New Competencies for Systems Thinking

Abstract

The growing complexity of technological systems, which has been elaborated upon in previous chapters, has increased the focus on the sociotechnical dimension of engineering, which engineers must increasingly address.

1 Introduction

The growing complexity of technological systems, which has been elaborated upon in previous chapters, has increased the focus on the sociotechnical dimension of engineering, which engineers must increasingly address.

First, the grand challenge of sustainable development has raised concerns about the nature-culture dualism, understanding ‘culture’ as related to what is human-made, while ‘nature’ is assigned to what is not human-made. Based on a review of literature that either constitutes or challenges this dualism in Western society, Haila (2000) has offered a more contextual and socioecological view on the spheres of human activity (Haila, 2000). With reference to Dewey (1958, p. 58), Haila (2000, p. 168) underlines that ‘… thinking is no different in kind from the use of natural materials and energies, say fire and tools, to refine, re-order, and shape other natural materials, say ore’. In this view, Haila (2000, p. 169) argues that ‘action-dependence and context-specificity of the artificial is a fruitful starting point for decomposing the nature-culture dualism’. Here, the interactions between human practices and natural processes, in particular cases, become the center of attention.

Many current conceptualizations to grasp the prospects of future societies have been developed in alignment with this understanding. Take, for example, the vision of a so-called circular economy. Geissdoerfer et al. (2017) carried out a comprehensive review of the concept of a circular economy and described how the concept has evolved from a rather descriptive approach to how natural resources influence the economy to a more intentional and design-based approach. This approach has gained traction with policymakers and matured to become institutionalized at the governmental level, e.g., in Europe and China. The resource flows are combined with an intensive focus on human practices involved with long-lasting design, maintenance, repair, reuse, remanufacturing, refurbishing, and recycling of products and services. What makes sense in these processes is, as noted above, the interactions between human practices and natural processes, in particular design cases. Without action dependence and context specificity, the idea of a circular economy holds no prospects.

Likewise, and although previous dualistic approaches are still rather dominant in current institutionalizations, the interrelations between technology and science have drawn increasing attention. The notion of technoscience grew out of French postmodernist movements (Hottois, 2006, 2018) drawing attention to the interrelations between technological development and scientific discovery. So-called science technology studies (STS) created the foundation for understanding these interrelations (Sismondo, 2010). Authors within this STS area of research have emphasized how human practices frame the creation and use of technology and, likewise, how technology becomes an actant in framing human activity.

The STS methodology has offered opportunities to analyze and get inspired by overviews of how technology is socially constructed. For example, Bijker (2012) presented a theoretical framework for the Social Construction of Technological Systems (SCOT) and pointed to a system of relations between problems, artifacts, and social groups.

Another example is the actor-network theory (ANT) as developed by Latour (2005) and others, which stresses the opportunities for nonhumans (e.g., technological artifacts) to act or participate in systems or networks involving different types of exchanges (material) and translations (semiotic).

On the micro-level, and very aligned with the rejection of the rigid boundaries of the dualistic approach, Haraway (2000) wrote the so-called Cyborg Manifesto to question the separation of the human from the other—an animal, a machine, another human being of a different gender, etc. These types of conceptions are striking in the sense that, although they reject boundaries as a premise, they make use of boundaries as social constructs to analyze and develop existing understandings.

Although STS research communities typically present themselves as interdisciplinary research communities, it can be argued that their grounding in social science and humanities (SSH) has pushed for an unintended nature-culture dualism, with an overemphasis on cultural issues. Likewise, although research communities in the environmental and sustainable science domain are typically interdisciplinary, it can be argued that their grounding in natural sciences has also pushed for an unintended nature-culture dualism, with an overemphasis on natural resources. And, finally, as the nature-culture dualism has dominated in Western philosophy, it can be argued that there are considerable barriers in finding appropriate ways to deal with the interactions between human practices and natural processes in particular cases; nevertheless, societal development urgently needs this approach.

From an engineering education point of view, institutions have tried to cope with the challenge within the disciplinary discourse, e.g., by establishing new disciplines as hybrids of existing disciplines, such as environmental engineering. This strategy has resulted in professionals who have, as their core, the capacity to bridge between different disciplines, filling in the gaps in the workforce. However, as there must be severe gaps to initiate such a degree of institutionalization, the new hybrid disciplines are not sufficient to address the complexity of the current societal challenges. More must be done. There is a need for integration of multidisciplinary knowledge in engineering education across all engineering disciplines, and there is a need to let the particular cases and the particular context to determine what human practices and natural processes are of relevance in specific engineering domains.

The rise of hybrid education, however, adds even more complexity to what could be called a ‘discursive battle’ between the beholders of core knowledge of the future. Therefore, institutional top management has a serious responsibility in creating leadership to balance different types of disciplinary settings in a way that will match the needed systems approach. There must be a strategy to combine disciplines of specialized experts who can work in interdisciplinary settings (an integrated program approach) with those who have the interdisciplinary expertise to bridge between disciplinary experts (the hybrid program approach). The balance between the two is crucial as the different types of disciplines must coexist when students move beyond university borders.

Consider one example. If all engineering students are to integrate design in their engineering education, how would that affect the curriculum of industrial design? How would industrial engineers contribute to a multidisciplinary team. These may sound like simple questions, but the ‘knowledge is power’ discourse in academia might trigger potential conflicts. Furthermore, if we do not accept rigid boundaries between design and engineering, how should we then merge the thinking, the methods, and the practices among staff and students? The same questions could be asked about the increasing call to merge sustainability science and engineering in an engineering education for a sustainability approach. Our understanding of ‘know-how’ within a discipline is considerably challenged and, furthermore, it is constantly changing.

