A Systems Approach to Addressing Human Challenges

Abstract

In Chap. 2, this chapter discusses the nuances of complex systems, accentuating that human communications and technology are integral parts of our social systems. Whenever humans are positioned at the centre of any system, complexity emerges. Interventions in complex systems are very difficult, due to feedback loops and their associated time delays and the presence of many interacting components. Each element within a complex system intertwines with others, making any intervention a precarious act, where intervening in a part of the system invariably affects other parts. Engineering education is a complex system, and it is evolving under the new AI technologies. Interventions are required and the relational dynamics among students, as well as between students and educators, are critical axes around which educational experiences must revolve. The dynamic educational ecosystems, burgeoning with myriad informal and formal, verbal and non-verbal communications,shape the educational experience and outcomes in profound ways. Ensuring that interventions do not inadvertently disrupt or diminish these interactional ecosystems requires an adept understanding of the inherent complexity embedded within the educational systems.

In Chap. 2, this chapter discusses the nuances of complex systems, accentuating that human communications and technology are integral parts of our social systems. Whenever humans are positioned at the centre of any system, complexity emerges. Interventions in complex systems are very difficult, due to feedback loops and their associated time delays and the presence of many interacting components. Each element within a complex system intertwines with others, making any intervention a precarious act, where intervening in a part of the system invariably affects other parts. Engineering education is a complex system, and it is evolving under the new AI technologies. Interventions are required and the relational dynamics among students, as well as between students and educators, are critical axes around which educational experiences must revolve. The dynamic educational ecosystems, burgeoning with myriad informal and formal, verbal and non-verbal communications,shape the educational experience and outcomes in profound ways. Ensuring that interventions do not inadvertently disrupt or diminish these interactional ecosystems requires an adept understanding of the inherent complexity embedded within the educational systems.

1 Investigating Challenges as Systems

The twentieth century ushered in many social challenges, which continue to test our intelligence and creativity. The UN work on categorizing these challenges is perhaps the most comprehensive list of these challenges. The Strategic Development Goals list provides elaborate details of these challenges, which are mostly inherited from the past. Ubiquitous digital technologies enlarge this list and put humanity at an inflection point of unprecedented threats.

Innovation causes rapid changes and vast disruptions, which affect every aspect of our lives. New technologies empower us with more efficiency, capacity to manage large data, which can create transparency and facilitate integration among physical, biological, and digital domains. But these advances are redefining what it means to be human.

Human challenges are multicomponents and multivariable functions and have many interactions. When we take a close examination of these components, we can create lists of their constituencies, but we rarely translate our lists to connections among them. If we did, we usually use a simple cause-and-effect logic. This approach simplifies the issues and may lead to actions that exaggerate, rather than reduce, the challenge. It is well recognized that a systems approach is needed to understand and mitigate these challenges.

Humans have an intuitive sense of systems, and over thousands of years, we built complicated physical systems with many components and connections, such as transport or water distributions. Seven thousand years ago, the Sumerians built dams and aqueducts on the Tigris and the Euphrates rivers (Helbaek, 1972) and they had a sense of their system. Their system boundary included roads and a network of irrigation spaces. Much later the Romans built amazing aqueducts (De Feo et al., 2011) and transportation networks (Hitchner, 2012). These early systems functioned very well and were sensitive to sustainability. They provide good insights into how early work considered human needs within nature and its limited resources.

Although there is a long history of systems thinking, there is a much shorter history of systems theory. Systems theory was formally defined by Von Bertalanffy in his General Systems Theory book (Von Bertalanffy, 1950). This work initiated the development of systems-oriented research in disciplines (Francois, 2004; International Institute for General Systems Studies, 2001; Midgley, 2002) and later work linked theories with implementation methods (Cabrera & Cabrera, 2015).

Systems theory invites a sophisticated level of classification and methods for addressing the different types of systems. In our childhood, we thought of events in cause-and-effect relationship, and we rarely challenge such assumptions. Most of us are linear thinkers, and our examination paradigm has limited tools. Interestingly, when we notice the connectivity among elements of an issue, we are not terribly surprised, but we brush their interactions aside focusing on some issues that we can solve quickly (Fig. 2.1). When we examine the SDGs, we cannot afford to do that, as every goal is part of the quest for a sustainable life, by staying within planetary boundaries (Costanza et al., 2016).

Fig. 2.1

Cause and effect—one event-oriented challenges

Paradigms are very powerful human constructs (Kuhn, 1970). They last for a long time and offer a sense of security and assurance. Often the human brain gets stuck in one of them with no place to escape. We need to shed the one cause-and-effect paradigm and move into a systems paradigm. The paradigm of interacting elements of nonlinear dependencies, requiring multiple tools to examine its elements, is the enabler to understand human challenges and finding mitigations. Indeed, our world is made up of many connections which create challenging systems (Fig. 2.2).

Fig. 2.2

Several interacting causes lead to an effect, which is part of the challenge

The number and types of these systems increased with the addition of the digital innovations that augmented our physical and mental capacities. Making decisions with the help of machines is relatively new to us, but it has been anticipated for a long time. Now we have additional tools to find mitigations and solutions for the plethora of systems issues that consist of older challenges inlaid with digital technologies.

Investigating systems requires using several tools or lenses. These lenses are part of our social-mental construct, and they represent different notions related to sustainability, socioeconomics, ethical, cultural, legal, business, health, and political issues, among many other lenses. This makes for a long list of interacting items and studying them requires a methodology, which we will discuss next.

2 Defining Systems

Systems are mental constructs, and some are nature-made. Every system is bounded by space and time, influenced by its environment, defined by its structure and purpose, and expressed through its function. Each system has a number of elements. These elements can be human-made, hardware and software artifacts, and nature made of physical and biological elements.

Systems can be organized hierarchically according to their type of interactions and may have categorical combinations like natural systems, man-nature systems, man–machine system, or machine-machine system. Typically, these systems are not isolated and there might be significant interactions between these hierarchies. These interactions lead to dynamics that may not be stationary and change in time.

Thus, system dynamics are manifestations of how the different components—being physical/chemical, virtual/symbolic, mental/cognitive, ecological, sociological, and biological—may exist, organized, and communicate within a system and with the outside environment.

3 Closed and Open Systems

Closed systems are perhaps the least interesting, and closed social systems are rare. A closed system does not exchange energy or communicate with the outside. There are some examples where the system is isolated and has only an internal flow. Most systems are open systems.

Open systems maintain their dynamic existence by continuously exchanging matter and energy with their environment. Von Bertalanffy studied systems that are maintained by ‘the continuous flow of matter. Living forms are not in being, they are happening, they are the expression of a perpetual stream of matter and energy which passes through the organism and at the same time constitutes it’ (Von Bertalanffy, 1968).

The conceptual model that Von Bertalanffy gave for the living organism, as an open system, has revolutionary implications for behavioral and social sciences. In particular, the role of entropy in these systems is very different than in closed systems. In closed systems, entropy is a source of disruption and disorganization. The universe, as a closed system, obeys the normal rules of thermodynamics and entropy. However, in open systems, which exchange energy and matter, entropy leads to new system organization! As the open system interacts with the environment, the environment suffers the consequences of the disrupting entropy, and the open system creates a new order! Open systems do not have infinite interactions, and there are ‘boundaries’ that define their extent. As we are examining a challenge, we need to define the boundary of its system.

