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Global trends in disruptive technological change: Social and policy implications for education

John W. Moravec, Ph.D.

María Cristina Martínez-Bravo, Ph.D.

Note: This is an institutional archival copy of an author-accepted manuscript (01-Aug-2023) for the journal On the Horizon. This manuscript is released under the Creative Commons Attribution Non-commercial International Licence 4.0 (CC BY-NC 4.0). To reuse this article for commercial purposes, permission should be sought by contacting permissions@emerald.com.

How to cite the published edition of this article: Moravec, J. W. & Martínez-Bravo, M. C. (2023). Global trends in disruptive technological change: Social and policy implications for education. On the Horizon, 31(3/4). https://doi.org/10.1108/OTH-02-2023-0007



The purpose of this study is to identify global trends in disruptive technological change and map the social and policy implications, particularly as they relate to the educational ecosystem and main stakeholders across all levels of education.


The authors conducted a two-stage meta-analysis of 1,155 scholarly, peer-reviewed articles. The investigation involves: 1) a systematized literature review for data identification and collation adhering to defined selection criteria, and 2) a network analysis to scrutinize data, consolidate information, and unveil correlations and patterns from the literature review to produce a set of recommendations.


The study unveiled educational trends related to disruptive technologies and delineated four principal clusters representing how these technologies are transforming the education ecosystem. Additionally, a series of transversal aspects that reveal a societal vulnerability towards future prospects in the realms of ethics, sustainability, resilience, security, and policy were identified.

Practical implications

The findings spotlight an enlarging chasm between industry (and society at large) and conventional education, where many transformations triggered by disruptive technologies remain absent from teaching and learning systems. The study further offers recommendations and envisions potential scenarios, urging stakeholders to respond based on their positions concerning disruptive technologies.


Expanding from the meta-analysis of pertinent literature, this paper offers four collections of curated resources, four mini case studies, and four scenarios for policy makers and local communities to consider, enabling them to plot courses for their optimal futures.


Disruptive transformations through technologies impact all elements of human society. The educational ecosystem is also modified by different factors, which on the one hand require new literacies to cope and lead through the social and economic development challenges of today's world; and on the other, centripetal forces and centrifuges direct not only what is learned, but also how, where, why and for what, since their own relationships with the world challenge what is known towards a disruption, transformation, innovation and subsequent revolution (Autio, Mudambi, & Yoo, 2021).

This article summarizes findings from the first of a two-phase study commissioned by the Prague Innovation Institute on social and policy implications for disruptive technological change at a global level1, identifying key trends and clusters.

To identify the most recent academic literature around disruptive technologies a meta-analysis was conducted, which included the exploration of 1,155 peer-reviewed, academic articles published between January 1, 2020 and June 5, 2022. As a quantitative, systematic approach to data analysis, meta-analysis generates insight into overall trends from independent sources of data to build an interconnected perspective of a whole. “Meta-research involves taking a panoramic view of science,” enabling a macroscopic look into broader trends (Ioannidis et al., 2015).

For this study, the meta-analysis consisted of two technical phases, producing two products:

  1. Systematized literature review - Selection of articles by criteria. For the analysis and systematization of different perspectives of research on education, a systematic approach was applied (Booth et al., 2012), which allows collecting, identifying, selecting and analyzing data in an adequate and reliable manner (van Laar et al., 2017).

    The objective of this initial phase was to conduct a comprehensive literature review, ensuring data identified adheres to the selection criteria, thus uncovering significant contributions of scholarly literature relevant to our research (Grant & Booth, 2009). The selection criteria included: a) core keyword: "disruptive technology"; b) academic databases: Web of Science and Scopus; c) publication period: 2020-2022; d) document type: academic articles; and, e) discipline: education and humanities.

  2. Network Analysis for identifying key trends and clusters. This secondary phase emphasizes processing and interpreting key contributions post literature identification conducted in the first phase. The network analysis explored relationships between articles, identifying trend clusters and highlighting patterns within the analyzed literature. This method, often applied to discern links between network entities (Grandjean, 2016), provides a synthesis of information, revealing connections, patterns, and initially allowing the identification of education-related trends in disruptive technologies. Further, it facilitates the construction of research approach clusters within education, highlighting thematic connections, and cross-cutting aspects of disruptive technologies and their implications for education and policy.

Both processes generates a graphical representation and a semantic interpretation of data derived from metadata (see Martínez Bravo, Sádaba, & Serrano-Puche, 2020, for a comprehensive methodology description). The employed methodology enabled the researchers to answer how various technologies are altering the educational landscape (trends), and their effects on the landscape (identified as clusters of social and policy implications) according to the analyzed literature.

The findings of this article are structured in a format conducive to policymakers and practitioners. Firstly, global trends are identified and detailed in five distinct sections. This is followed by a meta-analysis summary of the literature that mapped four clusters representing the influence of disruptive technologies on educational ecosystems. In this latter section, each cluster contains a list of challenges, key questions, relevant readings, and brief case studies. Concluding the article, four scenarios are suggested to guide policymakers and others in strategizing for futures shaped by increasingly disruptive technologies.


Meta-analysis of recent literature about education: trends, approaches, and implications

Five global trends

Five key categories were identified where disruptive technologies are framed as having a global impact. While connections to education for each of these categories may not be immediately clear or direct, they help to illustrate the broader context in which education systems operate and serve. In addition, many of these disruptive categories and their constituent elements are interconnected.

First: Accelerating technological progression among machine systems reflects an understanding that as technologies evolve and improve, costs decrease, and in turn, lead to further, accelerated advancements. As noted by Moravec (2013), accelerated technological change feeds into social change, which leads to a feedback loop that pressures the further development of technologies. Of particular interest to this literature search, three primary drivers of accelerating change were identified:

  1. Artificial intelligence is centered on the development of computer systems that can perform tasks that have relied on human intelligence to perform in the past, including algorithms that perform decision-making. Yang et al. (2020) write of “federated machine learning,” leading to a “connected intelligence” among various devices and systems. Such a broader network of artificial intelligences has the potential to fuel an Internet of Things (IoT) to interconnect, for example, “self-driving cars, unmanaged aerial vehicles, healthcare, robotics, and supply chain finance” (p. 15). Such applications that have traditionally relied on human expertise may now benefit from automation.

  2. Machine learning relates to artificial intelligence in that the concept is centered on computer systems employing algorithms and/or statistical analyses to make sense of complex sets of data. In some fields, such as healthcare, machine learning is used to analyze medical data and help humans make decisions. Farahani, Barzegari, Shams Aliee, & Shaik (2020) propose that future directions in the healthcare space could enable approaches to machine learning that is collaborative across devices (similar to IoT) to “provide real-time actionable insights which ultimately improves the decision-making power” or healthcare providers.

