THE work PERFORMANCE FIELD:

23 Historical Influences

The history of the work learning-and-performance field tells many important stories—too many to tell here. But still, a short recounting of 23 key influencing ideas will help us imagine what may come next year and the year after—and enable us to be ready for the challenges and opportunities we will face.

This history is simplified to focus on the most important ideas and practices; it is not an exhaustive list. This history also focuses on our industry in aggregate, and likely I’ve missed important personal stories, especially of practitioners who were far ahead of the industry. One final caveat: Given my vantage point embedded in the L&D field in North America, I regret that the perspective here is lacking important international contributions and trends. My apologies for these limitations! I’ve tried to portray a balanced accounting, and I encourage you and others to elaborate on the stories I have missed.

In my efforts to capture major ideas and movements impacting the work that we’ve done and the work that we do now, I landed on the following 23 influencing ideas as well as the benefits and limitations of each idea.

1. Ad Hoc Guidance

Before there was a formal work learning-and-performance field, there were people learning from each other in ad hoc ways: sometimes through observation and modelling, sometimes through coaching, guidance, and answering questions. These are excellent practices, but they are not always sufficient on their own. For example, when tasks are complex, a more scripted and progressive approach is required—such as is seen in apprenticeship. Still, ad hoc guidance should not be dismissed as a thing from the past. People in our organizations today are using these methods spontaneously. We can add value by highlighting opportunities and guiding our fellow employees in best practices.

2. Apprenticeship

The apprenticeship system evolved when task complexity required sustained learning and practice. Apprentices were given progressively more difficult task assignments and were mentored and coached as they progressed to full competence. The term “apprenticeship” may be used less frequently today, but the idea of progressively more challenging tasks is still a vital and powerful practice. Apprenticeship is more difficult as we scale to include large numbers of people in learning and development efforts. Still, we can look to provide progressive challenges in any number of ways—in onboarding, in job rotations, in serious training designs, and so on.

3. Standardization for Large Numbers of Learners

Once organizations began to grow in complexity—and labor markets began to develop—a more organized work learning-and-performance discipline was required. Manuals, classrooms, and chalkboards enabled large numbers of people to be trained at once. This broadcast model had the benefit of information dissemination, but it often suffered from problems of attention fatigue, forgetting, and lack of active responding. It is notable that education followed the same script as we did in training—lecturing to large numbers of students. Despite the limitations of “classroom” practices, there is an economic and limited-resource wisdom to reaching many people at once.

4. Rote Rehearsals

When classroom learning overtaxed the attention of learners, instructors began adding rote rehearsals and quizzing into the experience. These provided benefits of retrieval practice in supporting remembering, but the focus of these activities was too often on low-level knowledge. This created several problems, including knowledge that was isolated from other concepts in memory, and a lack of relevance to work situations. Today, there are few situations where we would use rote rehearsals, though if properly designed—using variety and multiple modalities—repetitions are beneficial in many learning contexts.

5. Time and Motion Studies

In the late 1800s through the early 1900s, folks like Frederick Winslow Taylor and Lillian Gilbreth championed the use of time and motion studies, which broke work actions into their component parts and timed them to create benefits through efficiency and time benchmarks. These efforts ushered in an important new idea—that work, and work situations, could be systematically studied. Unfortunately, time and motion studies led to work system designs that stifled worker judgment and reasoning, while creating dull and monotonous job roles. Still, we would be wise to systematically study work-performance contexts and the way people operate in those situations.

6. Operant Conditioning and Positive Feedback

In the early to mid-20th century, behaviorist researchers, including BF Skinner, advocated for the idea that desired behaviors should be followed by positive rewards, and that tasks could be broken into actions that could be rewarded. While such reward structures can support some types of work—especially where goals are clear and one-dimensional—rewards can also focus action toward inappropriate ends, weaken people’s drive toward intrinsically-rewarding activities, and diminish people’s focus on other important behaviors.

As we learned from decades of research on intrinsic motivation—from researchers like Richard Ryan and Edward Deci — the focus on rewards can be problematic because of all the subtleties involved. For example, rewards act as informational feedback, making it difficult to disambiguate which is important, the reward or the feedback. Despite these issues, there are two very important contributions from this work. First, while we might not want to set up strict operant protocols, we should recognize that people can turn away from situations that aren’t sufficiently rewarding. They may need positive outcomes at least somewhere on their learning journeys to persevere until they reach success. Second, feedback is critical to learning—not just in formal learning, but also in learning from our day-to-day experiences at work and in our lives.

