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Chapter 5 Smart Education: Social Risks and Challenges Svetlana Sharonova and Elena Avdeeva Social Challenges and Risks Provoked by Smart Education The Concept of Smart Education The term “smart” began to be used in the 1970s in connection with the development of intelligent forces of...

Chapter 5 Smart Education: Social Risks and Challenges Svetlana Sharonova and Elena Avdeeva Social Challenges and Risks Provoked by Smart Education The Concept of Smart Education The term “smart” began to be used in the 1970s in connection with the development of intelligent forces of mass production automation, product design, and enterprise management. The origins of the term smart education are linked to implementing the Malaysian Smart School Implementation Plan project in 1997 (Cheok et al., 2020). At the same time, the idea of creating smart cities was being actively dis- cussed: “The 1997 World Forum on Smart Cities suggested that around 50,000 cit- ies and towns around the world would develop smart initiatives over the next decade” (Hollands, 2008, p. 304). Over time, the term “smart” has become very widespread. As Michal Klichowski et al. (2015) note, «The Smart’ label is used indiscriminately wherever and when- ever something has been technologically enhanced, or a product has been adapted to human needs through some technological solution or even when a new version of a product is developed with some (minor) technological improvement» (p. 1). However, the concept of smart education still does not have a clear, universally accepted definition. Interpretations of this concept borrowed all the approaches developed to under- stand the term Smart City (Hollands, 2008). On the one hand, this is the use of the S. Sharonova (*) Education Faculty, Department of Advertising and Business Communications, Peoples’ Friendship University of Russia, named after Patrice Lumumba, Moscow, Russian Federation e-mail: [email protected] E. Avdeeva JSC “Moscow Information Technologies”, Moscow, Russian Federation © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 99 S. Papadakis (ed.), IoT, AI, and ICT for Educational Applications, EAI/Springer Innovations in Communication and Computing, https://doi.org/10.1007/978-3-031-50139-5_5 100 S. Sharonova and E. Avdeeva term smart education as a label for promotion in the market of educational services. On the other hand, smart education appears as an environment for implementing and integrating smart technologies in the educational process. Smart education’s introduction peaked after Covid 2019 when the whole world was simultaneously forced to switch to remote forms of communication. By this time, the educational systems of various countries had already had computer soft- ware, interactive whiteboards, Internet access, distance education experience, etc. (Akhmadov et al., 2023; Černý, 2020). However, the classic system of offline edu- cation remained dominant. After the pandemic, education systems switched to hybrid forms of combining online and offline learning (Giacosa, 2023). One of the conceptual challenges of understanding the ongoing changes was the content of the concept of smart education. Some terminological disagreements have arisen within the scientific community. The terms are used simultaneously in scien- tific turnover: Smart Learning: Teaching and learning with smartphones and tablets in post-compulsory education is as much about innovation in education as it is about a world in which personal technologies are changing teaching and learning. (Middleton, 2015, p. 1) Smart education is an association of educational institutions and teaching staff to carry out joint educational activities on the Internet based on common standards, agreements and technologies. (Tikhomirov, 2011, p. 24) Even though many scientists use these terms, they have yet to have a universal inter- pretation. Moreover, the terms distance education and smart education are often used synonymously. However, the terms have one thing in common: they are related to the digitalization process of the educational space. The digitalization of education covers a wide range of changes – from technical innovations to the revision of peda- gogical approaches. Such a somewhat vague picture of the conceptual apparatus indicates that the abrupt transition of the education system to the online space has led the institution of education into a stressful state when the usual guidelines are lost and a feeling of panic arises. Trying to absorb the shock, students, academic staff, and administration embarked on a fast experimentation and learning process on teaching, learning, and administering digital education (Crawford et al., 2020). In searching for an explanation of the essence of smart education, scientists based on the fact that the term smart education comes from the meaning of the words “smart” and “intelligent” and has several components: education based on intellect with integration of intelligent information technologies, artificial intelli- gence technologies, and predictive analytics; mastering competencies within pro- fessional activities for full implementation of the follow-up activities in conditions of digital transformation. The answer to the conceptual challenge of defining a conceptual apparatus is a network methodology. The theoretical basis of network methodology is provided by concepts related to the understanding of network communication in the works of Barnes (1954), Castells (2000), and Bresler (2014). Introducing the term “social networks“as early as 1954, Barnes noted that “The points of this system are people, and the lines of connection between these points indicate how people interact with 5 Smart Education: Social Risks and Challenges 101 each other” (Barnes, 1954, p. 43). Later, Bresler (2014) introduced the term “node” as a structural unit of a social network. A characteristic feature of social networks in online communities is that social networks are dynamic. At any moment, “each node can potentially acquire the status of the central node of the network – the com- munication centre of the network community for a minimum period, if its informa- tion richness