In line with the above, there is the question of how to integrate or, in other words, how can we make and create a curriculum that is so flexible that it can embrace the changing call for multidisciplinary approaches that real-life cases call for? How should we determine what is needed in the particular cases of our concern, and who is to decide what is relevant knowledge to combine with the knowledge we find to be necessary to uphold disciplinary identity? To answer such questions, no matter if we are institutional leaders, educational designers, students or professionals, critical thinking and sense making are key competencies.

2 Critical Thinking and Sense Making

Although definitions of critical thinking are diverse, some cornerstones have been defined, from Socrates and beyond, including processes of questioning, reasoning, and judging. Explicitly, critical thinking thereby also relies on a set of criteria and purposes, which are carried on by individuals, social groups, and even cultures. Therefore, it is a complex endeavor to study, to educate for, and not the least to effectively practice, critical thinking in diverse settings and situations.

Mogensen (1997) captures dimensions of complexity in critical thinking by outlining four perspectives of critical thinking being epistemological (underlining individual knowledge positions and acts), dialectical (including different views and social knowledge constructs), holistic (including emotional and social dimensions) and transformative (questioning wider structures e.g., related to political, environmental, and cultural spheres). Barnett (1994) has conceptualized the dialectics between individual and social knowledge constructs by distinguishing between critical thinking (as individual construct) and critical thought (collaboratively constructed).

Some scholars emphasize the context dependency of critical thinking. In a learning perspective, Schön (1987) has characterized critical thinking as a continuous process of reviewing models, theories, and ideas applied to a context at different levels (e.g., individual, community, and/or social levels). In an educational study, Guerra and Holgaard (2016) point out that arguments in a critical thinking process, besides being scientific and personal, are likewise grounded in contextual analysis. This underlines that even though critical thinking is a cognitive process of questioning, reasoning, and judging, it is informed by social and contextual interactions.

Other scholars have emphasized reflexivity considering the critical thinking process itself, which relates to meta-learning. Baron and Sternberg (1987) regard critical thinking as a thinking pattern that requires people to be reflective and pay attention to the decision-making process that guides beliefs and actions. King and Kitchener (2004) developed a model for reflective judgment including both people’s different assumptions and range of knowledge, and the way people mobilize and use knowledge to justify their own judgments.

As we move on discussing the prospects of critical thinking for engineering education at a learning, as well as a meta-learning level, we will include both individual and collaborative processes of questioning, reasoning, and judging, related to different aspects of technological systems in context. To further stress the social, collaborative, and organizational aspects of critical thinking, the concept of critical system thinking offers a complementary framework.

Critical systems thinking derives from two sources—critical social theory and system thinking in itself (Jackson, 2001, 2010). Critical systems thinking recognizes that real-world problems do not correspond to traditional disciplinary boundaries and cannot be addressed in a reductionist fashion (Jackson, 2001). A move away from reductionism creates a need for overview, and the systems approach (with its focus on boundaries, elements, interrelations, feedback mechanisms, and transformation) provides exactly that.

The critical approach provides an attention toward what is at stake, what is problematic, what is valuable and, finally, what might be missing. Ulrich and Reynolds (2020) furthermore stressed the need for critical thinking about the boundaries and introduced ‘boundary critique’ as a process of defining, discussing, and negotiating what is relevant in an analysis. Critical systems thinking, furthermore, encourages a methodological, pluralistic, and emancipatory approach (Jackson, 2010), depending on the alignment between the system and the need for negotiation of values and interests.

Although a critical systems thinking approach has been criticized for being too much of an academic discourse and a concept in need of reframing (Midgley & Rajagopalan, 2020), the approach more basically brings attention to the need for a critical dimension in systems thinking and the process becomes collective and interactive, besides being cognitive and mental. The approach is not only concerned with reflection on the chosen systems approach, but likewise reflecting upon the ethics of invention behind it as well as the type of problem addressed (Jackson, 2001, 2010). This means that the systems approach itself is questioned and, as the social part of a technological system typically involves different groups and organizations, critical thinking becomes an interactive and exposed process contributing to the sense making that informs shared decisions and actions.

Karl Weick introduced sense making to underline that complex problems do not make sense at the outset (Weick, 1995, p. 9):

In real-world practice, problems do not present themselves to the practitioners as givens. They must be constructed from the materials of problematic situations which are puzzling, troubling, and uncertain. In order to convert a problematic situation to a problem, a practitioner must do a certain kind of work. [They] must make sense of an uncertain situation that initially makes no sense

Thereby follows that without a sense-making process to understand the problem in situ, decision making and action plans to follow will likely not make any sense either. Based on Weick (1995), sense making is:

• Grounded in identity—a consistent and positive self-conception that includes self-reflexivity.

• Retrospective—to reveal meaningful lived experiences. Retrospections express modifications of prior experiences.

• Social—sense making seldom happens in isolation. People enter dialogues and build narratives and activities, which are both individual and shared.

• Enactive—people enact with the environment and project themselves into an environment to observe the consequences.

• Ongoing—enactments create experiences, which feed the following retrospections, of which new enactments are based.

• Informed by extracted cues—people extract cues from the context to help them decide on what is relevant to make sense of.

• Based on virtues of relevance and plausibility rather than truth and accuracy.