7. Systems Thinking

Systems thinking is a way of observing the world and our organizations—and recognizing that causality is often multifaceted, complex, and interconnected. For us as learning professionals, systems thinking enables us to see that our learners are influenced by organizational systems and subsystems and that learning and remembering are also subject to multiple interdependent factors. One interesting finding called suboptimization, highlighted by renowned economist and operations researcher Charles Hitch, is that optimizing subsystems can lead to poor performance for the larger system. This again highlights the complexity we find in the workplace.

Systems thinking is not a term we use often, but we use systems thinking when we recognize that work contexts matter; that supervisors and coworkers may influence learning application; that we need to satisfy our business stakeholders, not just create the best learning; and so forth. In their book, Conscious Capitalism, John Mackey and Raj Sisodia suggest that “systems intelligence” is a critical skill for today’s leaders. Systems thinking embraces complexity but can also lead to paralysis when we conclude that things are too complex to analyze and comprehend. Despite these worries, systems thinking is aligned with reality—we work in environments of complexity—and we would do well to see both the forest and the trees, learning how to use experimental methods to tease out what works and what doesn’t.

8. The ADDIE Model

The ADDIE Model originated in the 1970s in work done for the U.S. military. It proposes five steps for instructional systems design: Analysis, Design, Development, Implementation, and Evaluation. Often utilized as a waterfall approach—moving from one step to the other without iterating—in the 2000s, ADDIE practices were modified to be iterative and to include evaluation in every step in the process. ADDIE has created benefits in giving learning teams a roadmap for project management. Indeed, it has roots in system engineering from the 1960s, particularly in the phases ascribed to the systems development lifecycle. ADDIE also nicely highlights the benefits of doing full-scale analysis before starting design work.

Yet, ADDIE has two critical limitations. The five steps are project management steps and have no learning wisdom baked into them. We could use the five steps just as easily to plant a garden. Indeed, the original work around ADDIE offered specific learning-related tasks for each step of the process. Many of us focus only on the five labels—Analysis, Design, and so forth—and don’t add wisdom related to learning. This self-imposed limitation is worrisome to the extent that our learning teams may think they are doing good learning development if they just follow the five labels. The second limitation of ADDIE is that when we embrace it as a learning-development methodology, we may end up pushing ourselves toward a training-first solution, which may not be what is needed. The bottom line is that ADDIE without step details is not enough, but the five labels incorporate some notions that we should embrace. We ought to have a good project roadmap, we ought to begin with a serious analysis process, and we ought to utilize evaluation and feedback processes to help us improve our work.

9. Performance Improvement (aka Human Performance Technology)

In the 1960s through the 80s, a major shift occurred in the learning and development field, often attributed to Geary Rummler, Thomas Gilbert, Bob Mager, and other members of ISPI (the International Society of Performance Improvement). These innovators added three critical elements. First, they emphasized that training and development efforts should focus on performance—not learning per se. Second, they recognized that people in organizations are influenced—in a systems thinking way—by forces external to the individual, including incentives, time constraints, resources, etc. Third, they advocated for a performance focus rather than a learning-to-performance focus. Their argument was simple. By focusing on performance first, we will start by examining all the forces impacting work performers—and we will avoid bias toward seeing learning or training as the solution. While these insights are timeless, performance improvement has not been able to fully establish itself as a separate discipline, at least by that name. Instead, it sits heroically and too often underutilized in learning and development—even though its central tenets have been embraced by all, in principle. We would be smart to embrace a performance-first focus in our day-to-day work.

10. Quality and Process Improvement

The quality movement (Total Quality Management, ISO 9000, lean manufacturing, and Six Sigma) emphasized the idea that whole organizations must work together to improve product quality. Aligned with the work of W. Edwards Deming, Joseph Juran, and Kaoru Ishikawa, quality and process improvement focus on minimizing process variability, streamlining processes for efficiency, and empowering employees throughout the organization. Interestingly, despite using sophisticated practices such as statistical process control, this movement has always highlighted the importance of management and the culture of teams and the organization. Geary Rummler built his consultancy around the idea that work processes are a critical target to support organizational improvement. For us in L&D, the quality movement showed us the value of full organizational commitment, a goal we should aspire to today.