    Machine learning also connects with the idea of “big data,” where large, complex, diverse, and interconnected datasets are analyzed computationally to reveal patterns, signals, and insights that may not be apparent with any subset of the available data or without advanced computation. Such analyses have implications across various sectors (e.g., healthcare, agriculture, marketing), from providing insight into individual behaviors, to providing macroeconomic and social insights. While organizations and societies may benefit from the use of big data technologies, communities that do not have access to such technologies are at an increasing disadvantage, building possibilities for burgeoning data divides and expanded needs for digital inclusion policies (see, for example, Pawluczuk, 2020).

  3. Digital transformation relates to the broader adoption of digital technologies by organizations and societies, blending their use in practice to help make decisions and gain efficiencies. In many sectors, from education to healthcare, rapid advancements in digital transformation were prompted by the COVID-19 crisis. With 1.6 billion children out of school globally, resulting in a learning loss with an estimated net impact of USD 17 trillion over the children’s lifetimes (World Bank, 2022), schools scrambled to find new solutions to connect with students at a distance. In an opinion paper, Kamal (2020) noted that although the embrace of digital transformations in organizations were rapid and intended to prevent a loss of productivity, underlying complexities within organizations presented new challenges—highlighting difficulties in adapting human systems to operate within machine environments.

In education, the convergence of these technologies is often referred to as “Education 4.0,” where predictive models of student achievement may be constructed, but also where teaching and learning approaches may be optimized. López-Bernal et al. (2021) caution, however, technologies need to complement human sensemaking and problem-solving:

[...] one of the main pillars of Education 4.02 is not only to teach students the theory behind disruptive technologies, such as [machine learning], but also to provide the learning material so that they can have the opportunity of applying the acquired knowledge to develop problem-solving skills, as well competencies such as design mindset, transdisciplinary approach and computational skills. (p. 6)

Second: Transformations in context and reality relate to disruptions in how people perceive and interact with the world. Leveraging these technologies can enhance the flow of information a person receives, change the context in which one interacts with the world, or present a completely different “reality” altogether. Within this category, four key concepts were identified:

  1. Augmented reality (AR) is an approach of enhancing perception of the world by adding real-time sensory information, i.e., sound, images, haptic feedback, and other sensations delivered through geospatial and context-aware technologies. The compositing of information with naturally observed stimuli allow users to interact with and build knowledge about their environment across various modes. For example, AR-enabled eyeglasses can add a layer of information on top of what a user sees through the lens alone, such as providing turn-by-turn directions while walking through a city, or providing virtual, interactive interfaces for interacting with a computing device through hand gestures.

    Lester & Hoffman (2020) note that while AR technologies are still in their infancy, great potential exists for employing them in education. They theorize the “effects include greater learner control of the leaner process and encouragement of reflection in action” (p. 646). In vocational learning spaces, AR allows learners to practice with virtual, 3-dimensional objects, and early research is showing growth in learner comprehension (see esp. Putra et al., 2021).

  2. Virtual reality (VR) relates to AR in that it provides new sensory inputs to the user but differs in that it substitutes one or more senses of the real world with one that is artificially produced. VR technologies therefore have the possibility of producing entirely new, virtual environments that are accepted as “real” by the user.

    Applications of VR in learning include simulation environments across a variety of industries. Trade laborers, for example, can practice using welding equipment in virtual environments, surgeons can participate in training and remote surgeries using VR equipment, and pilots can train for a variety of situations in virtual trainers. On a broader, social level, however, VR technologies are increasingly employed in gaming through the adoption of VR headsets and integration within the “metaverse.”

  3. The metaverse is an imagined space, moderated by machines, providing both VR and AR experiences in an internet-connected broader “world.” As Hwang & Chien (2022) explain:

The metaverse has been recognized as being the next generation of social connection. It refers to a created world, in which people can “live” under the rules defined by the creator.

Jagatheesaperumal, Ahmad, Al-Fuqaha, & Qadir (2022) argue the integration of services in the metaverse presents ideal opportunities for education:

The vision of metaverse is driven by advances in technologies such as artificial intelligence (AI), extended reality (XR), and internet of everything (IoE). [...] Education is also one of the domains where the use of the concept/technology is getting momentum with metaverse promising several advantages. For instance, it allows students and teachers from different parts of the world to meet up in a virtual environment regardless of their real-world location. Similarly, building virtual landscapes on the basis of the teacher’s lesson plans provides better opportunity, resulting in an improved and more productive learning experience for the students. (p. 1)

Hwang & Chien (2022) further explain describe capabilities of the metaverse in learning contexts:

  1. “To constantly situate learners in a cognitive or skill practicing environment that could be risky or dangerous in the real world.

  2. To constantly situate learners in the contexts to experience and learn what they generally do not have the opportunity to be involved in the real world.

  3. To enable learners to perceive or learn something that requires long-term involvement and practice.

  4. To encourage learners to try to create or explore something that they cannot afford to do in the real world owing to some practical reasons, such as the cost or the lack of real materials.

  5. To enable learners to have alternative thoughts and attempts regarding their careers or lives.

  6. To enable learners to perceive, experience, or observe things from different perspectives or roles.

  7. To enable learners to learn to interact and even collaborate with people that they might not have opportunities to work with in the real world.

  8. To explore the potential or higher order thinking of learners by engaging them in complex, diverse, and authentic tasks.”

  1. Gamification is an approach to changing the context of an environment by adding game mechanics. Such approaches are used by product developers to entice preferred behaviors by employees, customers, or other stakeholders. In education, gamification of learning is built on adding game elements to educational experiences to incentivize learner engagement—and, in theory, to help improve learning outcomes.

    Barahona Mora (2020) identifies key elements as including: Interface design patterns (i.e., badges, levels, leaderboards); game design patterns or game mechanics (time constraint, limited resources, turns); design principles or heuristics (guidelines for solving a problem or evaluating a solution); game models (i.e., fantasy, curiosity); and implementation of game design methods. Such elements may help create a more open, playful learning experience while incentivizing learners to solve problems actively rather than through traditional, passive learning modes.

The increasing adoption of these approaches to context transformation and reality creation provides possibilities to form preferred realities in both simulational and social contexts. A number of experts warn (see, for example, Rondeau, 2020), however, the ease in which new, preferred realities may be created aids in divorcing individuals from realities of the real world. This manifests in the production of “alternative facts” where lies are presented as truth, and where inconvenient truths may be dismissed as “fake news.” Navigating easily manufactured disinformation or reality manipulation presents challenges for education systems that are charged with preparing students for the real world.

Third: Transformations in industrial production change the context in which schools must prepare students for work. Key concepts include:

  1. Industry 4.0 refers to a conceptualized fourth industrial revolution (“4IR”), based on leveraging interconnectivity among technologies and contexts, together with technologies that provide automation, such as AI and robotics. Hermann, Pentek, & Otto (2016) note that three additional themes drive Industry 4.0, built from interconnection: information transparency provided by interconnected data sources, technical assistance provided by machine systems, and decentralized decision-making by autonomous and semi-autonomous systems.