Kurtz and Snowden (2003) especially relate sense making to complex adaptive systems and chaotic situations. When faced with extreme complex situations, cause and effect are only coherent in retrospect and do not repeat, whereas a Probe-Sense-Respond strategy is proposed (Kurtz & Snowden, 2003). However, retrospections are not of much use in chaotic situations, as no cause-and-effect relationships are perceivable. In these situations, an Act-Sense-Respond strategy is proposed, as there are no patterns to be analyzed; the strategy simply is to act quickly (Kurtz & Snowden, 2003). Thereby a stimulus is enacted to provoke a response that can be analyzed and makes sense in the given situation.

Van Wart and Kapucu (2011) relate such chaotic situations to crisis management, which is defined as unplanned situations with an urgency for fast change due to high criticality. In such situations, personal traits as self-confidence, willingness to assume responsibility, and resilience become important, to exhibit calm and strong leadership. In popular terms, there is less time for critical thinking as immediate action is needed, and intuitive aspects of sense making come into play, as well as to perform what Schön (1987) termed reflection-in-action.

These situations, complex and chaotic, are the ones underlining the need for human capacity. As expressed by Kurtz and Snowden (2003), there are at least three contextual differences between human organizations and ant colonies that make them more difficult to simulate using computer models: humans are not limited to one identity, they are not limited to acting in accordance with predetermined rules, and they are not limited to acting on local patterns. This being said, the complexity of systems and the urgency to act underlines the importance of having abilities to use digital technologies to handle as much of the information processing as possible, and as quickly as possible.

Back in 1990, Jerome Bruner raised attention to the act of meaning, and in his own words, he was decrying the Cognitive Revolution for abandoning ‘meaning-making’ as its central concern, opting for ‘information processing’ (Bruner, 1990, p. 137)—today we have reached so far in the digital age that the potential for a distributed workflow between human and machine becomes ever clearer. When actions are enforced, they must be analyzed; when extracted cues are pointed to, we must know more about how/why the domain unfolded.

When things make no sense, we must ensure that we are informed about the parts of the system that make sense at a given point of time. Even chaotic systems are loosely coupled with systems that we might have to know more about. In other words, the capacity to use digital technologies, as well as to develop digital technologies, to assist complex problem solving and handle chaotic situations, will most likely stand as one of the most important cross-cutting competencies, and cores, of engineering of the future.

3 Abilities to Use Digital Technologies Are Required

Digital literacy is highlighted as one of the fundamental literacies for most frameworks of 21st-century competencies (Pilco, 2013). Ryberg and Georgsen (2010) form group ideas of digital literacy into three overarching categories:

• Retrieving and participating in information practices, including the ability to search for, synthesize and disseminate information, follow the flow of stories and information, and move across multiple modalities and diverse communities.

• Presentation, production, and performance: to play/experiment with one’s surroundings as a form of problem solving, adopt alternative identities for improvisation and discovery, provide dynamic models of real-word processes by simulation and be able to appropriate media content.

• Collaboration and work skills, including the ability to scan one’s environment, pool knowledge toward a common goal, establish a collective intelligence, and interact with digital tools that expand mental capabilities in a meaningful and distributed way.

In an educational context, this definition of digital literacy underlines that digitalization expands our view of both information processing, problem solving, and collaborative learning.

In the European Framework for the Digital Competency of Educators (DigCompEdu), the following six competency areas are considered: professional engagement, digital resources, teaching and learning, assessment, empowering learnings, and facilitating learners’ digital competency (Commission, 2017; Redecker, 2017). This framework is useful to underline the different areas of concern, as well as different response strategies from engineering institutions.

Area 1, Professional engagement focuses on engaging professional environments including organizational and professional communication and reflective digital practice as well as digital consumer data platforms (CDPs) (Commission, 2017). Area 1 thereby interlinks the digital and subject-specific competencies in professional practice and underlines the context dependency of digital competency. As an example related to engineering, the System Engineering Research Center has developed a Digital Engineering Competency Framework including five levels (SERC, 2022):

• (1) Data Engineering, which covers data governance and data management,

• (2) Modeling and Simulation, to predict real-life performance of potential technologies,

• (3) Digital Engineering and Analysis, to optimize engineering systems,

• (4) Systems Software, for systemic application of digital engineering approaches to develop software,

• (5) Digital Enterprise Environment, to create digital engineering environments including software, hardware, and management aspects.

Whereas most engineering programs will most likely cover up to level three, levels 4 and 5 are typically addressed in specialized IT programs, which provide the foundation for new knowledge of computation and simulations as well as big data analysis and machine learning. As such, most engineering candidates are expected to have a high level of digital literacy but transferring this literacy from one engineering discipline to another is challenging. Unsurprisingly, it is also a challenge to transfer this digital literacy from technology development to human development—in this case development of the next generation of engineers.

Area 2, Digital resources, is related to sourcing, creating, and sharing digital resources and includes competency to select, create, modify, manage, protect, and share these resources (Commission, 2017). Area 2 thereby links the use of digital resources to educational practice. Examples of digital resources are scientific search engines, learning management systems (LMS), audio-visual productions (AV), and software designed for educational purposes, e.g., simulation programs.

The perceived importance of different digital resources however differs according to the educational context. Morais et al. (2015) even showed significant differences in the importance given to the use of digital educational resources between 1st and 2nd year students within the same program.