11. Prompting Tools (Job Aids, Performance Support, etc.)

In 1991, Gloria Gery published Electronic Performance Support System, a book that changed the learning field. Her critical insight was that not everything had to be trained, and instead some information could be better conveyed through prompting tools like job aids, performance support systems, checklists, and the like. Gery’s work aligned with earlier work by Rummler and Gilbert on “guidance” and Joe Harless on “job aids,” but it was Gery’s book that popularized the whole notion of performance support (which I have labeled “prompting tools” to be more expansive). Gery’s insight also aligns with human cognitive architecture—we know people can’t remember everything and they do better when prompted directly within their performance situations. Prompting tools work great in some circumstances, but too often we don’t prepare people sufficiently to use these tools. Also, prompting tools are not useful in some situations, such as when deeper knowledge is needed to make good decisions, when it is socially awkward to respond with prompting tool in view, or when people are likely to forget they have these tools available. Yet, prompting tools should be considered an important option for most situations.

12. Activity-Based Training Methods

Training and education have often defaulted to a broadcast model, where an instructor broadcasts information, examples, and demonstrations to an audience of learners. In the 1970s and beyond, Sivasailam “Thiagi” Thiagarajan and others began advocating for more interactive training events. Training presentations were augmented with activities, games, role playing, small group discussions, project work, and more. These activities created better engagement and enjoyment, but too often activities were added that fulfilled no learning purpose other than providing a mental break and enjoyment. In my personal conversations with Thiagi and his business partner Matt Richter, they lament that activities are used without a good purpose. Despite the potential for lackadaisical use, activities can provide powerful benefits such as insight, retrieval practice, diverse perspectives, and reinforcement of key points.

13. Andragogy

Andragogy is a learner-centered, humanistic approach to training design. The term was originally coined in Germany in 1833 and developed into a theory of adult education there before being advocated in the United States in the 1960s by Malcolm Knowles. In Knowles’ original formulation, adult learners are fundamentally different than child learners; adults need to be intrinsically motivated, feel autonomous, see the relevance of what they are learning, and given opportunities to learn by doing. Critiques of andragogy have included that the principles are relevant to children too; that autonomy is more useful to those who have deep knowledge and less useful for people with less knowledge; and that not all adults are good at self-motivating themselves to learn. Still, Knowles had a profound impact on our work and andragogical beliefs still carry weight in our work—sometimes for good, sometimes not. We would do well to draw from andragogy’s emphasis on motivation and relevant practice.

14. The Learning Organization

In the 1990s, the concept of the learning organization bubbled up through the work of Peter Senge, Chris Argyris, and others. A learning organization is one that supports the learning of employees in such a way to enable organization-level transformation and improvement. Drawing on ideas from systems thinking, organizational culture, shared visioning, and cognitive psychology, the learning organization idea pushed organizations to focus more on their people and organizational culture aspects of learning and teamwork. This emphasis was beneficial, although sometimes the effort at transformation to a learning organization ignored business realities that were just as important or more important. The first takeaway for us today is that our interventions must recognize the impact of the work culture on the learning process and the work-performance context. Second, where we can go beyond formal learning events, there may be leverage points to enable learning in the flow of work.

15. Simulations for Learning

In the 1980s, “simulation” became a hot topic in learning and development. (I was in the middle of it, as a project manager and simulation architect for the self-described “world leader in business simulation,” the Strategic Management Group in Philadelphia.) Simulations make good sense from a learning perspective. They can provide contextually aligned retrieval practice, spaced repetitions, feedback—all the while being highly engaging and compellingly relevant. The simulation movement eventually gave rise to authoring tools that have enabled us to build branching scenarios, one of the most powerful and value-producing approaches in the L&D arsenal. While simulations are powerful and backed by the science of learning, to be valuable they must emulate—with sufficient cognitive fidelity—the linkage from operative stimuli to decision making. In short, simulations are powerful learning tools to the extent that they truly simulate important contexts and decisions.

16. Learning While Working (aka Informal Learning, Learning in the Workflow)

If the 20th century is marked by a focus on formal learning interventions, the 21st century has ushered in the age of workflow learning. Spurred by the academic work of Karen Watkins and Victoria Marsick and the industry work of Jay Cross, Jos Arets, Charles Jennings, and Vivian Heijnen, these and other innovators began looking for ways that people could learn organically while they worked. This idea—that learning can be improved not just by formal means, but also while people work—is critically important. This is an evolving area, with many weak implementations pretending to be workflow learning. For example, some organizations are providing formal learning in small chunks and calling it learning in the workflow. But lunchtime presentations, microlearning videos, and webinars are not fundamentally different than longer trainings. They are not leveraging the power of the performance context to enable learning. But we shouldn’t be discouraged by these weak attempts. There is likely a goldmine of opportunity in finding learning-in-the-workflow leverage points.

