworks, online collaboration methods, and mobile and multimedia development regarding translating learning spaces into online spaces. Jessica has experience in corporate and educational industries, designing and developing e-learning and m-learning courses, instructor-led training, videos, infographics, and performance support tools. Her doctorate in learning, design, and technology is from the Pennsylvania State University.

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