It is important to recognize that even though engineers, due to their technological focus, are expected to have a high contact with digital tools and a high degree of expertise in using digital tools, the resources needed for an educational session might differ. In other words, when the focus shifts from professional to pedagogical practice, faculty staff might lack an overview of digital resources for educational purposes. Without this overview, it becomes hard to understand which digital strategies might work best in particular educational contexts.

Some universities have responded by establishing technical support units with the obligation to create awareness of digital tools and support staff in selecting, creating, modifying, protecting, and sharing digital resources within the different fields of studies.

Area 3, Teaching and learning, relates to managing and orchestrating the use of digital tools in teaching and learning, including teaching, guidance, collaborative learning, and self-regulated learning activities (Commission, 2017). Area 3 thereby links to the use of digital means to improve educational activities.

An overarching example is the design of blended learning modes to offer a more inclusive, more flexible, or more diverse learning environments. In a systematic literature review, Boelens et al. (2017) underline the complexity of such blended learning environments as they point to challenges of incorporating flexibility, stimulating interaction, facilitating students’ learning processes, and fostering an affective learning climate. Another example of the use of digital means to improve educational activities is gamification to engage learners (Faiella & Ricciardi, 2015).

These examples illustrate that an overview of digital resources and their applications for educational purposes is not sufficient. Faculty and staff must align the use of digital resources to the overall curriculum model, including consideration of the interplay between intended learning outcomes, design of educational activities, and assessment (whereas the latter is considered more specifically in area 4).

One response strategy is to reinforce the alignment of digitalization, teaching, and learning by specialized staff who can work as consultants in combining insights in pedagogical practice and digital literacy to support implementation incentives. More ambitiously, some engineering institutions have seen the strategic potential of establishing teaching and learning design units, which are targeted to the development of digital strategies that are aligned with the educational models, and who are able to develop the educational models of the institution through the power of digital technologies. Such design-based approaches to educational development must go hand in hand with staff-training, mentoring and peer-to-peer learning to benefit from the dialectics of educational research and practice.

Area 4, Assessment, points to digital tools and strategies to enhance assessment, including assessment strategies, the analysis of evidence, feedback, and planning (Commission, 2017). Area 4, like area 3, is likewise related to digital means to improve educational designs, in this case of assessments, and thereby the response strategies are similar. For this specific area of concern, an example is integrating quizzes for formative self-assessment into the learning management system and providing standard feedback for the user based on identified user typologies. For more summative assessments, digital exams are an option, now introduced in many engineering institutions. However, converting a paper-based engineering problem into a computer-based problem that can be automatically scored is challenging. For one thing, engineering exam questions are typically presented as cases related to a specific context and, furthermore, the partition problem-solving steps are also to be considered in the design (Keijzer-de Ruijter & Draaijer, 2019).

For Areas 2–4, the COVID-19 pandemic, and the urgency to plan and carry out remote teaching forced a growth in digital competency for educational faculties around the world. Whereas the instructional technology to deliver lectures was considered rather straightforward, it was less obvious how to use active learning approaches in an online environment (London et al., 2022) It can be argued that the acknowledgment of the challenges and limitations of transforming constructivist learning approaches to an online platform, is just as important a side-effect of the COVID-19 situation as the overall rise in digital competency.

This acknowledgment does not only include the importance of social presence and sense of belonging in a study environment; it also highlights the need for more systemic approaches to digital transformation of education, including general principles for what is considered the right blend for the next generation to respond to current societal challenges. When all teaching had to be distant, urgency became the main motivation and the technical tools available shaped the way teaching was carried out. After COVID-19, the lessons learned by doing now stand as a potential opportunity, but a lack of reflection on the why, what, and when of digital education, may not deliver the changes of practice that are possible. Similarly, a move to better digital tools, to empower the next generation of learners, may not occur soon enough.

Area 5, Empowering learners, focuses on the use of digital tools that can create more learning opportunities by accessibility and inclusion, differentiation and personalization, and active engagement of learners (Commission, 2017). Area 5 thereby underlines the potentials of digitalization to rethink educational systems. Whereas levels 2–4 used digital means to provide feedback to users based on typologies, area 5 includes personalized feedback and moves the benefits of blended learning beyond substituting what a teacher could act on in situ, to what the teacher could possibly act on given better data on each individual’s performance.

With increasing ease of big data analysis, personal learning analytics have become within reach to inform students on their learning strategies, and machine learning makes intelligent big data management possible to guide students on their learning paths. Chen et al. (2020) concluded, based on a review of Artificial Intelligence in EDucation (AIED), that AI has extensively been adopted and used in education and, likewise, AIED has increased in modalities—from primarily computers and computer-related technologies to web-based online intelligent educational systems and a use of humanoid robots.

However, whereas such digital technologies can fill a gap in current scaffolding of learning and potentially can serve to increase retention of students, they also change the role of higher education in a way that unlocks dependencies from locations and questions the distribution of market shares. Whereas levels 2–4 have been heavily reinforced by a sense of urgency, due to the pandemic, level 5 has instead been strongly reinforced by a perceived risk of disruption of established educational institutions.

In the 1990s Clayton Christensen introduced disruptive innovations, where disruption describes a process where a company with fewer resources is able to successfully challenge established incumbent businesses by successfully targeting overlooked segments and gaining a foothold by delivering more suitable functionality for some customers (Christensen et al., 2018). Christensen et al. (2018) summarize different response strategies to prevent such processes of disruption, which includes extending current performance-improvement trajectories, proactively repositioning in new niches, using organizational dexterity by enacting dual structures, processes and subcultures; partnering with licensing start-ups or, more fundamentally, pursuing a re-emergence strategy by redefining the meanings and values associated with their legacy.