17. User Experience Design

User experience design blossomed in the 2000s with the work of people like Donald Norman, who coined the term “user experience.” User experience (UX) design is a multidisciplinary approach to creating products, processes, services, and events—aiming to create quality user experiences. Critical elements of user UX design include user research, prototyping, and usability testing. One central principle of UX design—often unspoken—is that we as designers can’t assume that we know best; instead, we must seek to learn and test our assumptions every step of the way through unbiased, rigorous testing. This nugget can be lost in drawing from UX design: If we just focus on creating desired experiences and don’t focus on validating their true effectiveness, we will create poor learning designs. This is especially important for the learning field because humans are notoriously imperfect in knowing what works to create effective learning. The discipline of UX design reminds us to research our learners’ work situations, prototype our designs and solutions, and improve our practices based on insights gained through testing.

18. The New Learning Sciences

Enlightened practitioners in learning and development have always sought to incorporate recommendations from learning science into their learning designs. At the same time, there has also been large doses of folk wisdom applied as well—sometimes helpfully, but many times harming learning despite good intentions. Fortunately, starting in the 1970s, learning researchers began compiling highly effective learning methods. These insights began to gain traction in the work-learning field in the 2000s through research translators like Ruth Clark , Will Thalheimer (me), Julie Dirksen, Patti Shank, Clark Quinn, Mirjam Neelen, Karl Kapp, Jane Bozarth, and others—and through popular books such as Make it Stick by researchers Henry Roediger and Mark McDaniel and writer Peter Brown.

These well-researched findings can help learning teams radically improve their learning results. Of course, as with all research, new findings emerge regularly, sometimes causing confusion. Also, research can be taken out of context or misconstrued, so practitioners would be well-advised to seek out the work of research translators before finalizing their design decisions. In addition to learning from the research wisdom, we should also begin to act more like researchers ourselves. Perhaps we don’t have the capacity or resources to do rigorous experiments, but we can find opportunities to do some A-B testing of our work.

19. Learning Technologies and Digital Learning

One of the most important recent changes in the learning field is the successful use of digital learning technologies. While learning technologies have been utilized throughout the last century—from slate boards, to radio, to mimeographs, to laser disks—we have begun a period of exponential improvements through digital and online learning. Sometimes our learning technologies are highly successful, while at other times poor learning designs or technological problems create failed learning experiences. The key to success—as always—is good learning design. We can look forward to continued innovations in learning technology—for example, improvements in online learning that we can’t even imagine today.

20. Performance Consulting

Performance consulting is a practice that aims to help organizations and teams close performance gaps. The recommended process starts by identifying both the current state and the desired future state. From there, the process utilizes a consultative framework to find the causes of the gaps between the current and desired state. Finally, and only afterward, are workable solutions proposed—and training is seen as only one option out of many. The basic process that underlies performance consulting bubbled up from the advances in performance improvement and human-performance technology (1960s-1980s), but it wasn’t until 1995 when Dana and Jim Robinson published their book, Performance Consulting: Moving Beyond Training, that the wider industry began to see performance consulting as a vital tool in the learning-to-performance toolkit.

21. Data Visualization

Data visualization, as the name implies, uses visual methods and elements to bring meaning to collections of data. Data visualization is especially valuable for large and ill-structured datasets. Well-designed visualizations can illuminate hidden patterns, bring focus to fuzzy data, and move us from paralysis to persuasion. One watch-out: Although compelling and sometimes beautiful, data visualizations can push us to forget how we sourced our data, prompt us to ignore data that went ungathered, and nudge us to misunderstand the big picture. Like many things, data visualization is a double-edged sword. If we use it with wisdom, we gain important insights. If we use it poorly, we can create misunderstanding and promote poor decision making.