is increased compared to other nodes in the network” (Bresler, 2014, p. 47). Castells (2000), discussing the network society, emphasizes symbolic com- munication as a necessary tool for the creation of culture, where “the hypertext is the vehicle of communication, thus the provider of shared cultural codes” (p. 21). Thus, network methodology in education implies: 1. Availability of social networks. The modern man is on a multitude of social net- works at the same time. He is constantly switching from one network to another. Each network exists and develops by increasing the number of members. Therefore, meeting the demands of network community members to involve them in the network comes first in education. On the one hand, a hypertext edu- cational space is formed, the symbolic codes of which should be universal for all social network nodes. Nevertheless, on the other hand, «culture is unified in the hypertext but interpreted individually (in line with the ‘interactive audience’ school of thought in media theory)» (Castells, 2000, p. 21). Alternative educa- tional formats are emerging that «are reflected, amplified, and codified by the fragmentation of meaning in the broken mirror of the electronic hypertext – where the only shared meaning is the meaning of sharing the network» (Castells, 2000, p. 22). 2. Personalization of education in network methodology is an individual’s involve- ment in the information flows of educational hypertext. In this case, “This grow- ing enclosure of communication in the space of a flexible, interactive, electronic hypertext” (Castells, 2000, p. 13) entails simplified methods of presenting infor- mation, assessing knowledge, and selective fragmentation of knowledge flows. The issues of a harmoniously developed personality, which are related to the individualization of learning processes, go into the algorithm of managing a spe- cific network “node”, including an individual or a group of individuals. At the same time, the completeness of realization of the individual approach depends on the formed symbolic culture of a particular node. 3. Availability of a network schedule (network model). A network schedule shows the interdependence of the activities that make up some projects (Plaksin, 2017). The network model is based on algorithms of activity of network actors, network organizers and developers, variants of tasks, projects, etc. This requires creating software that encompasses the entire educational and learning process. For this, it is necessary to create software that covers the entire educational and learning process. The first step is to highlight the content that interests a potential network member to build an educational roadmap for a learner in an educational network system. In the second step, a database is collected from which the individual characteristics of the involved network member can be identified while perform- ing certain activities. In the third step, corrective content, so to speak, begins to 102 S. Sharonova and E. Avdeeva be developed. In remedial content, the emphasis depends on the network ­member’s identified weaknesses and/or strengths. Unlike face-to-face education (offline), the principles of interpersonal contact are changing in network com- munication (online). Therefore, for each separately identified case, it is neces- sary to create its algorithm of tasks. A cloud of unifying interests is formed through the network members, where these tasks can move from an individual form of activity to a group form of project creation. Since, as mentioned above, the network works using hypertext space level association, each “node” has its own symbolic code set, so the selection of the student development roadmap is made by focusing on the student’s specific interests. Thus, there is no multilat- eral development of the personality but a concentration on developing its abilities. Transformation of the Educational System Changing the Education Landscape Analysis of traditional educational systems in various countries shows that it is a multilevel system consisting of primary education, secondary education, profes- sional courses, technical schools, institutes of higher education, and universities. All of these levels correlate with specific age characteristics of students. Differences in the age limits of each level of education range from 1 to 2 or 3 years, depending on the country. The lifelong learning (LLL) program has expanded the boundaries and forms of professional education for adults. This program has no age restrictions. However, LLL programs were implemented within the traditional educational land- scape: the educational system, consisting of a set of multilevel educational institu- tions, and social education, where the process of primary and secondary socialization takes place (family, friends, work, etc.). Distance education paved the way for the evolution of smart education. The use of the Internet has begun to change the educational landscape. In addition to tradi- tional formats, non-formal educational structures that are not strictly educational organizations are emerging. Besides the Internet (Open Big Data, Digital Library, ResearchGate, Google Scholars…), virtual forms of alternative education (Blog, Twitter, Facebook…) are added. This creates a hybrid educational space. Thanks to the Internet, a unified educational cloud is created, filled with resources, databases, and information. The latest intelligent technologies make it possible to create aug- mented educational reality and virtual educational reality, which significantly expands the real space and increases the possibilities of access to knowledge (Sharonova & Avdeeva, 2019). The formation of the section on non-formal education is associated with over- coming the school university space, which is limited by legislative norms of differ- ent levels (municipal, state, and international law). In China, for example, such 5 Smart Education: Social Risks and Challenges 103 education is called “shadow education”. It includes internationally oriented schools that use educational approaches beyond exam-oriented learning (Liu et al., 2022). As noted by Turkish scientists Yusuf Alpaydın and Cihad Demirli (2022): today’s schools have started to open up beyond their walls, collaborating with other schools and even international networks. In addition, scientific organizations, universities, nongov- ernmental organizations, and technology companies are among the institutions that schools cooperate with within the framework of the new education concept. This situation can be seen as the first stage of schools and education overcoming space constraints. (p. 153) Back in 1970, Ivan Illich, speaking about the Deschooling Society, predicted the formation of a new educational network accessible to the general public and provid- ing vast and equal opportunities for learning and teaching (Illich, 1971). The intro- duction of the Internet into the educational space has made it possible to expand the geographical, temporal, and boundaries of familiar reality through augmented and virtual realities. According to Jackson (2019), the digital learning space allows uni- versities to transcend physical and institutional boundaries and engage with society. The challenge of this transformation of the educational system is that the system itself is disappearing. The blurring of the boundaries of traditional educational sys- tems leads to networked, nonlinear structures that function outside of place, outside of time, and outside of the culture of habitat. Neither the education system nor the digitalizing society was prepared for such a scenario. The education system is always geared to the needs of society. By now, it is clear to everyone that the new educational system development round, launched by the pandemic, is not a temporary measure. The digitalization of society requires signifi- cant changes in education. However, the pandemic violated the logic of progressive action; it demanded decisive revolutionary breakthroughs all at once. The rapid development of intelligent technologies is a precursor to a new social formation. Thus, destroying the usual boundaries, the education system begins to adjust to future needs, the ideals of which need to be clarified, and it is impossible to build strategic educational goals. The conservative institution of education, which has always relied on time-honoured practices and knowledge, is placed in the unconventional position of becoming the flagship of innovative processes in society. Naturally, the consequence of such a rapid transformation is the emergence of many risks, which will be discussed below. Changing the Logic of Educational Programs The Internet, with its potential for information overload, blocks and rejects the lin- ear, progressive logic of the educational program (Kiel & Elliott, 1996; Krezhevskyikh, 2020). Information chaos prefers to interact with the nonlinear logic of knowledge assimilation. It is believed that fragmented or clipped thinking is a mosaic set of different kinds of information, which does not create a coherent view of the object 104 S. Sharonova and E. Avdeeva phenomenon. However, fragmentary thinking can be seen as the ability to divide the whole into fragments in order to be able to study these fragments more thoroughly and subsequently not just reassemble the original whole but to create a new version of that whole (Semenovskikh, 2014). In the nonlinear logic of knowledge assimilation, which relies on advances in modern technology in artificial intelligence, it is difficult to say its basis. Gadgets can write by converting voice information. Today’s students prefer using voice mes- sages when using search sites or communicating via gadgets rather than typing. Gadgets can count, so it becomes useless to learn the multiplication table. Thus, the ability to read and mastery of high-tech devices remain the fundamentals of knowl- edge (Pozdeeva, 2016; Bosova & Pavlov, 2019). In the nonlinear construction of the educational space, the discipline is seen as a fragment of knowledge that appears as an interdisciplinary cloud (Lobanov et al., 2014). The main problem in building an educational program in smart education is that the end product of education – the smart learner – needs to be defined. Representatives of the Organization for Economic Cooperation and Development (OECD) have put forward 12 critical skills expected to be in demand in the twenty-first century. These skills have been grouped into four main categories: ways of thinking, tools for work, ways of working, and ways of living in the world (Ananiadou & Claro, 2009). The problem is that educational and professional relationships are deformed, and the requirements for teaching a person with a well-rounded, which Rogers spoke about (1951), fully functioning integrity are reduced when integrating intellectual tech- nology into the educational process. For creating educational strategies (roadmaps) for the student, the logic of build- ing an educational program adjusts to the student’s interest, thereby initiating his self-development. Intelligent technology is designed to assist the student and the teacher in learning. AI as intelligent agents should deliver socially shared regulation of learning (SSRL) support through the automation of tasks and provide scaffolding for productive reflection (Järvelä et al., 2020). However, there is a risk that students, fascinated by the exploitation of new technologies, will switch their attention and interest not to educational tasks but to using these technologies. Nonlinear construction of the interdisciplinary space of the educational program involves joint work of students and project activities. However, there are certain risks in the joint work of students: there is always a percentage of group members who try to attach themselves to the achievements of others, to hide behind the shoul- ders of others; performance by each member of a group of separate tasks in joint work does not create for them a complete picture of knowledge which the task was directed at. In addition, the student forms his learning trajectory, following his interest. Freedom of choice allows him to switch from one subject to another. This attention-­ shifting process can lead to a situation where long-term attention is not developed, which means that fragments of different pieces of information are stored