    Manifestations of Industry 4.0 are found in technology-assisted manufacturing (e.g., 3D printing and rapid prototyping, see additive manufacturing, below) and the pervasive use of sensors and data to optimize and automate production, including maintenance of machinery and supply chain management. In the education sector, Oka & Fernandes (n.d.) note the education sector has not shifted toward supporting a transition to Industry 4.0 and that thinking about how technology is approached in education must shift:

Rather than perceiving digital technology as a network of IT infrastructure, there is a need to conceive 4IR as a digital learning environment where learners are the central focus in that ecosystem through the “technology-enabled learner-centered” approach.

  1. Additive manufacturing is a process in which computer-aided-design (CAD) systems are used to fabricate 3-dimensional objects by depositing materials in precise locations. Commonly referred to as 3D printing, additive manufacturing affords the possibility to prototype and produce complex objects that in previous manufacturing processes would require complex dies, machining, or milling to complete. Automatization of the manufacturing process and use of CAD systems further allow possibilities for rapid prototyping where possible products or solutions can be fabricated and tested in a short time.

  2. Smart cities leverage interconnectivity, sensors, and automated data analysis to aid in the governance and planning of urban communities. As an extension of IoT in complex, social spaces, together with other technologies such as blockchain (see below), artificial intelligence, and machine learning, recommendations and automated actions in key areas with scarce resources such as healthcare, energy, transportation, education, and public safety may be realized (Radu, 2020).

Changes in industrial production and societal governance pose the question, can schools that are focused on preparing for development in traditional knowledge areas and careers prepare students who are relevant in Society 4.0?

Fourth: The decentralization of data networks spreads computing and data services across multiple devices, eliminating reliance on a central, authoritative server. More importantly, they are built on a model where no single device is trusted more than others.

A blockchain is a chain of encrypted blocks of data, shared across large peer-to-peer networks. As blockchains are designed so blocks can only be created, and not destroyed, blockchain systems serve as a cryptographically verifiable, persistent, and often completely open ledger. Each machine on the blockchain may hold an entire copy of the ledger, eliminating the need for any central authority. One popular application for blockchain ledgers is cryptocurrency.

Given the decentralized nature of blockchain technologies, cryptocurrencies are often seen as being functionally independent of any single institution, including government regulators or management. Digital identities may also be managed in a blockchain network, in addition to immutable, persistent records about any person.

As decentralized networks, blockchain technologies have attractive applications in education. Ullah et al. (2021) argue, “the disruptive technology preserves its tremendous benefits in creating a smart learning environment,” and such technologies would be beneficial in supporting ubiquitous learning environments—that is, embedding learning in all aspects of society and life, where learning experiences and credentials are stored in an open, public network.

Looking at the perspective of traditional higher education, Fedorova & Skobleva (2020) note:

[…] nowadays there is a tendency to accumulate the entire range of university functions in blockchain projects: administration of the educational process; storage of information related to degrees, scholarships, etc, creation and maintenance of students’ and graduates’ portfolios; a large-scale application of operations with cryptocurrency (up to investment projects); realization of opportunities that the new pedagogics provides. The most important advantages of educational blockchain technologies are formation of a single educational environment, creation of network communities, exchange with technologies and scientific knowledge, and copyright protection of the network participants.

Fifth: Mobile information systems disrupt the time and place in which data and information are gathered and distributed. The “anytime, anywhere” aspects of mobile technologies permit the broader context in which information may be applied.

Mobile learning (or M-learning) leverages information systems to provide mobile educational experiences. As a form of distance of education, M-learning breaks the delivery and acquisition of information beyond the confines of designated learning spaces, replacing traditional classroom technologies (i.e., blackboards, desks, books) with mobile devices such as laptops, tablets, or cell phones. More importantly, M-learning approaches may be combined with other disruptive technologies to provide enhanced experiences. Putra et al. (2021) note that the coördination of technologies for solutions allow for new foci to be placed on learning, and, “because of the increased access to information, the position of digital technology narrows the globe” (p. 2).

Connections among the five trends

Care was taken to describe each of the above five, global trends on their own, but in real world manifestations, they are often entangled with each other. The metaverse cannot thrive without mobile information systems, for example. Nor could the blockchain without industrial social pushes to interconnect systems.

Among the greatest challenges facing education is that the use of disruptive technologies is relatively unsophisticated (see esp. commentary by Putra et al., 2021), and when technology is used, it is often misused, blocking any authentic learning that could be made possible with technologies (Cobo & Moravec, 2011). Whereas in the majority of socioeconomic sectors, these disruptive technologies transform how we live and work, very little has been conceived on how these technologies can transform how we learn within formal systems3. Instead, technologies are most often used to support legacy systems and approaches, not to complement or replace them. In this sense, education systems may be perceived as resistors to technological change.

Technologies shaping educational ecosystems: challenges and implications

Utilizing network analysis in conjunction with a clustering algorithm, specifically the 'modularity class' in Gephi Open Software, we are able to discern thematic relationships within the research. This method highlights patterns in the literature, which coalesce around four focal points: education in the context of global changes; digital transformation in the education sector; the challenges and objectives of organizations regarding their thinking models; and the key players and ecosystems within education.

Each cluster is visualized and grouped by color in the data representation. Within each cluster, keywords of varying relevance facilitate the identification of patterns, further linking them in the database to corresponding articles.

Figure 1. Map of education and disruptive technologies analyzed, based on data extracted from Web of Science and Scopus.

A thorough analysis was conducted on 1,837 keywords (referred to as nodes) from the literature, these shared 165,588 interconnections (termed as edges). Utilizing computational network analysis, four clusters were discerned, accounting for 97.22% of the relationships in the network. This network is visually depicted in Figure 1, illustrating a clear demarcation between key concepts. It provides an inclusive view of the ecosystem of disruptive technologies pertinent to education and echoes the global trends previously identified.

This process delineates the ecosystems shaped by technology, spotlighting related literature and subsequently, the challenges and cases discovered. Notable literature within each cluster is highlighted, gleaned from the list of articles assessed during the meta-analysis. Simultaneously, a compilation of key questions is crafted to guide practical application.

Cluster A: Adaption of national systems to a new, global reality

Figure 2. Visualization of Cluster A: The relationships in this cluster represent 28.25% of connections analyzed, where the most important nodes (keywords) are linked to social innovation within a framework of development in a global environment. Note. Based on data extracted from World of Science and Scopus, analyzed in Gephi.

As illustrated in Figure 2, the development of countries is built on an increasingly volatile, uncertain, complex, and ambiguous global system (“VUCA” world as popularized by the US military in the 1980s), which some prefer to describe as brittle, anxious, nonlinear, and incomprehensible (“BANI” as proposed by Cascio, 2020). We are faced with global systems and infrastructures that, while providing societies with multiple opportunities, also expose them to a state of vulnerability. Planning for and operating within this ecosystem is the first challenge facing the education sector.