22. Design Thinking

Design thinking is a process for creating useful innovative products and processes—and is viewed as especially valuable to create breakthrough solutions. Its key components involve empathetic analysis, careful problem framing, bold and expansive ideation, and prototyping. Originating in the 1950s through the work of John E. Arnold, design thinking began gaining traction in the 2000s and 2010s. Design thinking is useful in learning, as exemplified by the book Design Thinking for Training and Development, written by TiER1er Sharon Boller and her co-author Laura Fletcher . One landmine must be circumnavigated. Because we humans are quite often blind to good learning behaviors, efforts to empathize with learners must be done carefully and tested with objective measures of learning. Moreover, we must be careful not to overemphasize learner needs and underemphasize the needs of the organization, coworkers, the community, and environs. Still, design thinking is a powerful tool when we use it to uncover insights and think boldly about possibilities we might not have seen.

23. Online Learning, Online Work, Online Living

Our world as we perceive it is changing radically. More and more we live in a remote cyberworld of online meetings, online commerce, online relationships, and online communities. The COVID pandemic of 2020-2021 accelerated this paradigm shift; necessity being the mother of invention, L&D professionals and others rose to the challenge of making online learning and online work palatable. Major improvements are coming as individuals and organizations build new tools, innovate new strategies, and learn from one another. We are about to enter a golden age of innovation in online living. The possibilities are truly endless. It’s not to say that we as a field won’t take some wrong turns. No one can predict the future, but all of us can have a hand in shaping it going forward.

Going forward—particularly for us as learning and performance professionals—we must keep a few things in mind as we consider how to meet the challenge of online learning. First, and counterintuitively perhaps, we need to recognize that online learning won’t be the only opportunity for innovation as it relates to learning and performance. Classroom training will change. Learning with others will change. Ways of working will change. Learning evaluation will change. The whole ecosystem of learning could radically change—from who supplies learning to who pays for it. We must be open to opportunities, judiciously and with wisdom.

Second, for us to create effective online learning experiences, we are going to have to play the role of explorers. We will need to start a thousand journeys, try things out, and continuously learn—learn by doing, learn from other innovators and innovative ideas, and learn by failing fast (prototyping and improving.

To increase our likelihood of success as we innovate, we need three essential ingredients. We need to draw wisdom from science, wisdom from our own practices, and wisdom from rigorous learning evaluations. Without these ingredients, we will more likely create complacency rather than highly competent performers.

What This History Teaches

The history of the 23 major influences tells two stories. We get a sense of awe at the brilliant work of so many innovators in the work-learning-and-performance field. At the same time, we catch a glimpse of the wondrous complexity of human learning and performance.

From all this, we may develop a renewed humility about how difficult it is to make wise choices in our designs and practices. We may also develop a renewed dedication to our craft, knowing that is up to us to build on this work so we can better serve our learners and our organizations.

The takeaway from our shared history: Without three personal practices—humility, adaptability, and dedication—we will not maximize our contributions to learning and performance.

1. Working with Habits

As humans, we learn quickly that it’s hard to get rid of bad habits and form beneficial new habits. As L&D professionals, we see that some employee work behaviors are difficult to change. Through the research of Wendy Wood, BJ Fogg and others, the science of habit formation and change has really solidified over the last 25 years. Where willpower often fails, habits can be formed by connecting situational cues, actions, and outcomes. Habit formation isn’t easy, often requiring sustained effort over several months—and habits aren’t required for all skills or actions. Nevertheless, where habits are critical to performance, we ought to utilize this relatively new science to support our learners.

2. Performance Triggering

We do not live in a world of abstractions. We live in a world of situations, context, and stimuli. The stimuli we encounter trigger our thoughts and our actions. We see a cute kid, we smile. We see a rat at our feet, we jump. We immerse ourselves in a warm bath embraced in the cozy heat of hot soapy water, we relax. In some ways, the situations we encounter control our thoughts, emotions, and actions—or at least strongly influence them. We are triggered to performance. When behavioral economists report that 80% of our neighbors have enrolled as organ donors, we are triggered to become organ donors ourselves. When traffic engineers narrow our streets, we are triggered to slow down as we drive. When our cafeteria puts fruits and salad at eye level and puts the junk food just above the floor, we are triggered to eat more healthy foods. When we put our tasks on calendars instead of to-do lists, we are triggered more often to do those tasks. The idea of performance triggering, popularized in part through the book Nudge by authors Richard H. Thaler and Cass R. Sunstein, brings new opportunities for us in learning and performance. On the other hand, it may be difficult to gain access to people’s work-performance situations—so we may not always have the leverage to manipulate contextual cues directly. We can, however, enable triggering by helping people influence their own reactions to the stimuli they will encounter.