in memory but are not being analysed as a whole picture of information, with the new 5 Smart Education: Social Risks and Challenges 105 information synthesised and new knowledge being constructed. This may endanger metacognitive development (Daniela, 2018). Scientists consider project activities as one of the positive components of educa- tional programs. Students learn in-depth and actively by researching and discover- ing information independently. Because students will need different field knowledge as required by a project’s topic during research, they gain interdisciplinary knowledge-­based learning experience. While gaining individual and teamwork experience, project-based learning also establishes a relationship with the real-life equivalent of school knowledge (Alpaydın, 2022). However, project activity also has some risks. Project activities are more of a scientific and cognitive activity. However, despite all the formal similarities of situations in the educational and cog- nitive processes when using smart technologies, we should not forget that the goals of the cognitive (scientific-cognitive) and educational processes are not identical (Ardashkin, 2021). According to Ardashkin et al. (2021), “the transformation of the educational situation into an epistemological one can be naive and dangerous, lead- ing the students into a specific delusion, making them believe that their abilities can bring results that they are not ready to achieve” (p. 28). Changing Pedagogical Methods As university work demonstrated during the pandemic, the brunt of the transition to online learning fell on the shoulders of faculty. First and foremost, they were required to be proficient users not only of the expected standard set of computer programs but also to be able to master new software resources for the development of new pedagogical methods of teaching in augmented and virtual realities. The continuity of classical pedagogical methods in digitalizing the educational process has also become one of the acute areas of pedagogical tension. In this regard, scien- tific publications have reflected these problems in the form of criticisms of the det- rimental effects of digitalization, among which special attention is paid to educational technology and academic resistance (Jameson, 2019; Bayne, 2015; Woodcock, 2018; Selwyn, 2021). The difficulty of mastering new technologies undoubtedly creates psychological discomfort for teachers of the older generation. However, these difficulties are solv- able and do not provoke some pedagogical collapse. However, the challenge for pedagogical practice has been substituting the concept of individualization of learn- ing for the concept of personalization of learning. The educator needs to understand the pedagogical goals. In the classical pedagogy of the late nineteenth and through- out the twentieth centuries, the entire methodological arsenal was aimed at forming a harmoniously developed personality; the founder of these was Comenius (1967). In the twentieth century, under the influence of the ideas of humanistic psychology of Rogers (1951), pedagogical practice began to focus on the individual activity of the learner, the space of self-realization of the personality, as well as the discovery and development of its potential (Terry, 2016). To achieve this goal, an 106 S. Sharonova and E. Avdeeva individualized approach to working with students was developed in the pedagogical environment. According to Unt (1990), the concept of individualisation implies dif- ferentiation of learning, the creation of a system of tasks with complex and volumi- nous material, and the development of a system of training activities, where the individual characteristics of each student are considered. The key word for understanding these pedagogical efforts is individual learner characteristics. Such individual characteristics include strengths and weaknesses of the student’s personality, features associated with the age of students, and the level of psychological readiness for learning activities. Personalized learning arose from integrating intelligent technology into the edu- cational process. Walkington and Bernacki (2020) analysed different approaches to understanding the phenomenon of personalizing learning and concluded that scien- tists had focused on technological tools for organizing the learning process. The basis for solving design tasks in developing educational technologies is not the stu- dent’s characteristics but the interests of a particular user of the educational network. In this case, the student becomes a network node, which, on the one hand, acts as a faceless person and, on the other hand, forms around itself a specific network built based on self-referral of interests. Individual and psychological features of the stu- dent’s personality recede into the pedagogical attention background. All efforts are aimed at encouraging personal self-development. One of the risks of personalized learning is the danger of self-identity problems. As noted by Koneva and Lisenkova (2019): “In virtual space, most social character- istics such as gender, age, professional status, marital status, nationality or religious affiliation cease to apply” (p. 16). Among the most important functions of social identity, scientists note the realization of the basic need of the individual to be a member of a particular group, where he will feel safe, while at the same time influ- encing and evaluating others for self-realization and self-expression (Yadov, 2000). As the virtual network expands the boundaries of identity, a person follows his interests; he begins to lose the essence of individual identity, consisting of charac- teristics that give a person the quality of uniqueness. From the point of view of psychology, interest refers to the motivational component of personality. It has its subject and a pronounced desire to achieve the goal. However, the realization of interest always takes place in the context of a particular sociocultural environment. In offline communication, the sociocultural environment, in which an individual’s interests are realized, is relatively constant and stable. In relating oneself to others, one