One of the mysteries of life is the presence of open systems and their negative entropy. In open systems, i.e., life on earth systems, steady states are maintained by a self-regulating balance of decay and synthesis, leading to emergence of increased order and organization. These characteristics are specific to open systems and form new rules. Since all systems, physical and biological, interact with some other systems, a system can have a ‘negative entropy’ while the entropy of another interacting one increases (Prigogine & Stengers, 1997).

Evolution is a good example for the presence of systems, which continue to improve themselves. For example, viruses are biological systems that are expected to move into disarray and randomness. But some virus systems continue to evolve into better organization. The COVID virus, as a living system, is an improbable non-equilibrium state, but it continues to evolve into more dangerous ones—better for its survival, but not for us. As the system evolves, mistakes on the DNA level can happen, which could lead to the demise of the virus or not!

Due to the interactions among the elements of the system, the totality is more than the sum of the parts. There is more than one meaning for the word ‘more’ here. More might refer to the functions, activities, creativity, interactions with the outside, and possibly more viability.

4 Ordered and Unordered Systems

Academicians classify systems according to the type of interactions among their parts and with their environment. Theory and mathematics contributed to the classifications of systems, with the goal of understanding organizations and managing them (Thelen & Smith, 1998). Other contributors, interested in biological systems, or systems engineering, used network theory for classifications too. Social challenges need classifications that place humans and their environments at the center of the classifications. These theories identify features and create methodologies to address the challenges. There are several books and articles that have been written on the classification of systems (Bushe & Marshak, 2016; Stacey, 2016). A quick Google search gives more book titles than anyone could ever read. Here, we provide a general overview of the system interactions and their implications for addressing pressing human challenges.

In each system, there are several elements. These elements interact within the system and with other elements of other systems that may be present in the same environment; see Fig. 2.3. These interactions define the nature of the system. If the interactions are limited and they exhibit time reversal then, most likely, the system belongs to the category of ordered systems. Time reversal is an interesting test. Not all systems can be brought back to their original state. For example, most electromechanical systems can be fixed when some elements break; cars are very complicated, but a broken car can be fixed. On the other hand, the state of a traffic on a given hour of a certain day cannot be brought back. In this case, there is a significant number of interactions, and thus the traffic system does not belong to the ordered category. Interactions that are time-dependent and nonlinear create dynamics that may be difficult to predict, calculate, or simulate. These interactions are encountered in complex systems.

Fig. 2.3

An illustration of three interacting systems

Most human systems are unordered systems, and they exhibit unpredictable dynamics. There are categories within the ordered and the unordered systems, which will be discussed next.

The framework of ordered and unordered systems (Fig. 2.4a) was extended to a descriptive model, called the Cynefin framework (Snowden & Boone, 2007), which provides a more detailed ontology of systems and provides a methodology for addressing them. The Cynefin model can be used to engage engineers in ways of thinking about systems problems (Berger & Johnston, 2015). This model presents four ontologies: obvious and complicated (ordered) systems, and complex, and chaotic (disordered) systems (Fig. 2.4b).

Fig. 2.4

a Two general ontologies of systems, b Cynefin framework

There are many simple systems. Some of them may become complicated. This happens when the number of elements increase as well as the interactions increase. These systems become more capable or intelligent. But the interactions stay time-reversable. This type of ordered system is mostly Newtonian and can be addressed by good engineering tools.

On the other hand, when the system has a large number of interacting elements, the system is complex. Complex systems may end up chaotic with some changes in initial conditions, nonlinear dynamics, or emergent behavior (Bertuglia & Vaio, 2005). Chaotic systems do not last for a long time, and most of the time, they revert to being complex systems. Chaotic behavior is a manifestation of nonlinear dynamics, and therefore not all chaotic systems are complex (Rickles et al., 2007). Also, not all nonlinear systems are chaotic; initial conditions play an important role.

The engineering profession has developed an enormous body of knowledge around ordered systems, resting largely on the physical sciences and mathematics. This scientific knowledge defines the obvious or clear category and is constantly being refined, through scientific research. The best practice is an ongoing effort in every engineering discipline, and much of this knowledge is captured as codes of practice and international standards, e.g., ISO standards at https://www.iso.org.

5 Addressing Ordered and Unordered Systems

For ordered systems, the strategy for problem solving is to ‘sense, categorize/analyze, and respond.’ When the problem is obvious or simple, categorizing the problem makes the solution clear, and by using best practices and engineering standards, a good solution can be obtained. For more complicated problems, an analysis step is required. This may not be easy to do, but the outcome is predictable. Recall that for ordered systems, even when there are interacting elements, Newtonian physics and engineering science apply, and by breaking the system into components, a solution can be constructed.

Unsurprisingly, most of the undergraduate programs are built around analysis of complicated systems. Appropriate complicated challenges are normally assigned for different disciplines or majors. These prepare students to sense, analyze, and respond with appropriate solutions. Computer simulations have added significant capacity to problem solving and reduced the time to find solutions. In addition, big data, machine learning, and different intelligent machines can open doors for creativity and invention.

When the system has many interacting elements, finding effective solutions is not straightforward. The strategy for problem solving is to ‘probe-sense and respond.’ If the internal and external forces cause the system to reach a chaotic state, as in disasters and emergency situations, one has to act quickly, and the strategy becomes act-sense and then respond at a system level.

To probe a complex system, we need to map it and study its components and their interactions. Mapping provides a clear visualization of the components and their behavior. In addition, the map explains and communicates information on the challenge and is used to manage complexity and find root causes of the challenge.

To map a complex system, we start by identifying it. That is, we find its boundaries. Boundary identification is an important step since all systems exist within bigger systems, and one cannot try to find mitigations for a complex system in its broadest context. Next, we need to identify the system’s elements and how they interact with each other. We need to identify the feedback loops and the systems pattern of behavior.

6 The Dynamics of Complex Systems

Some human challenges are nonlinear dynamic systems subject to forces of stability and instability. These forces are a result of significant direct and indirect interactions among the elements and the presence of feedback loops. Other forces that contribute to the overall systems dynamics result from the amplification of fluctuations and random events. The overall actions of these forces lead to collective behavior of the system and its ability to adapt to changes in its environment.

Complex systems are dynamic entities, whereas a system’s map is a visual representation that is a snapshot in time and does not show the dynamics of the interactions among the elements of a system. It is possible to illustrate the feedback loops and give indications on the dynamics. Then, some may refer to the map as a causal map.

In general, systems have inputs and outputs and, in complex systems, the relationships among these components are nonlinear. Feedback loops, Fig. 2.5, are often present and create complexity as they drive some elements of the system.

Fig. 2.5

A general diagram of a system of input/output and feedback loops

Feedback loops are part of many physical and biological systems, as well as in economics. They are important in electronics, genetic regulation, and economic cycles, for example. There are two types of feedback loops called positive and negative loops. The positive ones increase or enhance a parameter or a process, and they tend to drive the system away from its equilibrium. The negative ones reduce a parameter or dampen a process and drive the system toward an equilibrium state. Thus, feedback loops may create a growth or the decline of the system. Figure 2.6 illustrates these points.