In an engineering education context, current strategies to counteract disruption include strategies for performance improvement by use of digital tools to enhance teaching activities, digital twins of on-campus educational activities, cross-institutional collaboration, combining educational and digital specialists, and partnering with companies, e.g., using AIED. Furthermore, more fundamental re-emerging strategies are getting established, by redefining the academic institution as a much more hybrid and open entity, with students and researchers as societal agents (Jamison et al., 2014). In such mission-driven approaches, accessibility and inclusiveness are not only a part of a fundamental democratic value of educational practice, but also, it is a necessary means to create the partnerships and outlook needed to cope with the grand challenges of our time, like the COVID-19 pandemic, or the concerning implications of climate change.

Areas 1–5 address the digital competencies of teachers, which are preconditions to scaffold students in developing digital competencies. However, this extensive focus on teacher’s generic competency also highlights the extensive need for faculty development. The use of proficient professional levels, outlined in relation to the DigCompEdu framework, (Commission, 2017) summarizes the exact challenge facing engineering faculties. The first two levels picture the newcomer, having very little contact with digital tools and in need of guidance to expand their digital repertoire, as well as the explorer starting to use digital tools comprehensively and consistently. As mentioned, these first two levels might not be a problem for the technologically knowledgeable engineer, whereas the challenge arises at the third and fourth levels.

The third level outlines the integrators being able to experiment with digital tools for a range of purposes, and in specific contexts, whereas the fourth level pictures the expert being able to use a range of digital tools confidently, creatively, and critically to enhance their professional competency. The expert thereby needs a double if not triple qualification related to educational, digital, and subject-specific fields of study; they need capabilities in assessing digital technology in a user context, and abilities to do so while interfering with the field. Furthermore, the more systemic level introduced in area 5 challenges the level of progression from the expert to the leader who has such a broad repertoire of flexible, comprehensive, and effective digital strategies that they can serve as inspiration to others. At level 6, pioneers are set out to question the adequacy of contemporary digital and pedagogical practices, calling not only for a systemic but also reflective system thinking concerning digital transformation of engineering education.

Not surprisingly, progression on these levels can be overwhelming for any university teacher and, therefore, digital transformation of education is to be considered as a distributed, collaborative process. With this outset there is a need to clarify, at the institutional level, what competencies teachers should possess themselves and what competencies they should know who to consult. Depending on this clarification, the right organization and information flows can be designed to ensure coordinated action. In more popular terms, strategy, not technology, drives digital transformation (Kane et al., 2015).

Area 6, Facilitating learners’ digital competency, stresses the facilitation of students’ competencies in areas such as digital literacy, information and media literacy, communication, content creation, responsible use of information, and problem solving. Whereas areas 2–5 create the pedagogical core of the framework, area 6 links directly to students’ competencies, including that students are educated to (Commission, 2017):

• (1) Articulate information needs, to find information and resources in digital environments, to organize, process, analyze, and interpret information, and to evaluate the credibility and reliability of information and its sources, both comprehensively and critically.

• (2) Effectively and responsibly use digital technologies for communication, collaboration, and civic participation.

• (3) Modify and create digital content in different formats, and to consider how copyright and licenses apply to digital content, how to reference sources and attribute licenses.

• (4) Manage risks and use digital technologies safely and responsibly.

• (5) Identify and solve technical problems, and to transfer technological knowledge creatively to new situations.

These points relate very much to the developed European Digital Competency Framework for Citizens (Commission, 2016). Interestingly, one could ask whether these capabilities, which, besides number 5, are rather instrumental in nature, will in fact educate engineers to lead or even take an active part in digital transformation processes. This point is underlined by a systematic literature study of the digital competency of university students (Sánchez-Caballé et al., 2020), showing that most documents dealing with digital competency bring concern that authors do not believe that young people actually have the digital abilities that they are assumed to have.

The good news for engineering institutions is however that the focus on innovation—from incremental to radical innovations—provides a framing for technological change in general. We will argue that coupling the discourse of digital transformation with the one of technological innovation, creativity, and entrepreneurship, holds potential for a deeper understanding of digital literacy in an engineering context, as well as more targeted use and development of digital tools for educational purposes.

4 Creativity and Entrepreneurial Skills to Create Value

If we are to consider the art of creativity, we also must consider what we would characterize as a product of creativity. An example used in our own teaching is to show students a collection of three abstract paintings, which sold for over US$25,000 to an American art collector. We ask whether they consider these paintings a product of creativity. Due to the introduced storyline picturing a recognized and valued piece of art, students very seldom argue that this is not the case. When asked why, they typically respond in notions of originality and clear intention to provide the viewer with a new insight.

The next question is: Who is the creative one? The obvious answer is the painter—the artist. Students then get the information that the collection of pictures is painted by the chimpanzee Congo staged by the zoologist (and painter himself) Desmond Morris (Wikipedia, 2022, 2023). The introduction of Congo typically puzzles students’ pre-assumptions of the intentional act of making something creative, and it puzzles their pre-assumption of the link between intelligence and creativity.

Then students are asked about the role of Desmond Morris and their perception of his creativity. From this discussion, students typically conclude that products of creativity might not only materialize in the tangible product presented. They also recognize that there are different types of actors in the process, which, although they have different incentives, are mutually interdependent on each other.

Desmond Morris is the entrepreneur, he is intelligent and original in the sense of being the one putting Congo, a chimp, behind a canvas to create paintings of value—he is knowledgeable about animals as well as art; he is choosing the materials; he is setting the stage; and he knows the target group and how to market his product by use of the storyline of its making. Congo, they guess, was just enjoying painting.