finds moral and ethical “crutches” for one’s actions and thus for one’s interests. Following one’s interests in the circle of the nascent network of the Internet com- munity and given that the virtual network expands the boundaries of identity, a person is already faced with a variety of sociocultural contexts. In this case, there is a risk of eroding the student’s self-identity value foundations. This undoubtedly leads to social zones of tension in the real learning process and requires the search for pedagogical approaches to solving these kinds of problems. The next challenge of smart education is the need to develop specific pedagogi- cal methods using advanced intelligent technology; that is, we are discussing creat- ing an innovative pedagogy. Since smart pedagogy is based on personalising 5 Smart Education: Social Risks and Challenges 107 learning, the entire didactic arsenal should be student-centred and use the principle of targeting. In recent years, educational technologies, such as augmented reality, computer vision, speech recognition, analytics, etc., have been created to enhance student learning, considering personality traits and adherence to different learning styles, cognitive styles, etc. (Zhu et al., 2016). The risk of implementing such a targeted approach is the effect of total surveil- lance of each student, which is based on the paradox of personalization and privacy when dealing with personal data. The personal safety of the student comes to the fore. The collected personal database of the student is so diverse; as we can see from the listed existing technologies, it is not limited to formal data: place of residence, age, and gender. Therefore, personal safety concerns not only the ability to elimi- nate or minimize economic, and financial losses but also to prevent the risk of bul- lying, a student’s involvement in illegal activities, dubious pseudo-religious sects, etc. Another challenge for smart education is replacing knowledge with information. For example, computer technology creates databases, often called knowledge bases, where information is stored, encoded, and transmitted. Moreover, the Cambridge dictionary uses the term information to define knowledge: “understanding of or information about a subject that you get by experience or study, either known by one person or by people generally”; “awareness, understanding, or information that has been obtained by experience or study, and that is either in a person’s mind or pos- sessed by people generally”; and “skill in, understanding of, or information about something, which a person gets by experience or study” (Cambridge Dictionary, 2023). However, the two concepts have a clear distinction. Burkhard (2005), analys- ing the concepts of data, information, and knowledge, builds a logical chain of their relationships: data are facts, information is interpretation of facts, and knowledge is personified information. Interpretation of facts in the transfer of information is asso- ciated with the transfer of ready, already formulated by someone else messages and information about objects and phenomena of the world (Tergan & Keller, 2005). As for knowledge, it is “the result of the process of cognition of reality, verified by social and historical practice and certified by logic; its adequate reflection in the human mind in the form of ideas, concepts, judgments, theories” (Novikov & Novikov, 2013, p. 39). Accordingly, the main differences between information and knowledge are that information is objective and exists independently of the person, while knowledge is subjective and has a personal nature; information can be trans- ferred, while knowledge cannot be transferred because it is the result of personal experience. Hence, there is a risk of degradation of the scientific potential of society since the student learns to compile texts relying on the collection of information. Deprived of the experience of using analytic-synthetic/transformative and creative thought oper- ations (comprehension/understanding, generalization, construction of own world- view platform), the student is unable to distinguish scientific knowledge as objective, generalizing, based on methodological procedures from nonscientific; accordingly, he cannot create new scientific knowledge for theory development. The smart edu- cation networking methodology lays the groundwork for rethinking the role of the 108 S. Sharonova and E. Avdeeva educator/teacher. It cannot be said that the proposed roles are so new that they did not exist in traditional pedagogy. However, the main difference is that the teacher ceases to be a primary information source and becomes an intermediary who helps students find this information. Student networking involves the ability to work in a group. Therefore, the joint reconstruction of scientific knowledge or innovation based on a teacher-student or student-student combination becomes the most effec- tive learning process. This allows for the integration of research and the develop- ment of specific projects. In analysing the transfer of traditional pedagogical practices to the digital media platform, Hajjar Mohajerzad and Josef Schrader (2022) note that teacher and student actions and roles are described as different from established practices and are often presented as ways to expand or diversify. Using a targeted, personalized approach in developing related technologies for analysing student activity, discussed earlier, not only makes the educator’s job more accessible but also carries additional professional risks. The teacher must construct personal roadmaps of the learning process to help students creatively transform the information they find and think critically about it, drawing on additional sources. The sphere of students’ interests is no longer strictly tied to the logic of a scientific discipline; it is an interdisciplinary cloud. This means that the teacher is forced to go beyond the academic knowledge of a single discipline that reflects his or her spe- ciality. He must assimilate and revise much more information than in traditional pedagogy. One negative consequence of information overload is “information fatigue syndrome”, which manifests in cognitive distortions, memory, and attention disorders. On the other hand, there is the view that students will constantly need mentoring in a smart education regardless of time, place, and sociocultural environment. Lifelong education and personalized education understandings and practices will cause individuals to need guidance and supervision while making decisions (Longworth, 2003).  