Delays in feedbacks create complexity. An example for systems with time delays is cloud-rain formation, depicted in Fig. 2.7. In this case, there are several nested loops and each may have a particular time delay and unpredictability. The feedback loops and interaction among the different elements illustrate a dynamic complex system.

Fig. 2.6

A simple diagram of a population system—the balance between birth and death

Fig. 2.7

Cloud-rain formation evaporation → clouds → rain → amount of water → evaporation

In complex systems, there is no central command and control, nor planning and management. System elements act individually or collectively, under the influence of the feedback loops and their dynamics. These give rise to collective and unexpected behaviors.

Complex collective behavior can be a result of simple rules that control the interactions and communications among the individual agents. This can be observed as swarm intelligence in social insects such as ants, bees, birds, and fish. Collective behavior by fish and birds has three rules which create complexity and include separation or collision avoidance—i.e., short-range interactions; cohesion, or steering toward average position of neighbors—i.e., long-range attractions; and attempt to match velocity with nearby flock mates.

When the environment changes, or some parts of the system change, the overall system adapts to the new conditions and a new order emerges.

Emergent properties of complex systems are profound. New phenomena and behavior appear unexpectedly. Elements that were not interacting become part of new webs of interactions, and other interactions may disappear, and the system undergoes a paradigm shift.

For systems with several time-delay loops, and when a time delay becomes greater than the intrinsic response time of the system, periodic and even chaotic events, and solutions arise. Such complex adaptive systems live dynamically at the edge of chaos, where new possibilities emerge from the diversity of the elements (i.e., agents) and their creativity. These spontaneous responses give the system new life and sustain it.

On the other hand, a system might be in a state that is different from what we desire. This may promote some actions. Such actions may drive the feedback loops or change some interactions. Because of the nonlinearity of the system and its time delays, a thorough study of the system map is not enough. Some experimentation and observation will be needed. When the system is in a dynamic stability, chaotic states might appear. These states are very sensitive to initial conditions and our actions may create unexpected outcomes. The origin of these outcomes may not be known for a long time, during which the system may have fluctuating states and explosive instabilities.

Addressing complex nonlinear situations is not easy, but it may be urgent. As we mentioned before, the strategy for problem solving is to ‘act, sense, and respond.’ Acting fast might be very important, as some situations require an immediate action plan since a system solution requires more time to study. Natural disasters are examples of these situations, and they put human beings at risk, and they need to be managed fast.

Introducing positive feedback might be appealing but should be monitored very carefully. Even small positive feedback steps that reinforce an initial change, can accumulate an exponential growth, and create an imbalance between the negative and positive feedback loops. When there is such an imbalance, unexpected chaotic outcomes may ensue (Radzicki, 1990).

After an intervention, a complex dynamic system may enter one of three states, albeit these states represent a continuum. The system can reach a stable equilibrium (point attractor) which is independent of time. Also, it is possible that the system periodically goes back to a previous stable state (periodic attractor). A more complex behavior can happen too. The system may be characterized by non-repetitive and non-predictable fluctuations that arise because of a concurrent interplay of negative and positive feedback loops (strange attractor). This interplay and the significant interactions create a new order.

The system can be in any of the above-mentioned states, depending on the dynamic combination of the forces, and on the relative strength of the interaction among the system’s various elements. The system passes from a stable equilibrium to periodic behavior to chaos when the strength of the value of the parameters, i.e., linkages between the variables, changes (Feigenbaum, 1978), or when the number of variables with different periodicity increases (Thietart & Forgues, 1995).

In complex systems, every intervention is an experiment and a step forward to create mitigations. A new order might be a progressive one. On the other hand, we need to be aware that some solutions may create additional future challenges and bring the challenge to the realm of wicked problems (Rittel & Webber, 1973).

7 AI as a New Agent

Systems have several classifications and attributes. They mostly evolve around human needs, experiences, and challenges. More recently, we are experiencing the introduction of artificial intelligence, within many engineering spaces, in the thinking, making, and implementing. AI will augment our systems and provide exciting opportunities and new challenges. Students and educators need to engage these new agents and, as we map systems, a special care needs to be paid to their new type of interaction.

We may summarize some of the attributes of the systems, as shown in Table 2.1, and we note that most of the initial defining work was done a long time ago. But this is not a complete list, and more can be included when considering artificial intelligence systems.

Table 2.1 Complex system classification, modified from (Magee & de Weck, 2004)

8 SDGs as Complex Systems

Clearly, the Sustainable Development Goals (SDGs) belong to the class of open and unordered systems. Each SDG has several complex challenges. The SDGs illustrate what engineering students should learn to address, and what instructors should include in their courses to engage students with human challenges. There are several ways of grouping the SDGs; one is listed in Fig. 2.8. Obviously, some of the SDGs have more engineering challenges than others (the circled ones), yet these goals also include social aspects, with which engineers will need to grapple. Climate action is perhaps the standout example of this kind. There are several engineering solutions to mitigate climate change, but they cannot be implemented without political and social will.

Fig. 2.8

Categories of the sustainable development goals, modified from (Kostoska & Kocarev, 2019)

The SDGs are giant problems and progress will only be made with each of them in a piecewise fashion, in every national and regional context. Engineers might reasonably be expected to address aspects of these problems, either in their own countries or in international contexts.

Although, in principle, engineering can contribute to problem solving in all aspects of the SDGs, there are some specific ones where engineers will need to provide basic services, such as in clean water and sanitation (SDG 6), infrastructure, in terms of buildings, transport, energy, telecommunications (SDGs 7, 9, 11) as more and more of the population live in cities. Engineers will also be involved in more efficient agriculture (SDG 2), better health services (SDG 3), education services (SDG 4), responsible production and recycling (SDG 12), and so on.

Many of these problems will be improved incrementally. Clean water is provided to one village or town at a time, often in conjunction with renewable energy and wastewater processing. This simple example shows the interconnectedness of the SDGs; in this case, clean water will invariably require energy and it will also produce waste, e.g., brine, which must be disposed safely for both humans and for the environment, an example of a water-energy-waste nexus. For example, see (Wang et al., 2018).

A close look at the SDGs brings forth the significant interactions among the strategic goals. Figure 2.9 shows how these goals are interdependent (Le Blanc, 2015). As we address one of the SDGs, we need to examine and address possible adverse effects on the others.

Fig. 2.9

Some of the interactions among the outcomes of the SDGs

The mitigation strategy for complex challenges is to probe, sense, and respond. This fits neatly into a design thinking approach (Dym et al., 2005), where the first stage is to empathize. In the probe step, respondents must empathize with people experiencing the challenge and appreciate their situation. This requires involvement with the challenge and the people facing it. Empathy is not a simple matter to develop. Human senses and heuristics can interfere with getting the appropriate answers. Perhaps with appreciation and a direct involvement and understanding, the problem becomes defined enough for students to exercise their innovations.