Ecosystem, change, and challenge are three keywords that stand out in the first cluster which require special attention as they imply consideration of interconnected, global axes:

  1. Technological ecosystem: in this axis, we have the global infrastructures of interconnectivity, storage, distribution and supply, as well as technologies on which new systems are based. Within these areas, artificial intelligence and blockchain stand out.

  2. Political ecosystem: Globalization is not only a cultural and economic phenomenon, but also a political one, which demands a long-term view regarding disruptive events such as the COVID-19 pandemic and climate change, requiring a new approach to governance, globally. In this sense, political territories also acquire "new dimensions" or "extended" visions that invite us to think about issues such as submarine and terrestrial fiber optic infrastructures (Internet backbone), cybersecurity, or technological independence in a more comprehensive way. Realizing and connecting local issues with this global perspective requires a glocal orientation. In other words, the ecosystem of innovation and disruptive technologies implies the development of policies in accordance with a global reality, without losing sight of the local perspective.

  3. Sustainable ecosystem: Global disruptive technologies cannot thrive without a long-term sustainable perspective, implicit development of policies, and connecting of global development agendas.

  4. Global training ecosystem: The global nature of information (and disinformation), systems and technologies, large-scale interactions, and flows require skills according to the nature of the challenges faced, which go beyond technological skills and rely heavily on the application of knowledge. Civil education, rights, ethics, identity and autonomy, emotional well-being, and intercultural aspects set the tone for chaordicleadership (Hock, 1999) and the emergence of 21st century knowmads (Moravec, 2013).

  5. Interconnected ecosystems: Education cannot be disconnected or isolated from other systems such as industry, business, public, science, technology, society, and the broader economic development of nations, which, in turn, extend global communities and dynamics.

Relationships in this cluster suggest nations are facing the sum of centrifugal and centripetal forces, which demand a deep analysis, since these synergies ground national educational agendas. The five axes of action bring us closer to a future where the great challenge for regulation and policy design is to ensure that disruptive technologies are not disconnected from the social challenges embodied in the educational system. These policy actions may be leveraged to fuel social innovation.

Although the pandemic brought a "new normal" that forced the world to adopt technologies rapidly and readapt their applications, the success behind a true educational revolution is based on the social strategy of the system; that is, technological strategies must be tied to the socio-educational challenges of the 21st century, where true disruption merges social development with technological development.


Key questions

Case study

Switzerland Innovation Park area

The Switzerland Innovation Park, branded as "a location for shaping the future," where the primary campus operates as "a hub for life + science," exemplifies the power of collaboration, knowledge sharing, the unification of diverse participants, and the constant synergy of goals and projects. Such a collaborative environment can foster social innovation that stays at the cutting edge of emerging technologies within a global context. Supported jointly by private funding, federal and cantonal governments, universities, and research institutes, "Switzerland Innovation" serves as a model for transformative progress.

In this context, learning spaces, schools, and classrooms are designed to interconnect with various participants of the knowledge ecosystem. These spaces function as hubs or experimental laboratories, wherein strategic alliances frame the learning process, allowing knowledge to be explored and tested dynamically.

More information: https://sip-baselarea.com/

Key literature connected to this cluster

  1. Hopster, J. (2021). What are socially disruptive technologies?. Technology in Society, 67, 101750. https://doi.org/10.1016/j.techsoc.2021.101750.

  2. Linor L. Hadar, Oren Ergas, Bracha Alpert & Tamar Ariav (2020) Rethinking teacher education in a VUCA world: Student teachers’ social-emotional competencies during the Covid-19 crisis, European Journal of Teacher Education, 43(4), 573-586, https://doi.org/10.1080/02619768.2020.1807513

  3. Autio, Erkko; Mudambi, Ram; Yoo, Youngjin. (2021). Digitalization and globalization in a turbulent world: Centrifugal and centripetal forces. Global Strategy Journal, 11, 3-16. https://doi.org/10.1002/gsj.1396

  4. Scheufele, D. A. (2022). Thirty years of science–society interfaces: What’s next?. Public Understanding of Science, 31(3), 297-304.

  5. Valcarce, M. (2021). Del VUCA al BANI, el nuevo entorno que nos toca vivir. https://www.valgo.es/uploads/app/163/elements/file/file1632993156.pdf

  6. Oke, A. ( 1 ), & Fernandes, F. A. P. ( 2 ). (n.d.). Innovations in teaching and learning: Exploring the perceptions of the education sector on the 4th industrial revolution (4IR). Journal of Open Innovation: Technology, Market, and Complexity, 6(2). https://doi-org.ezproxy.unav.es/10.3390/JOITMC6020031

  7. Bao, L., Krause, N. M., Calice, M. N., Scheufele, D. A., Wirz, C. D., Brossard, D., ... & Xenos, M. A. (2022). Whose AI? How different publics think about AI and its social impacts. Computers in Human Behavior, 130, 107182.

  8. Tan, J., Wang, L., Zhang, H., & Li, W. (2020). Disruptive innovation and technology ecosystem: The evolution of the intercohesive public–private collaboration network in Chinese telecommunication industry. Journal of Engineering and Technology Management, 57, 101573

    Akpan, I. J., Udoh, E. A. P., & Adebisi, B. (2022). Small business awareness and adoption of state-of-the-art technologies in emerging and developing markets, and lessons from the COVID-19 pandemic. Journal of Small Business & Entrepreneurship, 34(2), 123-140.

  9. Foladori, G., & Ortiz-Espinoza, Á. (2022). The capital-labor relationship in Industry 4.0. Íconos. Revista de Ciencias Sociales, (73), 161-177.

  10. Dvoráková, L., Horák, J., Caha, Z., Machová, V., Hašková, S., Rowland, Z., & Krulicky, T. (2021). Adaptation of small and medium-sized enterprises in the service sector to the conditions of Industry 4.0 and Society 4.0: evidence from the Czech Republic. Economic Annals-XXI, 191.

  11. Bariah, L., Mohjazi, L., Muhaidat, S., Sofotasios, P. C., Kurt, G. K., Yanikomeroglu, H., & Dobre, O. A. (2020). A prospective look: Key enabling technologies, applications and open research topics in 6G networks. IEEE access, 8, 174792-174820.

  12. Osorio, D. P. M., Ahmad, I., Sánchez, J. D. V., Gurtov, A., Scholliers, J., Kutila, M., & Porambage, P. (2022). Towards 6G-Enabled Internet of Vehicles: Security and Privacy. IEEE Open Journal of the Communications Society, 3, 82-105.