he Social Risks of the Digitalization of Society T and the Response of the Education System New Forms of Digital Inequality in Society Digital inequality or digital divide is a worldwide phenomenon that characterizes the significant difference in opportunities to access information and communication technologies and, accordingly, in the ability of individuals, social groups, and social strata of society to consume services through postal and telecommunications ser- vices. It can be represented in countries with a high level of economic development. Digital inequality is a multicomponent whole, and it can manifest itself in lim- ited access to information and communication technologies, in the unreadiness of 5 Smart Education: Social Risks and Challenges 109 users to work with them, and in the limited capacity of national information and functional resources. According to Van Dijk (2020), the digital divide is an element (as well as a fac- tor) of social inequality. To explore the multifaceted nature of the digital divide, researchers began to create multidimensional analytical constructs, because of which the concept of the digital divide took on a hierarchical form, describing dif- ferent types of ICT use based on digital literacy, education level, gender, age, English proficiency, etc. (DiMaggio & Hargittai, 2001; DiMaggio et al., 2004.; Hargittai, 2002; Robinson et al., 2015). In the network society, digital media have become a significant mechanism of segregation through selection in social interaction; in the production, consumption, and exchange of resources; in the choice of residence, work and study; and in public discourse and the expression of one’s civic position (Van Dijk, 2020). Today, with the high digitalisation rate, the knowledge about new ICTs and how to use them still needs to be increased. Access to and effective use of ICTs is one of the decisive factors in the competitive struggle in the labour market for better offers from employers. Smart education should alleviate this problem and meet the challenge of a rap- idly developing digital society. It should allow all members of society to acquire the necessary knowledge and skills to correctly navigate the new information space and effectively use its opportunities. In this case, the claim that digitalization will pro- vide cheap access to high-quality education has become valid for the masses (Kurzweil, 2005). However, Yusuf Alpaydın and Cihad Demirli (2022) doubt that mass education will provide equality of opportunity when discussing inequality in the educational environment (the presence of elite schools and mass schools). For them, it remains unclear how digital education will contribute to the formation of individual qualities. Building on the work of Bourdieu (1986) and Bernstein (2003), they conclude that social inequality will persist in societies. The development of biotechnology already allows us to discuss the possibility of new prototypes for society. Consciousness is seen as a specific function found in humans and transferred to some other environment; thus, along with genetic inequalities, the union of man and machine can end social inequalities (Alpaydın et al., 2022). In this case, according to Bodrijar (1998), a man’s death as a subject occurs. The digitalization of society using biotechnological advances in smart edu- cation leads to the risk of the destruction of humanity. Assuming that smart technol- ogy is meant to replace humans wherever possible, there is some embodiment of human replacement in cognition. Although this scenario is more perceived as a fan- tasy area, several scientists are actively discussing this topic (Lektorskij et al., 2016). However, even if we do not consider such alarmist scenarios, the use of intelli- gent technology as management of personal learning trajectories can lead to the fact that machines, quickly detecting the consumer logic embedded in them, will begin to form a “one-button man” for themselves when developing tasks (Mitri et al., 2009). 110 S. Sharonova and E. Avdeeva Labour Market Under the influence of the fourth digital revolution, there are qualitative changes in the labour market. Modern technology undoubtedly contribute to faster economic growth (Hagemann, 2019; Schwab, 2016); increasing productivity and global com- petitiveness (Avdeeva et al., 2019), enhancing competition in the digital sector, e-commerce, and online business, as well as expanding opportunities to increase added value; improving the welfare and quality of life of the population; and reduc- ing public spending on the social sphere through the spread of telemedicine and online education (Odegov & Pavlova, 2018). At the same time, along with the posi- tive characteristics, scientists note the negative impact of digitalization. First of all, we are talking about technological unemployment. Creating cyber-physical systems that can be placed in any engineering object allows the object to communicate with other objects or people. Adding artificial intelligence to an object allows many ser- vices to be performed without human involvement. Thus, positions, such as law- yers, financial analysts, doctors, journalists, accountants, insurance brokers, and librarians, will also be partially or fully automated. Unlike previous industrial revo- lutions, there are very few new jobs (e.g. 0.5% in the USA), and 47% of workers in the USA are at risk of unemployment (Alpaydın et al., 2022). According to OECD 2018 estimates, 14% of jobs in the European community are at risk of automation, and 32% expect significant changes due to digitalization (Job Creation and Local Economic Development, 2018). The OECD 2020 report notes that the labour force has shrunk in almost 30% of OECD regions over the past decade. Most of those who lost their jobs were specialists with