As there are differences in the types of products and incentives and roles of actors in the creative process, there are also differences in the epistemological view of creativity. (Sawyer, 2005, 2015) distinguishes between the rational and romantic approaches to creativity. Whereas the rational approach is generated by the conscious and deliberate mind, the romanticist approach bubbles up from the irrational unconscious.

Such approaches can be mirrored by different strategies to cultivate creativity. Whereas the rationalist approach implies a pedagogical practice that uses techniques to bring forward and combine a variety of cognitive schemes, the romanticist pedagogical approach is focused on creating a secure and undisturbed environment to explore possibilities, which at the time seems to be beyond reason underpinning current practices. In the rational approach, the ability to structure and combine mental maps is a key virtue, whereas in the romantic approach, the ability to free the thought from existing mental maps and open the mind for new perceptions are keys to develop new mental maps. Referring to the example above, Congo represents the romantic approach, whereas Desmond Morris might even address both.

In a constructivist view, as conceptualized by Piaget (2013), people construct knowledge and knowledge schemas based on external stimuli. Furthermore, people enter groups and what Sawyer (2015) calls group creativity, and groups are social systems which relate to symbolic rules and procedures (the domain) and social institutions (the field) by which ideas are included or excluded from the domain (Csikszentmihalyi, 1997).

In this respect, the idea of a free mind working independently of existing mental maps and starting out from a tabula rasa can be questioned (even in the case of Congo). On the other hand, if rational approaches to creativity build on the combination of already existing knowledge, the power of imagination, foresight, and radical innovation can likewise be questioned. In a pragmatic view of the two approaches, the combination, however, makes room for different ways to reach different types of innovations. The point is that engineers must build the capacity and the courage to master both approaches to creativity to foster the different types of innovations needed.

The interplay between convergent and divergent thinking (Guilford, 1957), which typically underlines design thinking frameworks emphasized furthermore the ability to shift between different cognitive modes and the embedded interplay between order and chaos. Whereas it can be argued that engineers traditionally have been more concerned with making order following a rather reductionist agenda, leaning toward convergence rather than divergence, complex systems call for the ability to handle chaos, think divergently, and create environments that challenge current mental maps. Turning back to our example in the beginning, distinguishing the creator (Congo) from the entrepreneur (Morris), entrepreneurial competency frameworks offer frames of reference to discuss the need for more specific competencies.

As an example, the European Entrepreneurship Competency Framework (EntreComp) distinguishes between three pillars of competencies in the following way (Bacigalupo et al., 2016):

• (1) Ideas and opportunities, including the competencies related to spotting opportunities, creativity, vision, valuing ideas and ethical & sustainable thinking.

• (2) Resources, including competencies related to motivation and perseverance, self-awareness and self-efficacy, financial and economic literacy, mobilizing others, and mobilizing resources.

• (3) Into action, including learning through experience, working with others, planning and management, taking the initiative, and coping with ambiguity, uncertainty, and risk.

This example of a framework for entrepreneurial competencies expands the notion of creativity from the process of creating new ideas and opportunities to the whole process from idea to value creation. Thereby, other skills, highlighted by the World Economic Forum (World Economic Forum, 2020), such as leadership, social influence, and resilience come into play. Distinguishing between learning ‘about’, ‘for’, or ‘through’ entrepreneurship, Hannon (2005) and Thrane et al. (2016) argue for a learning ‘through’ strategy, where the learning experience is seen as a co-evolutionary process in which the individual becomes an entrepreneur as they transform disclosive spaces into opportunities. The term disclosive spaces is used by Charles Spinosa, Fernando Flores, and Hubert Dreyfus to refer to the socially inscribed contexts in which cultural innovation takes place (McLaughlin, 2006).

If the case of engineering education, the learning ‘through’ can be obtained by letting students experience and reflect on an entrepreneurial experience related to the development of new technology. With reference to the EntreComp framework, students have to go ‘into-action’ and the teacher’s role is to frame the learning experience and facilitate students to mobilize the needed resources (and if possible, to provide easy access to resources) and the relevant actors (Bacigalupo et al., 2016).

The learning ‘through’ aspect of becoming also relates to the above EntreComp competency of self-awareness and self-efficacy. Besides the notion of learning ‘about’, ‘for’, and ‘through’ education, Mäkimurto-Koivumaa and Belt (2016) underline the importance of experiencing entrepreneurship by adding the preposition of learning ‘in’ entrepreneurship to stress the importance of a real-life experience.

The Entrecomp framework also touches upon the so-called life skills, as referred to by the Partnership for 21st Century Skills (P21) including personal qualities, such as taking initiative and coping with ambiguity, uncertainty, and risk (Bacigalupo et al., 2016). The focus on life skills presents a move toward affective learning outcomes including attitudes, motivation, and values. Life skills also relate to the industrial emphasis on resilience, as presented in the top ten list of skills needed by 2026 by the World Economic Forum. The focus on resilience can be seen as a recognition of the insecurity that follows complex technological systems as they unfold more and more wicked problems with higher urgency and complementing needs for quicker decision-making processes.