Probing and sensing are part of the steps to define the problem to be solved. In addition, probing and sensing the system response informs creative solutions. Some critical questions should be asked such as: What is the nature of the problem? Is the system what we expected?

Responding is to develop appropriately scaled experiments to determine changes in system behavior, and to answer questions such as: What might we do about the system behavior? How do we know that the intervention works? Do we need to modify an implemented solution, e.g., in the case of transportation, reduce the toll to attract more customers?

When engineers work on complex human challenges, they need to develop this systems approach to problem management.

9 Systems Thinking

An important notion in human systems is the role of people in systems. Most systems are some combination of both engineered systems and social systems. A transport system, for example, is certainly made up of hardware such as roads, vehicles, and energy supply, but it serves human needs, and those users of the system will ultimately shape the hardware through their interactions, leading, for example, to a particular car size, height, new bus routes or new freeways or, indeed, reduction in the use of the transportation as people work from home. Understanding human transport behavior is critical to the design and operation of transport systems. This is an important lesson that is normally not discussed in technical courses.

To understand the interaction among the different components of the Human-Artifact system, different models had been proposed. Meadows proposed a model where ‘system stock’ is tracked (Meadows, 2009). Another model relies on establishing lenses and mapping by creating positive and negative interaction loops. Time delay is always noticed. These important factors of complex systems were discussed in the previous sections.

As we discussed earlier, the direct and indirect complex interactions among human beings, the human and the artifact, and machine-machine communications require several lenses and multidisciplinary people to study them. In the method of mapping complex systems, we created a visual study of a system and required that the relationships and the loops among its components to be indicated.

Broadly speaking, mapping allows us to provide an explanation of a system to better understand its complexity. Systems nest within larger systems. These systems interact and communicate. In addition, some elements that exist in a system may also exist within other systems.

In the example below, society is depicted as composed of several systems that have many interactions, Fig. 2.10. It is worth noting that the boundaries of these human systems are intertwined, and it is not reasonable to assume that each system has a clear boundary. For example, among education, medical, and financial systems there are several shared subsystems. When examining any of these systems, we need to identify these connecting elements yet focus on the main elements that attract the most connections. In addition, as we emphasized before, identifying feedback loops is critical.

Fig. 2.10

Society as depicted by several human systems

Mapping the system with diverse groups and using multiple lenses, i.e., ways of viewing, ensure a broad view of the system. It is how we consider a system and its functions, as we define it.

As an example, let us create a simple map for an educational system. One may discuss this system from a financial point of view and consider budgets, faculty salaries, endowments, and financial aid. Equally, we could discuss it for infrastructure and link it to finances and pedagogy. A third view might be the curriculum and connections to jobs and the economic welfare of the graduating students as well as their contribution to society. As we use different lenses, we are connecting the educational system to the larger system that represents society.

This concept is illustrated in Fig. 2.11, where we limited the large number of interacting systems to one, e.g., the financial system and education.

Fig. 2.11

Education as a system of interacting elements

Further, Fig. 2.11 needs to be considered within the broader social context. The system presented here shows relationships among several important elements, but these elements are not the same for all education systems, and the strength of their interactions can be very different. Educational systems change from one country to another and even from an institution to another within a country. Social contexts are critical, and they determine the relationships to other systems, as well as the different feedback loops.

In the example given here, the context is a US university where the educators constitute a main element in the system. Educators affect the curriculum, the pedagogy, and the learning environment. They drive policy and influence technology and the communication system. Of course, they have a significant influence on student learning and their careers. Each element in this example affects other elements and is affected by the totality. Many relationships are established, and many feedback loops are working. All of these create a unique complex system.

In a different model, the administrators might be the main element of an education system. Administrators make many decisions that can affect what and how students are educated. Similarly, the financial system with its banking and investment institutions significantly interacts with education in a social process that would vary in different sociopolitical systems.

As discussed earlier, mapping a system creates logical relationships among the system’s elements and prioritizes these elements within a social context. Mapping is a study that is built on assumptions. One of the assumptions that might be built in the map is how the different elements evolve. But the map is a snapshot in time, and we need to be careful if we are using the map to consider how things will interact in the future. Time evolution can be very difficult to determine over a long time.

When we read a map, we may examine the purpose of a system. The purpose of the system asserts the values and determines the outcomes. If we wish to change the outcomes of a system, we need to examine all the main elements that affect the purpose and determine how the desired change might create feedback loops that may affect each element.

A good example to consider is a change in the outcome of an education system from being ‘knowledge acquisition’ to ‘problem solving.’ How would the change take place? and who would be creating the change, and who is affected by the new outcomes? In this example, the educators might be the prime element to create the change. But we cannot ignore the administrators, the technology, the learning environment, and the financial system. Moreover, we need to engage other stakeholders such as students’ families and members of the political system, among others. The students are part of the system and will be heavily affected by this change, they need to be part of the process, but young people may be most ready for change. When such change takes place, what are the feedback loops? Are they from internal to the current system? Are they related to the infrastructure/finances? Are they related to employers? Is the accreditation affected? These are the types of questions that need to be addressed broadly.

Changes do not happen quickly, and their impact might be found several years after they take place. These time delays are important and difficult to track when a change propagates through several systems. Although interactions in human systems are based on communication (Luhmann, 1995), changes in one system do not necessarily cause changes in another interacting system. However, some changes might make a big difference across several interacting systems. For example, in the case of COVID-19, changes in health led to changes in the use of IT technology, which caused a significant change in the learning environment. Most of the curricula in different universities and countries adapted successfully to this change and offered interactive courses online. The value of this outcome will not be known for many years to come, and it might be that we are observing the beginning of a significant change.

10 System Mapping with Stakeholders

Enquiry for problem definition and understanding is critical, and it is important to engage end users and stakeholders in the change process. There are several tools that help the creation of an inclusive system map, which captures the views of the client, end users, the stakeholders, and the community at large. Some of these tools are qualitative and some are quantitative, and it is important to use both methods (Morgan, 2013). These tools include experience mapping (Kalbach, 2020), stakeholder mapping (Walker et al., 2008), fishbone diagrams, five whys (Romo et al., 2013), system mapping (Finegood, 2011), motivation mapping, the issues wheel, causal layered analysis, causal loop mapping (Inayatullah, 2005), generative dialogues (Proctor & Bonbright, 2021), assumption smashing, empathy mapping, analysis of feedback loops, system dynamics (stocks and flow) (Meadows, 2009).

System maps that include stakeholders give information on the persons and organizations that might be interacting in the presence of feedback loops. In Fig. 2.12, the map of an educational system shows causal loop diagrams from which a system dynamics model of stocks and flows can be built. This allows for an exploration of the system dynamics and its time lags.

Fig. 2.12

Different interaction loops among stakeholders

Another framework for systems thinking (Cabrera and Cabrera, 2015) uses four concepts: distinctions, systems, relationships, and perspectives, cleverly implemented graphically with the Plectica software https://www.plectica.com. In this model, there are four steps to analyze systems. In distinctions, we separate parts of the system from other parts based on our purpose. Similarly, we separate the system from its surroundings. This process of boundary making is critical in the analysis. The next step is defining the components of the systems. Every component can be considered a whole, and made of parts, depending on the purpose. The degree to which we zoom in for more detail or zoom out for less detail depends on what is the purpose of the analysis. Similar to the above-mentioned method, using different lenses, we point out the relationships between the elements, the feedback loops and time delays. The analysis and solutions are done next and obtained within a perspective which is defined by the objective of the study.