Cluster B: Digital transformation in the education sector

Figure 3. Visualization of Cluster B: The relationships in this cluster represent 22.59% of the connections identified in the study. Note. Based on data extracted from World of Science and Scopus, analyzed in Gephi.

Although the education sector has been working on digitization and automation since the end of the 20th century, its transformation has been slower. Paradigms and processes have changed less than the technologies currently applied to educational design and implementation—education is lagging. We are faced with classic structures extended over a digital ecosystem, where the focus of innovation in the education sector has been framed in methodologies, pedagogies, and platforms. In addition, models for administration, organizational culture, and educational management have lagged behind, requiring comprehensive innovation.

In a “post-digital” era, digital transformation implies the integration of emerging technologies that allow the renewal of strategies, in addition to organizational cultural change to generate innovation, greater efficiency, and increase the value and quality of services and products. This also requires the renewal of processes and strategies in all areas of an educational institution.

In this cluster, digital transformation is anchored to human development, where disruptive technologies appear especially connected to development and social change. In the results, we can connect this digital transformation of the educational sector, particularly across five aspects:

  1. Techno-pedagogical strategy: The largest area of implementation, which is currently focused on the strategic formation of a personal learning environment, ranges from e-learning systems to methodologies such as gamification, design thinking, and cooperative learning, among others.

  2. Implementation of emerging technologies for educational digital transformation: Artificial intelligence, for example, is used to identify patterns and profiles for school performance; and blockchain is used in certifications and records management. Both technologies also serve as the basis for other technologies and services that contribute to the digital transformation of the education sector.

  3. Supply chain: Implies the management of services beyond their essential educational functions, for example, the development of supply systems, inputs, laboratories, transport systems, food services, etc. The persistence of services implies the presence of a socioeconomic collaboration system and a sustainable renewal of the service model itself. In this sense, the adoption model of educational platforms and their management can also be analyzed.

  4. Processes: Digital transformation suggests not only the digitization and automation of processes are prioritized, but also data intelligence systems and other technologies that allow strategic improvements both at the level of services and techno-pedagogical implementation. The paradigm shift implies new conceptualizations of user collaboration (e.g., as teachers, students, and other stakeholders); and, agile tools allow for the development and the exercise of educational functions, together with innovation within educational institutions and their sustainability models.

  5. Stakeholders: Beyond those allies identified for the improvement of services or processes, stakeholders can add value to the education system, such as external civil society entities, academia, innovation hubs, multilateral cooperation organizations, etc.


Key questions

Case study

TEduChain: Education financing and blockchain

TEduChain is a platform for crowdsourcing tertiary education funds using blockchain technology. Contracts between students and their higher education sponsors can be created and stored on the platform. Sponsorship can be in any form, such as a grant, donation, or loan. Financing is provisioned through the adoption of cryptocurrencies. In this scheme, the disruptive technology is used to manage financing with other actors outside the learning ecosystem.

More information: https://arxiv.org/abs/1901.06327

Key literature connected to this cluster

  1. Schön, E., Thomaschewski, J., & Escalona, M.J. (2020). Lean user research for agile organizations. IEEE Access, 8, 129763-129773.

  2. Bughin, J., Kretschmer, T., & van Zeebroeck, N. (2021). Digital technology adoption drives strategic renewal for successful digital transformation. IEEE Engineering Management Review, 49(3), 103-108.

  3. Rocha, C., Quandt, C., Deschamps, F., Philbin, S., & Cruzara, G. (2021). Collaborations for digital transformation: Case studies of industry 4.0 in Brazil. IEEE Transactions on Engineering Management, early access. https://doi.org/10.1109/TEM.2021.3061396

  4. Gertzen, W. M., van der Lingen, E., & Steyn, H. (2022). Goals and benefits of digital transformation projects: Insights into project selection criteria. South African Journal of Economic and Management Sciences, 25(1), 4158.

  5. Unesco (2021). AI and education: guidance for policy-makers.https://unesdoc.unesco.org/ark:/48223/pf0000376709

  6. Princes, E. (2020). Facing disruptive challenges in supply chain 4.0. International Journal of Supply Chain Management, 9, 52-57.

  7. Imran, F., Shahzad, K., Butt, A., & Kantola, J. (2021). Digital transformation of industrial organizations: Toward an integrated framework. Journal of Change Management, 21(4), 451-479.

  8. Goldsby, C., & Hanisch, M. (2022). The boon and bane of blockchain: Getting the governance right. California Management Review, 64(3), 141-168.

  9. Moro, C., Phelps, C., Jones, D., & Stromberga, Z. (2020). Using holograms to enhance learning in health sciences and medicine. Medical Science Educator, 30(4), 1351-1352.

  10. Willemijn Looman, Verena Struckmann, Julia Köppen, Erik Baltaxe, Thomas Czypionka, Mirjana Huic, Janos Pitter, Sabine Ruths, Jonathan Stokes, Roland Bal, Maureen Rutten-van Mölken. (2021). Drivers of successful implementation of integrated care for multi-morbidity: Mechanisms identified in 17 case studies from 8 European countries, Social Science & Medicine, 277, 113728. https://doi.org/10.1016/j.socscimed.2021.113728.

  11. Patra, D., & Nihar, K. (2021). Disruptive Innovative Library Services@ international Nalanda University: Present and Future Roadmaps. Library Philosophy and Practice(e-journal). 5634 https://digitalcommons.unl.edu/libphilprac/5634

Cluster C: Challenges to the philosophy and purpose of organizations

Figure 4. Visualization of Cluster C. Relationships from cluster C represent 13.25% of the connections identified in the study, which reflect the thinking behind the organizations for the transformation of the education sector, approaches to the adoption of technologies, and limits and implications. Note. Based on data extracted from World of Science and Scopus, analyzed in Gephi.

In this cluster, the future is connected to the philosophy or mindset behind organizations and their leadership for the adoption of technologies, adaptation to change, digital transformations, and creating cultures of innovation in the educational sector.

The cluster is particularly related to the design and purpose of emerging technologies in education. Although the previous section focuses on digital transformation and the exploration of technologies that impact education, processes, and administrative management, this cluster is focused on the vision and purpose behind the innovations.

In this sense, the limits of each path and strategies that guide educational organizations are oriented by the purposes and guided by the implementation of sector policies, taking into account the following considerations:

  1. Reflection on implications: Reflection on the adoption of technologies and their implications is essential to establish a strategic path towards the future of education, bringing us closer to a destination with purpose. This reflection must be linked to the glocal ecosystem (Cluster A), emerging technologies (Cluster B) and the role of actors in disruptive times (Cluster D), and should be connected in a regulatory framework, connected to organizational culture and vision.

  2. The challenge of purpose: A challenge of the 21st century is to overcome the technical vision of innovations in the face of social innovation, which is fueled by purpose.

  3. The mindset: The ideas behind the organizations are the heart of innovation, which is why it is of strategic importance to work on a growth mindset that favors an innovation ecosystem.