3. Supporting Remembering

It’s not enough to help our learners fully comprehend some learning content. If they can’t remember what they’ve learned—or can’t remember what to do on the job—we have not done enough. We must do more than create learning; we must minimize forgetting and maximize remembering. Fortunately, there are research-based learning approaches that specifically support long-term remembering. We can provide learners with context-realistic practice and conceptual repetitions spaced over time.

4. Performance Preparation, Inoculation, Resilience

When people have learned successfully—even if they have been primed to remember what they’ve learned—they still must take what they’ve learned and apply it at the right time, in the right context, and in ways that are appropriate. Let’s be real. Some people will hit obstacles, face resistance, or have their motivation wane. Accepting that difficulties are inevitable, there are things we can do to prepare people for the situations they may face, inoculate them to persevere against anticipated difficulties, and enable their resilience.

5. Insight Learning and Creative Ideation

In almost all our L&D efforts, we aim to create transfer learning. That is, we hope that concepts and skills are transferred from some person, program, or repository into our learners. What we hardly ever do is to support our learners in creating their own insights. Fortunately, there is a scientific literature on creativity and creative ideation we can draw from—enabling us to support insight learning, not just transfer learning.

6. Innovative Perseverance

Good ideas are not worth much unless those ideas can be nourished, supported, improved, and embraced by a wider audience. Fortunately, there’s a science and practical playbook for moving from ideas to innovation. We likely have more leverage than we think in being able to create work environments where ideas can become innovations. We should look for opportunities, especially when innovation is critical to our organization’s success.

7. Self-Guided Learning

Some people are brilliant at motivating and guiding their own personal learning and development. Most of us, me included, can do a better job in our own development—by learning how to learn, by getting better tools, or by being in situations and contexts where self-guided learning can happen more easily. We as learning professionals should see where we may leverage our work to support others in learning.

8. Team Leaders as Learning Ambassadors

People who lead teams in organizations—whether they have formal authority as managers or informal influence—are critical in team functioning and individual learning. They help set the learning culture, whether they intend to or not. Many L&D teams already do things to support these team leaders, but certainly, we can do more. The concept of leadership development still resonates as organizations look for ways to maintain and improve productivity, loyalty, motivation, and more. As learning professionals, we should look for ways to improve leadership behaviors—not just through training, but through tools, practices, and other ways to leverage the work-performance context.

9. Going Beyond Conscious Intent

The common assumption is that people learn only when they intend to learn—and they act only when they make conscious decisions to act. However, the truth is more complicated. Much of human behavior is automatic, not involving conscious intent at all. We’ve already seen how we can trigger performance directly, often with little involvement of conscious intent. There are likely other opportunities where we can leverage non-intentional processes for learning and performance.

10. Data Science

Data science comprises many sets of tools and competencies: computation and reporting, algorithms, process simulation, statistical analysis, machine learning, and more. Commerce, business, and communication are all built on data. Learning will not be untouched. Great promise awaits our ability to marshal our learning and performance data. In recent years, using labels like learning analytics or learning engineering, many people are thinking deeply about how to use data to support learning. This will be tricky, however, and we will make mistakes. We may measure what’s easy to measure, not what’s important. We may make decisions based on meaningless data. We may waste time chasing unicorns. We can and will find useful data today, but we must be careful in our work. We should be open and hungry to find or create learning data to guide our work—treading carefully to avoid the traps and trappings mentioned above.

11. Artificial Intelligence

Artificial intelligence (AI) is a diverse field of applications and tools; for our purposes, we can define AI as the ability to use computer systems to look for patterns in large data sets, draw conclusions from that data, and make decisions based on the data. AI has been an evolving field since the 1950s when well-known giants in early computing—Herbert A. Simon, John McCarthy, Marvin Minsky, Allen Newell, and Arthur Samuel—began building AI applications. Progress in AI has grown in spurts as emerging technologies and approaches enabled progress. AI has shown clear benefits in some areas, while being less than effective or harmful in some cases. For example, AI trained on current data often “learns” and uses the same biases that we humans have been prone to utilize. Today, in the learning field, most of us simply don’t have large data sets. Without large data sets, AI is not really feasible, except in mundane applications. Going forward, to gain benefits from AI, we must understand its strengths and limitations, and circumnavigate the problems it can bring. Still, someday soon it is likely that AI will have profound impacts on learning, but not just learning! Indeed, before AI is significant in learning, it is likely to improve people’s workplace tools—for example email, scheduling, and personal productivity tools. AI may not be one of our main competencies today, but we should remain open and prepare ourselves for the opportunities it will bring.