secondary education (Job Creation and Local Economic Development, 2020). Yusuf Alpaydın and Cihad Demirli (2022) confirm the trend that higher education provides access to jobs that can be less automated, while vocational education at the high school level provides more automated jobs. Being a university graduate reduces automation risks by 8.8%, while being a voca- tional training graduate increases automation risks by 2.5% in OECD countries. The digital society challenges smart education: education must now not only meet the demands of industry and the economy but also anticipate the emergence of new and disappearance of existing professions, not only create a digital educational space but also monitor the professional conformity of educational programs to the labour market and create practices of staff forecasting, focused on high-tech and knowledge-intensive sectors of the economy. Today, a paradoxical phenomenon occurs in the higher education system: students are taught professions that no longer exist, or the educational program’s skills are no longer available in digitalized pro- fessions. In 2021, the Ministry of Education of the Russian Federation (2022) was forced to close 23 educational programs of secondary vocational education and make changes to 43 educational standards. In parallel with this decision, within the framework of the National Project Education for the period 2019–2022, 59 advanced professional training centres and more than 3100 modern workshops were created and are functioning, which are provided with advanced technologies for conducting practical training sessions on mastering modern professions and subsequent passing exams in the form of a demonstration exam. 5 Smart Education: Social Risks and Challenges 111 Human Capital: New Characteristics According to T. Schultz’s definition, “Valuable qualities acquired by a person, which appropriate investments can enhance, we call human capital” (Schultz, 1968, p. 78). One of the peculiarities of the impact of information and communication tech- nologies on human capital is the increasing weight and importance in its structure of knowledge and skills required to exist in a digital environment. Digital literacy is becoming an essential part of human capital. These days, digital competencies and skills that form digital literacy are essential for professionals and everyday life, as the socioeconomic environment requires an employee with relevant knowledge and skills and a consumer. Thus, the intensity and effectiveness of interaction with the digital environment are directly dependent on digital literacy, broadly defined as the ability to safely and effectively use digital tools (Berman, 2017). Digital competencies are a set of knowledge, skills, and behaviours that facilitate finding, retrieving, storing, and evaluating digital data, interacting in a digital envi- ronment through digital tools, and developing informational content (Siddiqui et al., 2018). These competencies in education apply to both teachers and students. As the concept of digital competence did not have a universal interpretation in 2017, the European Commission attempted to establish a European Framework for the Digital Competence of Educators: DigCompEdu (Redecker, 2017). Twenty-two core com- petencies grouped into six spheres were identified. The purpose of this document was to provide a typical frame of reference for developers of digital competence models at all levels, from national governmental to educational organizations, as well as public or private providers of vocational training. Another trend characterizing the modern state of human capital is developing its network form. It implies the presence of abilities and competencies of interaction with network structures (power, educational, business structures, etc.), acquiring an integrated distributed form in the networking conditions of interaction processes. Network human capital can be defined as a set of capitalizable distributed net- work abilities, skills and competencies of managers, highly skilled workers and the population used for effective interaction via the Internet with network government agencies (e-government structures), network business structures (e-business, inno- vative firms, offshore programming), network scientific and educational communi- ties (network research groups, digital libraries, online universities), and social networks, which are used for various public goods, market benefits, and network effects (Dyatlov, 2017a). In the literature analysing network capital, there has long been an academic dis- pute as to whether network capital is capital. In particular, Robison et al. (2000) argued in their work that social capital has all “capital” qualities. Nevertheless, net- work capital differs from the other components of intellectual capital in that it is formed mainly in the interaction process between a company/individual and the unmanaged external environment (Kogteva et al., 2019). Network human capital can be defined as a set of capitalizable distributed network abilities, skills, and 112 S. Sharonova and E. Avdeeva competencies of managers, highly skilled workers, and the population at whole used for effective interaction via the Internet with network government structures (e-government structures), network business structures (e-business, innovative firms, offshore programming), network scientific and educational communities (network research groups, e-libraries, e-networks), network scientific and educa- tional communities (network research groups, e-libraries, network universities), and social networks that are used to obtain various public goods, market benefits, and network effects (Dyatlov, 2017a, p. 72). New functions of intellectual network capi- tal of specialists in the global digital economy are creativity, universality, polyfunc- tionality, network thinking and mobility, distance-network continuous education and self-education (Dyatlov, 2017b). The digital economy, focusing on the new qualities of human capital listed above, challenges the existing education system in the training of highly qualified person- nel. For example, in Russia, themes of higher education programs are formed and