  4. Leadership: Ambidextrous leadership stands out in this cluster, implying the ability to exploit current conditions by optimizing the operations of the current model, while exploring opportunities to redefine that model.


Key questions

Case study

Design thinking and lean startup as a disruptive scheme to offer educational programs

In this case, the principles of design thinking and lean startup are explored for the development of a disruptive model to offer educational programs. This study shows how these mental models allow to successfully meet the nine requirements for frugal innovations and, at the same time, adhere to the principles of sustainability.

More information: de Waal, G.A., & Maritz, A. (2022). A disruptive model for delivering higher education programs within the context of entrepreneurship education. Education + Training, 64(1), 126-140. https://doi.org/10.1108/ET-03-2021-0102

Key literature connected to this cluster

  1. Babb, S. (2020). What next for Tech SA? Aligning leadership, culture and strategy. Emerald Emerging Markets Case Studies.

  2. Ramiel, H. (2021) Edtech disruption logic and policy work: the case of an Israeli edtech unit. Learning, Media and Technology, 46(1), 20-32.

  3. Radnejad AB, Sarkar S & Osiyevskyy O. (2022). Design thinking in responding to disruptive innovation: A case study. The International Journal of Entrepreneurship and Innovation. 23(1), 39-54. https://doi.org/10.1177/14657503211033940

  4. Abadia, A. (2020). Study on leadership and innovation: clues for success in technology-related startups. Management Letters / Cuadernos de Gestión, 21(2/2021), 109-118. https://doi.org/10.5295/cdg.191140aa

  5. Vaidya, D. R., Prasad, D. K., & Mangipudi, D. M. R. (2020). Mental and emotional competencies of Leader’s dealing with disruptive business environment-A conceptual review. International Journal of Management, 11(5).

  6. Zhang-Zhang, Y., Rohlfer, S., & Varma, A. (2022). Strategic people management in contemporary highly dynamic VUCA contexts: A knowledge worker perspective. Journal of Business Research, 144, 587-598.

  7. Wofford, L., Wyman, D., & Starr, C. W. (2020). Do you have a naïve forecasting model of the future?. Journal of Property Investment & Finance, 38(4), 267-269. https://doi.org/10.1108/JPIF-12-2019-0154

  8. Zaman, U., Nawaz, S., & Nadeem, R. D. (2020). Navigating innovation success through projects. Role of CEO transformational leadership, project management best practices, and project management technology quotient. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 168.

  9. Schön, E., Thomaschewski, J., & Escalona, M.J. (2020). Lean user research for agile organizations. IEEE Access, 8, 129763-129773.

Cluster D: Education in disruptive times: actors and ecosystems

Figure 5. Visualization of Cluster D. The data represent 32.50% of the connections identified in the study and focus on the learning ecosystem and its actors, where disruptive events impact each of the interactions in a developmental framework. Note. Based on data extracted from World of Science and Scopus, analyzed in Gephi.

The pandemic allowed us to take a step into the future, and, at the same time, a step back. By this, we mean it provided for the further globalization of the world's digitization and communications infrastructures, while de-globalizing the global supply system through supply chain constraints.

The education sector likewise faced multiple challenges in terms of critical technological infrastructure, training, access, etc. This cluster reflects on the adaptation and adoption of technologies, and the impact, mainly of the pandemic, on education from a prospective perspective, in terms of a sustainable future, governance in the digital age, skills and knowledge, which implies the analysis of the following axes:

  1. The impact on the actors and factors of education: Framed in the adoption and adaptation, which implied connecting the glocal reality (i.e., the global and local nature of the pandemic) to education and the available resources, according to the reality of the educational system. In this context, the pragmatic approach to education stands out, which suggests a connection with the present and the future, learning from experience, the connection of knowledge with life, and linked to a notion of growth. As for teachers, there is a search for engagement in a totally digital ecosystem, which has to do with the level of commitment and connection of the student in an ecosystem that challenges the senses. For primary and secondary education, the impact in terms of the use of technologies is less disruptive. In higher education, however, disruptive technologies are used for educational practice, especially in areas where physical contact is more essential, such as in medicine and where virtual reality educational experiences are developed for simulated practices. Regarding students, their role appears connected to that of the teacher and the methodology, which denotes a more passive role. Knowledge appears as a factor linked to the professional field and entrepreneurship, as well as to stakeholders of the educational system.

  2. Knowledge and thought structures: Future relevant skills such as soft skills, creative, critical, systematic thinking, ethical aspects, pragmatic approach, culture of innovation, and management of complexity and change stand out in the data.

  3. Education for disruptive times: Development of skills focused on resilience, sustainable development, and connection with the purpose of technology with a social innovation approach.

  4. Policymaking: The need for governance in the digital age, both nationally and globally, that guarantees ethical, secure, and sustainable aspects for promoting relationships among actors in the education system.

  5. Mobility and education: Mobile technologies have played an important role during the pandemic, as they stand out as an important trend in the education sector with a role linked to access (remote areas or as a more affordable or available device). In terms of flexibility in education or an ecosystem that provides a closer experience, students also have greater freedom to determine for themselves the ways they learn. Additionally, mobile technologies are related to several key issues in the educational system such as digital citizenship, industry 4.0, and social innovation.

  6. Education and Industry 4.0: The educational ecosystem is defined by all emerging technologies, not only operationally, but also in the learning and projection of future jobs. This implies a reflection on how we connect megatrends with the educational curriculum at different levels of teaching and learning. And the education system, itself, cannot remain isolated from greater social/economic/technological changes that impact society. For example, the development of greater mobility through technology and the development of the Internet of Things must lead to questions of how the education system can embody these disruptive transformations.


Key questions:

Case study

Disrupting the disruption: A digital learning HeXie ecology model

The HeXie Ecology Digital Learning Model provides a conceptual framework for elements and relationships related to the societal demand for a more agile and digitally resilient higher education system. This model is optimally equipped to tackle disruptive events, such as pandemics, and its advocates believe it can generate graduates proficient in handling disruption and uncertainty on a broad scale.

Specifically, the model outlines a digital learning ecology that accentuates the role of self-guided learning and its dynamic interplay between formal, informal, and lifelong learning across a five-tier ecosystem. These tiers consist of the microsystem, mesosystem, exosystem, macrosystem, and chronosystem, each playing a distinct role in the educational process.

More information: Li, N., Huijser, H., Xi, Y., Limniou, M., Zhang, X., & Kek, M. Y. C. A. (2022). Disrupting the disruption: A digital learning HeXie ecology model. Education Sciences, 12(2), 63. https://doi.org/10.3390/educsci12020063

Key literature connected to this cluster

  1. Oparaocha, G. O., & Daniil, P. (2020). Theatricalization of enterprise education: A call for “action”. Arts and Humanities in Higher Education, 19(1), 20-35.