The objective of the system may change in time and, in addition, the ecosystem within which the system is operating may undergo a significant shift. System mitigation must be sustained for a long time to create meaningful interventions. A long-term perspective is not easy to develop as it relies on speculating on the events of the future. In particular, unpredictable feedback loops and emergent behaviors create significant challenges. A method to investigate possible future directions is discussed in the next section.

11 The Power of Unpredictability

Life on earth continues to change. New generations of pupils and educators are learning and practicing many new disciplines. Pedagogy is evolving too and taking advantage of digital technologies. Such changes are normal, but the rate of change is unprecedented. The rate of change as well as the number of changes have created situations where the future directions and outcomes are very unpredictable. As research is creating new knowledge, the content of the disciplines is fast evolving too. This is an unprecedented and exciting time; predicting what will happen next is very difficult.

The need for understanding a particular phenomenon is creating a convergence between topics and disciplines that are normally at a distance. Students are interested in learning across different disciplines and researchers are applying new techniques borrowed from other disciplines. New names for these disciplines are invented like opto-genetics and quantum Internet and the like. We are in an exciting time with inventions and innovation bombarding our brains and changing our lives. But these bring a high degree of unpredictability too. Some of these changes could lead to new paradigms that affect society and our daily life.

Since 2020, COVID-19 and its mutations have had a dramatic effect on many aspects of our lives, including politics, economy, trade, transportation, medicine and public health, education, science and engineering, social relations, and the future of work. But as we are being harassed by the COVID-19 and its derivatives, there has been significant and rapid growth in technology, especially AI and biotech. In addition, the influence of COVID-19 has isolated and eliminated some technologies and created new ones. It is safe to say that the digital tsunami that has been hitting our lives, has not ended yet.

Could one have predicted these events and prepared for them? For example, could COVID-19 have been predicted? Maybe! People are obsessed by the future and many people spend significant energy trying to predict it. Could that be done? What prevents us from predicting the future?

The future is not a continuation of the present and is not necessarily a reflection of the past. It is something else. As in the past and present states, many elements and events interacted to form these states. These interactions and their nature cannot be reproduced. The events that are happening now can be an outcome of some events that happened in the past, but also due to some emergent events that are the outcome of complex interactions that are the nature of complex systems. In addition, human traits and interests may enhance some dynamics and dampen others.

Are there methods to analyze human traits and predict the time evolution of dynamic complex systems? Not really. But there are techniques of reducing the uncertainty and attempting to predict not a single future, but a wide range of futures. These are discussed in the following sections. We start by bringing attention to human mental models as they affect how we deal with unpredictability and then follow with a discussion of the foresight technique.

12 Mental Models and Biases

Learning is a social process, which relies on interactions and discussions. Learning strategies are personal and are affected by many factors, including experiences, culture, interests, and accessibility to knowledge. These factors may also create blind spots and hamper sociability, which is a cornerstone for creating constructive human interactions. Through sociability, conversation becomes productive, and disagreements are constructive.

Human interactions have been significantly changed by the digital tsunami that continues to hit our cognitive reality and behavior. These changes have altered our institutions and produced unexpected entanglements of our social traits. On the Internet, information is disseminated instantly. Social networks and instant messages create new sets of values that may be unacceptable to most people. For example, faced by the lack of options for accessing certain websites, people are coerced to accept new norms for privacy and security. New mental models are formed by such experiences.

Not only have digital technologies altered our mental models, but they also modified our heuristics. Human heuristics are the effective and ineffective strategies we apply when we face complex challenges. These strategies are based on previous experiences and tend to simplify the required complex cognitive strategies leading often to systematic errors. Heuristics are part of our mental models. They are representations of how the world works; they shape our thinking, connections, and opportunities. Education, on the other hand, makes us accept more models and gives us tools to better understand the world and make good decisions.

Our mental models are shaped by many things. An important one is our discipline. The call for interdisciplinary education is critical. Interdisciplinary education broadens our mental models and opens avenues for innovation and creative problem solving. It makes us able to appreciate the richness of diversity.

Disciplines have different mental models. In science, the model may describe a particular ecosystem. For example, in physics and chemistry, thermodynamics has laws that describe energy in a closed system and regard energy as something that cannot be created or destroyed. In biology, the ecosystems are in constant evolution, and the mental model describes groups of coexisting organisms, where some are cooperating, and others are competing for energy and resources and self-preservation. The model is governed by natural selection and adaptation. Thus, the system cannot be closed, as it requires energy to create adaptation.

Similarly, in economics, the concept of opportunity and opportunity cost and trade-offs dominate relations. Personal incentives lead to capitalistic competition, and creativity and innovation lead to the development of new technology and products that end up replacing the older ones. In free markets, the system is open to supply and demand forces, which are determined by customers that can be swayed by marketing forces and opinions. Like biology, there are limited supplies and competition through innovation (energy) and create evolution and diversity of products and services (species).

Similar to the above-mentioned scientific mental models, culture and human interactions and dynamics create mental models and human biases. An example of human biases is our tendency to stereotype from limited experiences. Often people miss nuances through such filters.

At schools and universities, we learn to curb our heuristics and tame them. Heuristics are useful shortcuts that may create helpful efficiency in urgent situations. But they also create biases. The combinations of different mental models and heuristics can drive wedges among people. Although engineers learned to use scientific models to create technologies, they are not immune to these biases.

One may consider curiosity as a bias. But that might be a positive one for those who are eager to create new value. Engineers need to be curious. Curiosity is an instinct that leads to unique behaviors. Since infancy, children test the boundaries and learn through a form of experimentation. Curiosity is not only a driver to knowing but relates to creative actions and active learning. It motivates student engagement to create solutions, and when it is combined with critical thinking (Moon, 2007), it can become a driver for innovation (Pusca & Northwood, 2018). In fact, human progress is built on creativity that is fueled by curiosity.

Trust is a required condition for most human interactions. Without it, markets and economies vanish (Zak & Knack, 2001), and countries go to war. Trust has a biological basis as well (Zak, 2017), because it is needed for socialization. It requires consistency, clear communication, and a willingness to tackle awkward questions (Galford & Drapeau, 2003). In organizations, building and maintaining trust is critical, and it requires skills, supporting processes, and unwavering attention. Trust plays a critical role when teams are formed. During active learning activities, trust becomes a key element for the success of the project.

Notions of curiosity and trust are critical for creating effective teams that can create important outcomes. Some students are natural leaders, and others can be taught to be effective leaders (Kozlowski et al., 1996). Creating teams with curious engineers, who trust each other, is a huge task for leaders.