Resilience refers to a class of phenomena characterized by good outcomes in spite of serious threats to adaptation or development. In an educational context, resilience can be phrased as a matter of addressing, reflecting on, and coping with complex challenges in a way that results in good outcomes both in terms of personal and organizational long-term development and well-being. At the organizational level, resilience at least implies an appropriate capacity of qualified human resources with access to the resources necessary to succeed. Resources include both financial and natural resources, hardware as well as software, and finally, knowledge and supply networks. At the individual level, human resources are further elaborated in the Entrecomp framework, e.g., in terms of perseverance, self-awareness, and self-efficacy.

There can be different strategies to uphold resilience. In an educational setting, students build and maintain resilience in different ways and with different types of appreciation as a response (Fig. 4.1 provides an example of such). A traditional engineering curriculum leans toward appreciation of hard work and the ability to apply and develop new theories, methods, and tools. These virtues are, as a plus, also more easily measured in the assessment of student learning outcomes than more affective and interpersonal learning outcomes, which are often tacit and harder to point to, measure, and appreciate.

Fig. 4.1

Example of different strategies for students to foster and uphold resilience together with different types of appreciative responses (Holgaard, 2019)

Some institutions have tried to integrate intended learning outcomes pointing to students’ ability to use and develop their knowledge networks across disciplinary borders or their ability to collaborate in a way that nurtures healthy work environments with higher performance levels. These capabilities are central in the 21st-century skills and in the future skills pointed out by industry partners, but there is a risk that the lack of assessment methods in higher education will limit students, and especially surface learners, who might lack the motivation to develop such skills specifically. This can result in lost potential to build resilience during education.

In the context of safety management and in alignment with the sociotechnical system perspective, Patriarca et al. (2018) present an extensive literature review of resilience engineering (RE). The findings show significant appearance of arguments for a new paradigm in terms of handling complexity across organizations due to increasing organizational flexibility, but at the same time, limitations occur in the ‘knowledge for action’ literature making it hard to operationalize RE. Consciousness on system dynamics was pointed out as playing a crucial role, and furthermore, it was stressed that resilience is not just about being able to adapt, it is also about being able to obtain stability after a transformation process. In more popular terms, adaptation and robustness go hand in hand.

In relation to an educational setting, a design-based study of challenges in entrepreneurship education points out that lacking resilience is one of the core challenges for students together with tunnel vision and a lack of boundary work (Holgaard et al., 2022). The iterative ability to move back and forth between stability and transformation, between the disciplinary and the interdisciplinary, seems to be as hard as it is necessary to address complex sociotechnical systems. However, as resilience is fundamentally important to transform ideas into value in engineering, more attention could be expected and recommended in the reshaping of engineering curricula.

Finally, it should be stressed that entrepreneurship is a matter of both individual and collaborative learning. In the entrepreneur paradigm, entrepreneurship is viewed as a creative act and an innovation in itself (Zhao, 2005). As we have introduced creativity as an individual as well as social act embedded in entrepreneurship, and as mobilizing and working with others is considered as a core competency in entrepreneurship, we view entrepreneurship as a social process relying on individual agency. Understanding entrepreneurship as a social process is evident—an engineer might become an entrepreneur, but they can never work in isolation. Their understanding of their interdependence with others is crucial to cope with the distributed innovation processes of complex technology systems.

5 Focusing on Societal Needs and End-User Requirements is a Priority

While user satisfaction has always been within scope for technological innovation, there are considerable changes occurring, to clarify user needs. A ‘more-is-better’ consumer-centered mass production perception of the user has gradually been undertaken through an increased focus on product differentiation, to offer products and services to satisfy specific user needs. User-centric design approaches have emerged and have provided methods to analyze ‘user needs in context’. Later, user-driven approaches developed and emphasized users as important actors in co-design. Entering the Industry 5.0 era, this co-creation process seems to emerge even further in the use phase itself, whereas technology is seen as an answer to address personalized user behaviors.

Together with the increasing focus on personalization of technology, grand challenges on the societal level have called for urgent action. This means that the lead focus on user behavior is basically questioned and even regulated for the sake of the common good. The more complex and the more urgent the grand challenges have become; the more attention has been given to engineering to address more abstract societal needs. More reactive approaches to technology assessment (TA) have been supplemented by more proactive approaches like constructive technological assessment approaches (CTAs) interfering with user needs even in the design phase (Rip et al., 1995). Recently, CTA approaches have been even further elaborated, for example by suggesting ethical constructive technology assessments (eCTAs) (Kiran et al., 2015).

This development, which spans from the personalized to the societal level, challenges the engineering profession, as engineers of the future are expected to have the ability to address and connect different contextual layers. As noted by de Carvalho Guerra & Holgaard (2019), contextual layers in engineering and science studies include contexts of technology in materialized form (e.g., the context of use), contexts of technology in an institutionalized form (e.g., standardization), and contexts of technology in a discursive form (e.g., a public debate). As coordinated action is needed to address global challenges and as the impact of social media is increasing, a user-centric approach is too limited. For engineers, this means that more contextual layers must come into play and considerably more contextual knowledge is needed.

Aspers (2006) points to three qualitatively different dimensions of context. While the first-dimensional concerns networking, the second stresses the aforementioned emphasis on the end user in terms of ‘Knowing the Final Consumer market’. The third dimension stresses the importance of ‘Knowing How to Interpret Provinces of Meaning’ (Aspers, 2006, p. 755), whereas ‘provinces of meaning’ refer to information embedded in different sources of inspiration. Besides moving attention to other ‘provinces’ than those centered on users, this dimension also indicates that creativity and divergent ‘out of the box’ thinking are important for questioning established boundaries and dominant relations.