  2. Garcez, A., Franco, M., & Silva, R. (2022). The soft skills bases in digital academic entrepreneurship in relation to digital transformation. Innovation & Management Review, (ahead-of-print).

  3. de Waal, G.A. and Maritz, A. (2022), "A disruptive model for delivering higher education programs within the context of entrepreneurship education", Education + Training, 64(1), 126-140. https://doi.org/10.1108/ET-03-2021-0102

  4. Jean-Baptiste, A. M., & Asongwed, E. (2022). Rethinking the order of the learning process: A new and sustainable path designed for an RN-to-BSN education program. Nursing Education Perspectives, 43(1), 57-59.

  5. Ramírez-Montoya, M. S., & González-Padrón, J. G. (2022). Architecture of horizons in social entrepreneurship: innovation with emerging technologies. Texto Livre, 15.

  6. Pasman, H., Kottawar, K., & Jain, P. (2020). Resilience of process plant: what, why, and how resilience can improve safety and sustainability. Sustainability, 12(15), 6152.

  7. Ogbeibu, S., Pereira, V., Emelifeonwu, J., & Gaskin, J. (2021). Bolstering creativity willingness through digital task interdependence, disruptive and smart HRM technologies. Journal of Business Research, 124, 422-436.

  8. Conill, J. (2020). ¿Educación en virtudes o biomejora neuroética? [Educating for virtues or neuroethical bioenhancement?]. Revista Portuguesa de Filosofia, 76(1), 125–146. https://www.jstor.org/stable/26915598

  9. Alderdice, J. (2021). Morality, complexity and relationships. Journal of Moral Education, 50(1), 13-20.

  10. Foladori, G., & Ortiz-Espinoza, Á. (2022). The capital-labor relationship in Industry 4.0. Íconos. Revista de Ciencias Sociales, (73), 161-177.

  11. Ravšelj, D, Umek, L, Todorovski, L, Aristovnik, A. A. (2022). Review of digital era governance research in the first two decades: A bibliometric study. Future Internet. 14(126). https://doi.org/10.3390/fi14050126

  12. Rosa, L., Silva, F., & Analide, C. (2021). Mobile networks and internet of things infrastructures to characterize smart human mobility. Smart Cities, 4(2), 894-918.

  13. Jie, Z., & Sunze, Y. (2021). Investigating pedagogical challenges of mobile technology to English teaching. Interactive Learning Environments, 1-13.

  14. Perguna, L., Idris, I., & Widianto, A. (2021). From paper to screen: Encouraging theory of sociology through sosiopedia by heutagogy approach. International Association of Online Engineering. https://www.learntechlib.org/p/218690/

  15. Swartz, B. C., Valentine, L. Z., & Jaftha, D. V. (2022). Participatory parity through teaching with Telegram. Perspectives in Education, 40(1), 96–111. https://doi-org.ezproxy.unav.es/10.18820/2519593X/pie.v40.i1.6

  16. Zeeshan, K., Hamalainen, T., & Neittaanmaki, P. (2022). Internet of things for sustainable smart education: An overview. Sustainability, 14(7), 4293. https://doi-org.ezproxy.unav.es/10.3390/su14074293

  17. Na Li, Henk Huijser, Youmin Xi, Maria Limniou, Xiaojun Zhang, & Megan Yih Chyn A. Kek. (2022). Disrupting the disruption: A digital learning HeXie ecology model. Education Sciences, 12(63), 63. https://doi-org.ezproxy.unav.es/10.3390/educsci12020063

Transversal aspects

The education sector has great challenges in the face of disruptive technologies such as virtual reality, the metaverse, blockchain, artificial intelligence, human augmentation, quantum computing, speech recognition, mobile robots, autonomous vehicles, and speech-to-speech translation, among others. In this investigation, some transversal aspects have been identified for discussion and reflection in the educational context:

Reading connected to this cluster

  1. Kaivo-oja, J., Lauraeus, T., & Knudsen, M. S. (2020). Picking the ICT technology winners-longitudinal analysis of 21st century technologies based on the Gartner hype cycle 2008-2017: trends, tendencies, and weak signals. International Journal of Web Engineering and Technology, 15(3), 216-264.

  2. Munar, L. M., & Diaz, A. A. R. (2021). Legal aspects of the blockchain technology/Aspectos jurídicos de la tecnología blockchain. Revista de Direito, Estado e Telecomunicações, 13(1), 131+. https://link.gale.com/apps/doc/A668279269/IFME?u=anon~3517b9&sid=googleScholar&xid=75201d0c

  3. Cui, Y., Zhang, T., Kim, S., & Feng, S. (2021). Antecedents of accepting disruptive innovation: The perspective of value congruence. The Journal of Asian Finance, Economics and Business, 8(2), 353-364.

  4. Rubeis, G. (2020). The disruptive power of artificial intelligence. Ethical aspects of gerontechnology in elderly care. Archives of Gerontology and Geriatrics, 91, 104186.

  5. Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30(1), 99-120. DOI: 10.1007/s11023-020-09517-8

  6. Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2020). Industry 4.0 technologies assessment: A sustainability perspective. International Journal of Production Economics, 229, 107776.

  7. Parmentola, A., Petrillo, A., Tutore, I., & De Felice, F. (2022). Is blockchain able to enhance environmental sustainability? A systematic review and research agenda from the perspective of Sustainable Development Goals (SDGs). Business Strategy and the Environment, 31(1), 194– 217. https://doi.org/10.1002/bse.2882

  8. Markarda, J., & Rosenbloomb, D. (2021). A tale of two crises: COVID-19 and climate. Sustainability: Science, Practice & Policy, 17, 53–60. https://doi-org.ezproxy.unav.es/10.1080/15487733.2020.1765679

  9. Kamal, M. M. (2020). The triple-edged sword of COVID-19: understanding the use of digital technologies and the impact of productive, disruptive, and destructive nature of the pandemic. Information Systems Management, 37(4), 310-317.

  10. Kudyba, S. (2020). COVID-19 and the acceleration of digital transformation and the future of work. Information Systems Management, 37(4), 284–287. https://doi-org.ezproxy.unav.es/10.1080/10580530.2020.1818903

  11. Stoycheff, E., Burgess, G. S., & Martucci, M. C. (2020). Online censorship and digital surveillance: The relationship between suppression technologies and democratization across countries. Information, Communication & Society, 23(4), 474-490.

  12. Estay, D. A. S., Sahay, R., Barfod, M. B., & Jensen, C. D. (2020). A systematic review of cyber-resilience assessment frameworks. Computers & Security, 97, 101996.