While creating AI software and devices, engineers must feel responsibility for examining the consequences of biases that could be built in their algorithms. Embedding ethics training in the curriculum through case discussions is critical. This makes engineers aware of their social responsibilities (Fleischmann, 2004) and the need to act accordingly. Embedding ethical constraints within the development of AI algorithms is a huge challenge. AI is affected by our heuristics, and AI is altering them.

Some students can be self-motivated and have internal drives to learn and create, and one may consider that incentives are not too important in learning. But this is not correct. Most people respond to incentives, and their views can be affected by incentives. However, it is difficult to know what incentivizes people. Some incentives are related to physical and mental needs, others might support ideology, and create ambition. In general, incentives end up creating a bias for certain thoughts and actions. Grades, honorary mentions, and financial rewards all create incentives for learning. However, it may not last long. Encouraging intrinsic interests might be more effective. Further, faculty enthusiasm creates excitement and motivations for subject matters. Providing relevant examples and getting the students involved also incentivizes learning and enhances students’ motivations (Buckmaster & Carroll, 2009). Here we find that students of different backgrounds, working in teams help each other to learn and solve problems. In such situation, excitement and enthusiasm enhance the chance to learn. Removing fear of making mistakes and failure is essential for innovation, and under such conditions, learning by trying and daring to expose new ideas are tremendous experiences that young people carry with them for future projects.

In general, human beings are not precise observers, and we make mistakes by omission and admission. We tend to overgeneralize from small data, which may affect an engineer’s ability to make appropriate decisions. Using small data sets to infer overarching conclusions is common, and it might be a way to make quick decisions and reduce uncertainty but, unfortunately, it could also lead to poor conclusions and decision making (Tipton et al., 2017). The tendency to generalize from small samples is another aspect of heuristics that should be understood and avoided, especially for engineers and educators who are working on human challenges.

The above discussion brings out the importance of human conduct during the decision process. It is rather critical that we prepare students to become socially savvy. Heuristics and similar behaviors add complexity to the work environment and require careful examination of the structure and the norms of the team. While the team is defining the goals of an activity, serious dialogue needs to take place to limit biases. Such biases can be gender related as well as biases against some technical backgrounds.

Engaging diverse groups in defining and understanding the essence of a challenge is very worthwhile. Groups of broad interest discussing issues in open forums can reduce biases. Having stakeholders participate in guiding engineering students during the design process can uphold ethical conduct and avoid some biases.

13 Navigating the Futures

In the previous discussions, we encounter several situations where communications are critical to form a glue for human systems (Luhmann, 1995). We also pointed out that this glue led to fundamental changes. Communication is now instantaneous, and there are low barriers to disseminating information. In some sense that should create a more cohesive society with closer views. But there are enough indications to the opposite. A major issue is the difficulty of asserting factfulness to the transmitted information. How can we attest that heuristics and prejudices had not altered the information?

Is truth inaccessible? Or is it not important what is truthful but what people believe is true? (Ellerton, 2017). Should we not worry about the truthfulness, and treat it as an intellectual objective, but not a cultural value? Williams asserts that truth is an intrinsic value (Williams, 2010), and there is no doubt that truthfulness has political consequences and is an important element for trust and sociability.

Our fundamental values and mental models are affected by AI. Even the strict scientific methodologies have been altered by AI and that may have led to positive outcomes. Through machine learning, more discoveries are happening, and more unrealizable designs are made. But the interpretations of science and the translation of science into engineering and technologies, which can serve humanity, are under siege. Different interpretations may cause significant shifts in public opinion and confuse public health discussions and environmental debates (Koonin, 2021). These are not trivial matters. In media, we put our trust in reporters and editors. Nowadays, reporting is a matter of presenting points of views to support motives. Medicine is supported by scientific evidence and trusted scientific methods and reports. With these assurances, we accept treatments and subject our bodies to medical tests and synthesized drugs. Similarly, we put faith in our curriculum, but with questions on what a fact is, teaching becomes a challenging endeavor, not only from a pedagogical point of view, but also from a content view. If trust is at question, what do we dare teach?

If complexity is created by many interacting items, then complexity is alive and well in the current times. The interacting elements creating complexity are not only increasing, thanks to AI, but their dynamics are changing. The nature of the interactions is evolving unexpectedly. We know that complexity is not a static environment, but now its dynamic is fast moving, and far from an evolutionary process. Chaotic states are expected not to last for long. With the current speed of change, there is no time to understand the strange attractors or the consequences of the interactions. We must accept that we are swimming in a sea of unpredictable depths and unknown creatures.

In the past, people tried to make predictions to create decisions, but these predictions were mostly in error. The literature is full of predications by very knowledgeable people, yet many were proven wrong (Kappelman, 2001). Consider for example that by Albert Einstein (1932), or Thomas Watson, IBM Chairman in 1943, stating ‘I think there is a world market for maybe five computers.’ And Robert Metcalfe, an inventor of the Ethernet in 1980, saying ‘I predict the Internet will soon go spectacularly supernova and in 1996 catastrophically collapse.’ How could such knowledgeable people miss so much?

If the past and the present are consequences of many interacting events, issues, human interventions, as well as environmental changes, why should we expect these to persist into the future? (Fig. 2.13).

Fig. 2.13

Present is made up of many interacting elements—most of them do not persist into the future

We know that the present is not a continuous function of the past. Thus, the future must not be a continuation of the present. Therefore, our views of the future cannot be distilled to one single future, and a different method for generating these future states must be used.

14 Foresight

Understanding the parameters and possibilities of the future helps in making decisions in the present. In the past, progress took big steps over changing paradigms. A paradigm took many years to hold firm and establish new grounds. This is not the case in the twenty-first century. Shifts happen very quickly. Education must keep track of sociotechnical changes, economic trends, and related human factors. What could one do to prepare for new content and pedagogy? The evolution of education, like any complex system, requires a methodology to investigate.

An older method, forecasting, had been used over many years and was successful in some short-term situations to enable decision making. However, this method is not appropriate in the current fast-evolving events. The forecasting method (Armstrong, 2001; Slaughter, 1990) is based on two concepts:

• (a) Theory of cause and effect: Investigated variables are put in a dependent relationship with their relevant determinants and are then predicted based on this knowledge.

• (b) The use of time series analyses: a statistical tool determines trend and typical seasonal changes but may not fully consider the short-term fluctuations, business cycle, and irregular influences.

Although forecasting methodology had been expanded to include nonlinear trends (Fig. 2.14), the condition of direct cause and effect renders this method unsuccessful in addressing complex challenges, where there are several root causes, and these root causes interact nonlinearly.

Fig. 2.14

Examples for forecasting—time series analyses and nonlinear trends

A different approach relies on using probabilistic processes, some of which could be nonstationary and chaotic. This method is named foresight (Popper, 2008). Since most uncertainty comes from human behavior, foresight admits uncertainties through a concept for several futures and allows for human interests and behavior to be considered as parameters for exploring the possible futures. Multiple futures are an essential concept for reducing the long-term uncertainty (Battistella & De Toni, 2011).

Human behavior and culture, as well as technology, influence human futures through multiple complex and interdependent drivers. Futures built around critical uncertainties are the most useful to consider. But if insights gained from strategic foresight studies are not linked to today’s decisions, they would be useless!