Heikkinen (2018) argue that what is needed is knowledge workers that have the skills to cross boundaries and, inspired by the T-shaped expertise (Conley et al., 2017), he argues that there is a need for these knowledge workers or so-called T-shaped professionals to possess deep disciplinary knowledge along with the ability to communicate across, for example, social, cultural, and economic boundaries. Working from knowledge depth in one discipline, the challenge is to create understanding of and communication with many disciplines and many systems using so-called boundary-crossing skills (Heikkinen, 2018).

The illustration of the T-shaped professional offers a frame of reference when considering how to further emphasize societal needs and end-user requirements in engineering education, but it also raises areas of concern. How should the boundary crossing between design and engineering be arranged when addressing user needs in technological innovation? How should the boundary crossing between engineering and social science and humanities be arranged when addressing societal needs in engineering education? What level of understanding is needed for the engineer to move horizontally in the T? What is needed for the engineer to stay deep in the analytical thinking and problem solving of their discipline? And, finally, which curriculum strategy is flexible enough to combine the horizontal and vertical dimensions of a T-shaped engineer?

A way to start is to relate the design process and the science, technology society perspective as a framing of the T-shaped engineer. How well it is performed depends on the interdisciplinary skills required at key stages, e.g., stakeholder engagement, leading to requirements, generation of solutions, choice of solutions, etc. It’s the ability to grapple with the social and environmental elements, as well as the technical and economic, that showcases interdisciplinarity. The overlay on the above T-shaped profile in Fig. 4.2 illustrates how such things come together through design.

Fig. 4.2

Reproduced from the adapted version from the T-Summit, 2017 presented in Heikkinen (2018) with the addition of the design process

Concept of a T-shaped professional.

Jamison et al. (2014) argued that a curriculum strategy that embraces contextual and transformative processes, including complex mapping of the appropriation of technology into society, has to move beyond acquisition of knowledge and practical training. It is not only a matter of knowing about, and being able to use, specific methods for, as an example, collaborative design or technology assessment. It is an identity formation process taking into consideration the interplay between scientific, technological, social, and environmental dimensions of engineering, and it is a matter of being enabled to point to dynamics, synergies, trade-offs, and potential controversies of importance in this contextual landscape.

To avert an overcrowded curriculum, there is tremendous pressure on engineering education designers to select curriculum content in a way that creates the foundation for exemplary learning. Thereby students can acquire skills to situate content and methods (Klafki, 2007), and be able to transform learning experiences to other situations of relevance, meaning that exemplify relevant societal, material, and social constructs (Negt, 1974). For example, the way that we iterate, reframe, and co-create might be quite transferable across disciplines. Nevertheless, there is a risk that the attention toward such transferable processes is blinded by instrumentalism related to each of the theories and methods.

Furthermore, the shifting focus toward identity formation implies a new social role for engineering, which, according to Jamison et al. (2014), is that of the change agent, or social reformer, whose competency and professional identity consists of knowing how to adapt theory and professional practice to the specific sites in which technologies are to be used. It is an interplay between theory and practice, and an interplay between what is perceived to be and what is imagined to be. It is a transformative learning process, which is a process of examining, questioning, and revising our perceptions of our experiences (Taylor & Cranton, 2012, p. 6). Not surprisingly, a curriculum focused on transmission of mostly technical know-how runs short in this transformative turn of pedagogy.

6 Summary

In this chapter, we have presented diverse future engineering competencies which challenge current understandings of what it means to become an engineer. The new understanding leans toward a more dialectic and contextual approach to engineering requiring an ability to relate to and make synergy of a multitude of interests as well as epistemologies. We argued that the Science, Technology, and Society (STS) methodology offers opportunities to contextualize engineering practice. We also point out that independent of the chosen methodological framework an instrumental view to future engineering competencies will not be sufficient to capture the complexity of human systems. The interdependencies in these systems are too important to be seen as linear and a matter of use. Methods and tools generated in other knowledge domains cannot just be borrowed as the challenge is to reshape the dialectics of knowledge systems for changing contexts.

In this reshaping process, we have pointed to a set of overarching competencies. Critical thinking and sense making are needed to examine, assess, and make reason of and not the least develop the interference between technology and society, between constructing and co-constructing, between self-reflexivity and enactment. We must expand our knowledge above professional engagement.

One of the upcoming challenges is the rethinking of digital resources in engineering practice as well as in engineering education. Yet again the challenge is not as much to learn how to apply digital resources but knowing how to reshape our mindset and select the right strategies to frame digital competencies to their context of use. Another core area of future engineering competencies is related to creativity and entrepreneurial. This is a skill of shifting between different cognitive modes and social spheres to create whatever is perceived to be valuable.

We have stressed that a focus on end-users’ requirements should be a priority, but as values of our time include grand challenges as sustainability the trade-offs moves beyond the user of technology as is, to also include the impact of the technology on current as well as future generations and natural environments. This means that the entrepreneurial competencies and the move from idea to value creation have increased in complexity to a point where cognitive modes of creativity are pillars to social modes of entrepreneurship.

Engineering students must be capable to cross boundaries among the many disciplines and the many systems as well as to keep up the depth of disciplinary knowledge. This is a matter of competency management and competency development across borders. The previously addressed frameworks for system and design thinking offer suitable outsets to rethink contextual integration in engineering, but still the competencies to handle the process of contextualization continue to change due to the rapid changes in the technological context. We cannot transfer from one comparable situation to another, as contextual complexity has increased. We must integrate and transform what we know, what we do, and how we perceive ourselves as engineers.