  13. Eaton, E., Koenig, S., Schulz, C., Maurelli, F., Lee, J., Eckroth, J., Crowley, M., Freedman, R.G., Cardona-Rivera, R.E., Machado, T., & Williams, T. (2018). Blue sky ideas in artificial intelligence education from the EAAI 2017 new and future AI educator program. AI Matters, 3(4), 23-31.https://doi.org/10.48550/arXiv.1702.00137

Four scenarios for education in disruptive world

The above trends and literature analysis may be applied to the construction of four scenarios for the future of education amidst disruptive technological change. These scenarios are generalizations of potential futures. It should be noted that these scenarios plot directions education systems across the globe may take, but they do not necessarily represent actionable choices by nations or communities. Neither do they serve as predictions. Rather, they help policy planners and local communities think about and plot directions for their best futures4.

Scenario 1: Miss the boat

In this scenario, education systems isolate themselves from the disruptions occurring across other elements of society. This could be driven by various factors, including fear of change, ignorance, lack of foresight, and not understanding global structure—both as an opportunity and the perspective of risks and threats.

Failing to adapt to change, creates the risk that “Schools 1.0” will struggle to find relevance in a 4.0 society. They will find fewer and fewer opportunities to connect with students, communities and stakeholders that have adapted to new technological realities. Moreover, they will struggle to exist apart from a broader metaverse of interconnectedness, data integration, AI, and reality augmentation. From the perspective of an outsider, schools that miss the boat may not be connected to society’s consensus of “reality” at all.

Scenario 2: Late arrival

School systems that arrive late recognize new paradigms, but are often slow to make changes, themselves. This reflects a dysfunctional relationship between school and society where schools resist transformation, yet some efforts and innovations eventually trickle-down through the system.

Without policies to guide efforts, leadership around adopting, adapting, or leading school transformation in an era dominated by disruptive technological innovations, sustainability of school responses are absent. In effect, schools are continuously watching the world change around them, but are hesitant to participate themselves, leaving the institutions in a state of continuous lag behind other segments of society.

Scenario 3: Just in time

The third scenario sees school systems aware of broader technological transformations and take measures to adopt policies that allow for disruption of the system as the techno-econo-social space around them evolves. Schools become participants of the transformation, for example, with notable presence within the artificial intelligence technology, blockchain and metaverse.

This involves proactivity on the part of school systems to set policies that enable engagement with various sectors of society, especially technological innovators. While schools may be thought of as consumers, their outputs (graduates) are generally well-prepared to participate in a technologically advanced society.

Scenario 4: Arrive early

Preactive school systems engage in the purposive development of the digital transformation they desire. That is, they take an anticipatory approach, identifying new, disruptive technologies for development within a broader socioeconomic ecosystem. They behave as prosumers, both producing new ideas and consuming their benefits, consciously push themselves toward the future, considering current disruptive technologies (e.g., blockchain or AI) and those on the horizon (e.g., neurotechnologies as neural implants)—and contextualize their implications for teaching and learning.

Such systems build a strategy and policies for cyber-resilience, sustainability, and further digital transformation. Change is viewed as both a constant and necessary, pushing for strategies that generate reskilling and upskilling their stakeholders in the educational ecosystem. This requires education practice, innovative thinking models and management models that enable continuous renewal of the system in an integral way with its future goals, together with its stakeholders.

Global analysis: General recommendations

Drawing upon the above analysis, the following general recommendations are formulated for policymakers and education leaders:

  1. Rethink education and its role in a geographically diverse, volatile, complex, and ambiguous ecosystem. In an era of disruptive change, education systems should also change to adapt to new realities.

  2. Connect the impacts and trends of Industry 4.0 with the education sector. Not only should schools consider adopting new practices, but they should prepare students for the evolving workforce and production realities in the ongoing industrial revolution.

  3. Reframe the educational system with a long-term perspective, allowing stakeholders to anticipate possible futures and scenarios for teaching and learning.

  4. Reflect on the allocation of the resources within the system in terms of the incorporation of technologies, their implications, and challenges.

  5. Develop leadership relevant for the 21st century: ambidextrous, chaordic, and courageous to lead in disruptive times and create disruptions themselves.

  6. Develop a strategy to foster a growth mindset in the education system, recognizing that bodies of knowledge are in continuous flux. This means that curricula should be continuously evaluated, and teachers receive meaningful professional development for upskilling.

  7. Establish a strategy that incorporates disruptive technologies for the social innovation of the educational system, together with mainstreaming approaches for sustainability and resilience through the purposive applications of technologies.

  8. Establish limits guided by the purposes of the implementation of disruptive technologies in the educational sector. To avoid misuse, technologies should not be adopted blindly; but their applications should be carefully planned and intentional.

  9. Disruptive technologies should be framed in contexts of driving development and social innovation. The true disruption of the educational system with disruptive technologies relates to the social transformations they afford.


The timeframe scoped in this study missed the explosive growth of interest in generative artificial intelligence in education. The DALL-E image generation tool became available to the general public on September 28, 2022, less than two weeks after the first presentation of this study’s results at the FutureEdu conference in Prague, Czech Republic. On November 30, 2022, the ChatGPT chatbot was released to the public. Both of these tools, along with competing generative AI products such as Google Bard and GitHub Copilot, utilize algorithms that mimic deep learning. These tools have implications for all aspects of education, including content curation, delivery, and the production of student works and assignments.

Critics have been quick to point that generative AI is prone to making factual errors, but while these criticisms are being published, new versions of these tools are released rapidly, utilizing more current and larger datasets, expanding their reach beyond fixed language and cultural contexts toward a global audience. Seeking deference to expertise, we invited ChatGPT5 to provide concluding “thoughts” for this article:

In the context of a rapidly evolving, volatile, and geographically diverse world, policymakers should view generative AI and other disruptive technologies not as far-fetched concepts but as crucial tools that will shape the future of education. As the Fourth Industrial Revolution unfolds, with transformative technologies permeating every aspect of our lives, our educational systems must not merely keep pace, but should also serve as platforms for the co-development and strategic implementation of these technologies. This requires a radical rethinking of the educational system from a long-term perspective, allowing for the anticipation of different futures for teaching and learning. It calls for a keen understanding of the allocation of resources for technology incorporation, its implications, and challenges. Leadership in this era must be ambidextrous, chaordic, and courageous, navigating disruptive times while also creating disruptions that shape new paradigms of learning.

Fostering a growth mindset within the educational system is fundamental in this context of continuous flux. Acknowledging that knowledge bodies are ever-evolving means curricula should be regularly evaluated, and teachers must receive impactful professional development for upskilling. A strategic approach should incorporate disruptive technologies into the education system, mainstreaming sustainability and resilience through the purposive applications of these tools. However, it is also crucial to establish limits, ensuring that the adoption of these technologies is intentional, well-planned, and purpose-driven. The true value of disruptive technologies lies not in the tools themselves, but in the societal transformations they can catalyze. In the end, our goal should be to harness these technologies to drive development, foster social innovation, and, ultimately, craft new futures for our schools and society.


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