12. Virtual and Augmented Reality

Virtual reality (VR) immerses us in simulated situations. Augmented reality (AR) helps us interact with our actual reality by enhancing our perceptions of that reality. Today’s most common augmentations include visual overlays of information or objects, but auditory and kinesthetic stimuli may one day provide benefits as well. VR has great potential to help us create learning simulations—although the costs are likely to remain high and may be suitable only for critical or highly utilized learning needs. AR has great potential to help us support people directly in their work performance contexts. In short, VR and AR will certainly impact the learning and performance field, and we should be open to opportunities and innovations provided by these technologies.

13. Improved Learning Evaluation

Another opportunity for us in learning and performance is improving our evaluations. We make a ton of mistakes in the way we measure learning. The once dominant Kirkpatrick-Katzell Four-Level evaluation model mashes all types of learning outcomes into one bucket: Regurgitating trivia is learning. So is recall of meaningful knowledge. So is decision-making competence. So is a full demonstration of task competence. Unfortunately, what happens in practice is that we measure what is easy to measure, not what is most meaningful to measure. We provide knowledge checks, but by doing this, we fool ourselves into thinking we’ve created learning—when we may have only created a momentary change in knowledge. Fortunately, and I admit bias of authorship, there is a new evaluation model, the Learning-Transfer Evaluation Model (LTEM). The LTEM framework is valuable because it not only guides us to better learning evaluations but also nudges us to better learning strategies and designs. LTEM is just one way to improve learning evaluation. If we can improve our ability to gather valid information, we will be able to create virtuous cycles of continuous improvement—and this will create a cascade of changes in our work.

14. Social Responsibility

Another force arising outside L&D is an emphasis on social responsibility. Organizations are focusing more on responsibility to all stakeholders, not just shareholders, as they seek to reduce pollutants, minimize environmental disruption, source from sustainable materials, reduce energy use, support the health and safety of employees and communities, avoid bias in hiring and promotions, and more. Even corporate interests are advocating against a singular focus on profits. The Business Roundtable, a group of large-company CEOs, have issued their Statement on the Purpose of a Corporation, that it should benefit the interests of a wide range of stakeholders, including “customers, employees, suppliers, communities and shareholders.” In 2021 Larry Fink, CEO of Blackrock, the world’s largest investment company, said they would stop supporting company managers whose companies aren’t taking actions aligned with environmental sustainability. He essentially argued in his letter to CEOs that investment risks were too high for companies that were not taking the climate crisis into account. It appears that momentum is growing for organizations to take drastic steps to realign all their practices around sustainability and social responsibility. We in L&D will be asked to do our part. In the learning and performance field, Roger Kauffman was an advocate for L&D professionals to look beyond business results when we do learning evaluations. LTEM, my evaluation model, specifically directs us to consider measuring the impact of learning on “learners, coworkers/family/friends, organization, community, society, and the environs.” These quiet admonitions have mostly been ignored, but perhaps the need for us to move more forcefully is upon us.

15. Increased Professionalization

There’s a quiet movement in the work learning-and-performance field. More of us are using evidence-informed practices, utilizing learning evaluations to create feedback loops that enable cycles of improvement, and creating proactive processes rather than being order takers. We are beginning to be driven by professional principles and practices, rather than just following recipes and directives. It won’t happen overnight, but we are readying ourselves to have a different type of relationship with senior organizational leaders—more of a partnership based on the respect we earn as a professional field. As we move in this direction, we will have more leverage to do our work and more reach into the day-to-day workplace—enabling us to be more effective in supporting work performance.

These Forces of Change Put into Context

It’s impossible to know which of these 15 forces of change are most important, how they might combine in unique and unforeseen ways, or what factors may be missing. Nevertheless, a new focus seems to be emerging from this list—a focus on the work-performance context. Where we have historically focused on creating instruction, prompting tools (like job aids), and learning experiences, these 15 forces suggest an important new set of opportunities: if only we focus more on the experience of people in their work-performance situations.