Methodologies for creating strategies consider different futures (Kosow & Gaßner, 2008). Such futures include desired, probable, plausible, and possible futures. Wildcards are also part of the mix. Surprises happen, but most of the time, and in hindsight, we find that these surprises could have been foreseen.

The concept of several futures is illustrated in Fig. 2.15, where each one of these futures has drivers and interacting elements (Kosow & Gaßner, 2008). None of these futures is more likely to happen than the other, and one should not expect to get a single unique answer from the research that created each one of these futures. Through rigorous investigation, one can find different scenarios that lead to each of these futures.

Fig. 2.15

Exploring multifutures, modified from (Kosow & Gaßner, 2008)

In foresight work, the term ‘scenario’ is used for different activities such as summaries of ideas and foresight results. It is also used as an element of the process, such as visions or outcomes of some activities that the team wishes to communicate. Furthermore, a scenario might refer to exploratory information on what might happen in certain situations. Here, we use scenarios for visions of future possibilities and for visions that have been derived from quantifiable and non-quantifiable studies, which can be presented in narratives.

The objective is to make strategic plans for each of these scenarios and create decisions based on each one of them. The analysis may start by creating exploratory questions examining the futures that are possible (Börjeson et al., 2006). Questions like: ‘what might happen?’ and ‘what would have led to what.’ Probable futures answer ‘what is most likely to happen?’. This category includes forecasting studies, which are characterized by a predictive nature and mainly focused on historical data and trend analysis. Another set of questions are normative, e.g., ‘what future do we want?’, and starting from a point in the future, we may ask, ‘how can this happen?’ ‘What does it take to reach a future where….?’, ‘What would have led us to a situation…?’ Also, we can ask a predictive question ‘what happens if …?’.

When we examine the present, we obtain insights. These are reflections that can be used to feed information about the futures, but this does not mean that they are simple extrapolations. They should be deep linear or nonlinear time explorations. By examining the past, we gain hindsight. Hindsight is assumed to be 20–20, but not exactly. People have a bias that distorts the thinking about the past, and they have a tendency to perceive past events as more predictable than they were before the event took place.

Predictability is not the only bias. Inevitability—‘it had to happen’ and memory distortion—‘I said it would happen,’ are two other biases. In addition, any feedback or corrected information a person may receive after they had given a judgment automatically updates the knowledge base underlying the initial judgment (Hoffrage et al., 2000). This situation of rewriting history happens often. It is true that when there are crises or poor consequences, we can look back and realize that a poor event could have been avoided. This could help when similar circumstances appear again. But that might be rare (Fig. 2.16).

Fig. 2.16

Looking backward and forward (Kaivo-oja, 2006)

Similarly, foresight thinking has its biases: optimism and pessimism. Whereas optimism might be due to good data creating confidence, it is also possible that it is only wishful thinking. Pessimism might be a way to avoid making difficult decisions and taking a leadership position.

From research and investigations, different scenarios are generated using several methods. The challenge is selecting a small number of scenarios that can do a good job of explicating the range of alternatives that may be confronted—or of highlighting the paths of development of underlying drivers and other factors.

A general sequence to follow for creating scenarios is outlined in Fig. 2.17. These steps are listed here as a guideline, and other methods have been used too.

Fig. 2.17

Steps to create scenarios to study foresight

In this diagram, the first step is to find signals and trends. Signals provide tangible specific evidence of change and trends detect patterns and change direction in signals. Some of these signals might be weak, ambiguous, and complex. Finding them, and discerning their implications, is very difficult, but rewarding. Many times, these weak signals are the ones that create the new trends and the unexpected futures (Ilmola-Sheppard, 2014).

Drivers show possible structures behind the trends and point to root causes. Unpredictable directions or effects of drivers are the uncertainties. The important futures imagined with combinations of critical uncertainties are the desired outcomes, the scenarios.

There are several processes for creating scenarios. These may include individuals presenting their informed speculations about the future, using scenarios as a template for illustrating and enlivening their accounts. Surveys are used to obtain cluster views. These surveys could be digital and might be obtained through social media. In addition, expert panels may establish a framework of scenarios based on research reviews or conceptual analysis. Experts’ judgements, as well as computer simulations or AI studies, may shed light on what is feasible. Workshops and open debates to enhance teamwork and hear broad views of different stakeholders and decision makers are required.

The different views are then clustered, from which scenarios emerge. Although there are no recipes for what is important, topics to study may include economic, cultural, educational, technical, and environmental. Quality of life, future of work, public attitudes to risks, strategic technical expertise, and evolution of technology and its rate of change are suitable topics to study. In addition, policies for public health, immigration, trade regulations, future of infrastructures and facilities, intellectual property, and treaties are additional topics for examination.

After collecting data, further work is needed to test the meaning of the data. Outcomes from group discussions become the important factors, and biases must be kept in check as well as demanding evidence when ambiguity or uncertainty are found. Questioning what we find and finding if there are some roots for a trend or an issue is part of the foresight mission. Figure 2.18 serves as a reminder that we should not only ask why, but also why not.

Fig. 2.18

Finding trends—“ask why and why not” as in Shaw (1989)

Several research methods to investigate different types of futures have been used, and here we cite some of them:

• Delphi is a basic method of foresight (Sossa et al., 2019). Experts submit anonymous feedback and ideas (e.g., using Post-It notes) to begin an open dialogue. This allows addressing complex and controversial issues through a structured debate.

• Backcasting starts with defining a desirable future (Tinker, 1996) and then works backwards to identify policies and programs that will connect that specified future to the present (Bibri, 2018; Dreborg, 1996).

• SWOT analysis (Gurel & Tat, 2017) systematically investigates strengths, weaknesses, opportunities, and threats.

• Horizon Scanning (Cuhls, 2020) seeks trends before they emerge into mainstream and assesses whether one is adequately prepared for future changes or threats. It also identifies key action points to proactively shape desirable futures (Cuhls et al., 2015).

• Black swan (Petersen, 1999; Taleb, 2007), events are characterized by their extreme rarity, severe impact, and the widespread insistence that they were obvious in hindsight.

15 Anticipation is the New Strategy

As had been discussed before, the fundamental step in obtaining information to create options for the futures is finding signals that inform possible future changes. Most of these signals are weak signals or silent signals; that is, they are not clear indicators of an important change. In fact, most of the ones that later were found to be very important were only emergent information, which can be easily missed. Yet they are about something beyond a current paradigm (van Veen & Ortt, 2021). Weak signals are the seed of change, and they do not come as an extension of our knowledge; nor do they fit our current thinking and expectations.

Weak signals have several manifestations. They inform us about a shift in something important, which may affect culture and society, and they could also be warning signals of events to happen, or of an impact that could lead to dramatic changes. In business, they may be announcing the future death of a technology and the birth of new companies (Lesca & Lesca, 2014). They may give indications for political unrest and uprisings or an environmental challenge. For individuals, weak signals may help creativity, create innovation, or inform about health, and what to learn and practice.