developed by the state on an instructional-administrative basis, often without con- sidering the interests of production enterprises. As a result, it leads to a mismatch between the qualitative characteristics of the educational process and the profes- sional competencies of employees required for the effective development of the enterprise (Shirinkina, 2018). However, business education must have guidelines for responding immediately to qualitative changes in industry demands. We need to understand what kind of society we are building and a person’s place in it. So far, there is no answer to these questions. There is an idealized description of a digital society with its bright economic prospects, its need for highly skilled IT personnel, and the need to develop creative competencies in students. This attitude states that all people are potentially talented, gifted, and capable of creative intellectual activ- ity. According to Leites (2008) and Semenov (2017), giftedness is always the result of a complex interaction of heredity (natural inclinations) and social environment, mediated by child activity (play, learning, work) and psychological mechanisms of personal self-development. Osipova (2019) emphasizes that giftedness is an unsta- ble phenomenon and needs specific psychological and pedagogical support and assistance (depending on the individual characteristics of each child). The question arises: Does the human capital of the digital society include the part of the popula- tion that does not have innate intellectual abilities, or they have not been able to realize them? What role will they play, and how will they exist in this society? In these questions lie both the challenge of a digital society and the risk of dehuman- izing society through smart education. Discussion Smart education is the answer to the massive digitalization of society under the pressure of Industry 4.0. Beginning innocently with the digitalization of education, where Internet technologies are used in the learning process, the transformation of the traditional system into smart education is moving to a new stage of 5 Smart Education: Social Risks and Challenges 113 development, characterized by the interaction between man and machine. For the first phase, it was essential to have a purely technical saturation and provide educa- tional institutions with computers, Internet access, and other supporting technologi- cal things. During this phase, which lasted until Covid 2019, the educational community created methodologies for using different devices, programs, and plat- forms. All these works were not systematic and were conducted either on the per- sonal initiative of a teacher, as part of some educational institutions, or private initiatives on the part of the authorities. However, in this chaos of growing practices, a picture of methodological shifts in the understanding of the smart education phenomenon is already emerging: the principle of nonlinear construction of educational programs, the characteristic of the student as a “network node”, the formation of a cloud of professions, a cloud of interests in the interdisciplinary space, etc. All these qualitative changes were based on a network methodology, which relies on two fundamental tenets: the obligatory presence of social networks and the net- work schedule. In 2019–2023, there have been many review articles in which the academic com- munity has focused more on systematizing the experience of building a “smart edu- cation”. At the same time, an understanding of the new digital society, the role of human beings in it, and the possibilities and consequences of digitalization of edu- cation is emerging in scientific works. The concept of “Society 5.0” is being devel- oped and has begun to be used to express the cooperation between society and technology. However, in this concept, scientists saw the danger to humanity in the contradictions between artificial intelligence and ethics. Saracel (2020) suggested the option of a “super-smart society” that reevaluates the relationship between humans and machines (p. 31). Salgues (2018) defined Society 5.0 as “the artificial intelligence society that strongly connects the physical and cyber spaces” (p. 1). The ethical debate focuses on events involving scientific and biotechnological interference with human beings’ physical and cognitive structure. A new scientific trend, “transhumanism”, has emerged, arguing for the legitimacy and necessity of such intervention. Digitalization processes have had a serious impact on didactics. Among the descriptions of the negative impacts of digitalization on the classical didactic arsenal that we have indicated, positive expectations related explicitly to smart education are maturing. Walkington and Bernacki (2020) note the need for learning theories underlying the personalization of learning. In their opinion, even if these theories emerge, empirical support still needs to be improved. As a result of their study, the researchers offered several recommendations for improving research work in personalising learning. Arapova et al. (2022) offered a concept of an intel- ligent platform aimed at implementing an individual learning path according to the student’s basic level of knowledge and psychological type. Thanks to the opportuni- ties provided by artificial intelligence, there is a chance to build a bridge between the processes of personalization of learning and individual approaches to develop- ing the learner’s personality. 114 S. Sharonova and E. Avdeeva Conclusions In our analysis of the emerging challenges and risks in the evolution of smart educa- tion, we assume that the development of smart technology is so rapid that society needs more time to grasp the possibilities. However, these technological possibili- ties are fascinating in their prospects. Smart education’s challenges and risks are associated with the lack of clear guidelines, which are hard to build when you are amidst a significant change. Nevertheless, identifying risk zones allows for activating scientific thought in search of either solutions to emerging problems or preventing their occurrence. 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