Most of the 15 influences are set in, or target, work-performance situations:

• Habit formation requires some connection to situational cues—as found in work-performance contexts.
• Performance triggering involves manipulating work-performance stimuli or the way they are interpreted and action-planned.
• Remembering can be a general goal but can also target specific work-performance contexts.
• Insight learning is relevant not in the abstract, but when related to real work issues.
• Creative ideation and perseverance in innovation are similar—they are relevant to specific work opportunities.
• Self-guided learning is relevant to people’s lives and their work, but our efforts will tend to focus on their work performance.
• Team leaders lead at work.
• Automatic unintentional thinking happens everywhere but can be leveraged in work situations.
• AI will have its earliest impact in people’s work tools.
• VR may help us build performance-focused simulations. AR may help us support people in their work contexts.
• Improved evaluation approaches will ensure that learning is performance focused.
• The push for social responsibility initiatives in organizations will, hopefully, influence the way works gets done.
• With increased professionalism, we will have more leverage to reach into work situations to support behavior change more fully.

These examples demonstrate how the work-performance context is central to behavior change and improvement. And more urgently, the examples tell a story—that these areas of influence are impacting us today and will impact us even more tomorrow as enlightened organizations utilize them to competitive advantage. These forces of change are laying the groundwork for the future of the learning-and-performance field; we would do well to begin using these new opportunities for the benefit of our learners, organizations, and others we serve.

Like any new endeavor, this shift will not be simple. We will have to innovate new methods, while utilizing and improving methods suggested from research and from others’ practical efforts. Still, it does suggest a new opportunity for us and a new direction—and maybe a new name for what we do.

As professionals, we help other people do their best work and we help organizations thrive. By so doing, in our way, we help make the world a better place.

With this as our mantle, we cannot afford to get stuck in small battles. We must focus on making ourselves as productive and effective as possible. In this article, I hoped to celebrate the innovators and innovative ideas that brought us to today, while also urging us to be open to the next wave of opportunity. The best of us have brilliantly mastered our roles as trainers, instructional designers, and learning experience designers—but now the next vanguard is leveraging the performance experience context.

Perhaps it would be helpful to talk about the evolution of our field using the common—if a bit cliché—method of MS-DOS labeling, i.e.:

• L&D 1.0 – Training
• L&D 2.0 – Instructional Design
• L&D 3.0 – Learning Experience Design
• L&D 4.0 – Performance Experience Design

I’m ambivalent about this labeling, and I leave it to you and others in the field to decide whether this is useful. It seems that, as a field, learning and development (L&D) would really need a new label, something more focused on performance. (Indeed, throughout this document we refer to the “work-performance” context and the “work learning-and-performance field.”)

Wherever we are now, whatever the label we land on, let us begin the journey toward more effectiveness today!

Acknowledgements

As with all things, others have made significant contributions. Let me first thank the many innovators who have come before us—some mentioned in this article, but hundreds who were not. Let me also thank today’s innovators who are pushing us to new conceptions of what is possible.

Special thanks to Jerry Hamburg, my TiER1 colleague and performance-savant who has been passionately waving the flag on these issues for years! Thank you to Jerry along with Zac Ryland and Josiah Holland of TiER1 for providing wonderfully illuminating feedback on an earlier draft, helping me improve this article immeasurably. Special thanks to Kaitrin McCoy of TiER1 who led the effort to edit and improve my words and the structure of this work. Amazing improvements, Kaitrin! Thanks also to TiER1ers Carlina Scalf, Katie Coburn, and Sarah Ehrnschwender for substantive suggestions for improvement!

Also, thanks to the whole TiER1 team who have made it easy for me to jump in, feel valued, be supported, and be encouraged to explore and learn and push things forward—like in this article.

Gratitude to the following collaborators:

• to Guy Wallace—amateur historian of all things “human performance technology”—who answered my many questions with patience and precision, schooled me bigtime on the history of our field, and pointed me to some important articles on his HPT Treasures website.
• to Julie Dirksen, who enlightened me on several of the historical forces reviewed in this article.
• to Matt Richter for providing feedback and helping me see several historical forces I had missed.
• to Alex Salas for his work compiling research on the history of ADDIE—without which I would not have known that ADDIE adherents worked to break the overreliance on the waterfall approach in the early 2000s.
• to Carl Binder for hosting a great library of articles that enabled me to walk through some important history
• to Clark Quinn for talking with me about his thoughts on the term Performance Experience Design and more generally on the goal of performance improvement.
• to Jackie Crofts for her beautiful illustrations accompanying this article and embedded in our workshop slides.

Finally, let me thank you in advance, if you can, for sharing your thoughts and reflections with me and with the public—so we can learn together.