In a world obsessed by quantification, weak signals are immune to that; they are gathered qualitatively. They need to be collected from multiple sources and be explored across many events that might be happening in different locations (Taylor et al., 2015). Weak signals may emerge from many sources like science, arts, philosophy, political events, and from the work of creative people. They could be found through discussions, or by reading text and blogs, and they can be embedded in images. Paying attention to their appearance is difficult and requires focus and training.

People’s observation and detection are affected by their mental models and openness to understanding what might appear as illogical ideas. Occasionally, weak signals are dug out of noise and recognized in camouflaged patterns, where they could hide within some current trends or concepts. Sometimes they are fragmented ideas, and the observer has the task of synthesizing them—vision becomes a necessity.

When a weak signal is suspected to be of importance, one must interpret it and attempt to consider its impact if it were to happen. Considering that these signals are part of complexity, their trajectories are not linear and can be random and on the fringes. Also, they are subject to feedback loops and their significance may take some time to be realized.

Identifying and interpreting weak signals is pivotal for initiating a foresight study and when such signals are revealed, they need to be taken seriously (Hiltunen, 2013). It takes efforts and concentration for many people to discuss and share ideas before they realize that they have stumbled on an important weak signal. Let us consider for an example, cryptocurrency.

Blockchains are ‘tamper evident and tamper resistant digital ledgers implemented without a central repository nor a centralized authority’ (Yaga et al., 2019). Weak signals for this technology came with the work of Leslie Lamport in the 1970s (Lamport, 1978) and later in 1980s (Lamport, 1981). In the 1981 paper, Lampert discussed password authentication with insecure communication, and later a consensus model for reaching an agreement under situations where the computers and networks are not reliable (Lamport, 2019). This seminal work led to another one by Satoshi Nakamoto and the creation of a peer-to-peer electronic cash system, i.e., Bitcoin (Nakamoto, 2008).

Although the science needed to be developed, we may ask whether Bitcoin could have been anticipated earlier than 2008? Could the weak signal for creating cryptocurrency revealed the possibility of creating this currency? Most people were surprised by the emergence of cryptocurrency and, most likely, had no clue that this type of currency is important, and it has special position in the commercial market. From an examination of Lamport’s early work, one may conclude that cryptocurrency could become a reality. The peer-to-peer network technology that created Bitcoin is broader than finance, and Blockchain technology has a massive potential for disruption by providing good digital security.

With the help of the Internet, there are ample opportunities for discussions on the future. People today recognize that the future is embedded in technology and there are articles that attempt to cite many potential new technologies, such as quantum computing and soft robots that could make the future exciting. Unfortunately, these thoughts are descriptive of what we already know, and they are far from what we have termed here as weak signals.

16 Thoughts on the Future of Education

In the last section, we examined how a process, foresight, enables us to have information to help decision making in the face of the significant uncertainties posed by the complexity of modern life. In Fig. 2.19, we illustrate steps leading to the creation of the strategy to examine the future of a topic. Weak signals and megatrends are important parameters to scan first. Weak signals can be elusive, and megatrends can be more readily searched and studied. But the two topics are not independent. There could be some information from the megatrends that point to weak signal and thus can be explored. Data from both, megatrends and weak signals, must be collected and critically examined. With a visioning process, such as the one outlined above, scenarios are created. The scenarios facilitate creating the strategy map, which leads to create a strategy for today that anticipates several futures.

Fig. 2.19

A road map to create future strategy

For education, the strategy is not universal. As we discussed, learning is a social process. Cultural values and practices must be considered as part of the strategy. Different schools in different countries may choose different paths. There is a general trend of using digital technologies, and this has affected education among many sectors. Digital learning and machine learning are megatrends. The general theme is that, faced with an abundance of digital information, we need to process information digitally to form new knowledge.

Most students are highly exposed to images and screens and tend to consume less text. Teaching and learning must pay attention to create pedagogy and research that take advantage of visual information and its flow. Research and learning through the Internet is highly productive, and students at all ages in any country should be encouraged to pursue such style of learning. At the same time, one must keep in mind that incorrect heuristics can seep in if one is not mindful. Teachers must monitor and correct misinformation. They also should help learning online by creating visual modules and by creating online teams.

All in all, as the younger generation, that is accustomed to digital technologies, grows to participate in higher education, we must expect a significant change in attitudes and abilities. Education must be ready to satisfy and drive this trend. A distinction between formal and informal learning is no longer valid. Certification (degrees) might end up less important in the future. Higher education needs to provide series of workshops, short courses that educate students and enable them to learn using online resources. Creating communities of knowledge online must become a priority. These communities are not only for students but also for educators who could connect to different educational landscapes (Hopkins et al., 2020).

Creating a curriculum where students find it fun and interesting to search the Internet and interact, socially, on the Internet is a productive trend. This is a megatrend, and it will continue for a long time. Related to that is learning through gaming and creating projects with ideas that can be shared and discussed online.

The digital wave with data and computation is creating a new paradigm that will force different curricula. Who will start making the change? In which topics? and when? These are questions that require foresight analysis. Most likely there will be a steady gradual change, but through the foresight process, educators can create the right plans for the future of education. In particular, engineering topics are more affected by changes brought by digital technologies. Simulations, computation, and machine learning are tools that will substitute a significant part of the current curricula.

Interaction and designing with machines are more than weak signals. Whether it is through big data, deep mining, or robotics, learning with machines is going to be a megatrend. It is also possible that some computer simulations progress and enable sophisticated problem solving with limited human intervention. Design becomes more sophisticated with creative options provided with intelligent machines. However, forming problem statements and choosing the needed constraints, i.e., the boundary conditions, will continue to be a human’s work.

A related topic that may affect the future of education is student movement, as well as researchers, across countries. These movements will affect the future of education. Of course, this issue relates to political policies. Climate change may also force some unexpected movements. In addition, tools and financial means are obvious drivers for these movements and we could be surprised by the socioeconomic changes that may take place in areas of the globe that might be nascent now. These financial constraints could lead to alliances among colleges and universities and a degree of specialization to enable differentiation.

Environmental issues and stress on sustainability can also be a driver of change in education. These issues may end up affecting different regions and countries with different challenges and may lead to migrations across the globe. Research in this area should promote society’s cultural development and not solely its socioeconomic development. Through reflection, analysis, and evaluation, students and their instructors can make a substantial contribution to a sustainable future (Hopkins et al., 2020).

As time passes, more cognitive activities will be required, and less physical work will be performed by people. Big data analysis shows considerable demand for online work (Stephany et al., 2021). For example, there are measurable changes in demands for different skills that are needed for projects related to robotics—how will these demands change the type of work that will be performed by people and how will education respond and prepare the appropriate workforce in time? (Stephany & Luckin, 2022).

The workforce requirements will change even more in time (Soto, 2020), and there is no question that the overall use of online work is a megatrend. Could education become mostly online events? How can we train engineers to learn online and work online?

Although a new vision of education must include digital technologies, broadly, it must gear learning toward building an equitable and inclusive society with sustainability as the main goal (Tawil & Locatelli, 2015). New literacies to enhance critical thinking in this information-intensive age, building up socioemotional and affective dimensions in learning are required to achieve an inclusive and equitable future society. Interdisciplinary learning and working on projects that are broad must become part of all aspects of engineering education.