Education Strategy for the Net Generation

Abstract

This paper addresses the urgent need to redefine education strategies for the Net Generation in the context of rapid technological and societal changes. First, the educational challenge is placed within a broader philosophical and cultural framework, focusing on the fluid and evolving nature of knowledge and human experience. Building on the paradigm shift from Web 2.0 to Web 4.0 and the emergence of Education 5.0, this paper investigates the pedagogical implications of these developments. Through conceptual analysis supported by contemporary educational theory, this paper proposes a model of education that integrates personalized learning, real-time feedback, and collaborative, interdisciplinary environments. A special focus is placed on the role of educators as mentors, rather than mere transmitters of information, and on the ethical, social, and emotional dimensions of digital learning. This article highlights the importance of adjusting educational practices to real-life contexts and future challenges of young learners while ensuring that the humanistic essence of education is not lost.

1. Introduction

Sartre warns that existential questions cannot be answered once and for all. Philosophical questions, by definition, are questions that generations must continually ask themselves, as they instill a living sense that we are alive [1].

Today’s society finds itself at a crossroads: creating a new philosophy for future education systems seems to be a necessity. This is certainly a major challenge for educational policy creators and, of course, for all schoolteachers of the Net Generation. In this process, some of the most fundamental postulates in education need to be considered, especially the following:

• Learning (in schools) means learning for life;
• Life (the human society) is changing more rapidly and more drastically than ever before.

A conclusion can be drawn from these two premises: if life (the human society) is changing rapidly and dramatically, then the school system (the education system) should also change accordingly. Of course, one must be aware that not all changes are good and beneficial for society. Therefore, schools need to think critically; they need to operate with some time delay, which allows them to formulate critical reflections and to search for what are hopefully more rational and more optimal solutions.

The Net Generation primarily refers to Generation Alpha, whose members were born between 2010 and 2024. The expression Generation Alpha symbolizes the start of something new rather than a return to the old. Even more than their parents’ generation (the Millennials, or the Z Generation), there is a greater sense that Generation Alpha was shaped in the new millennium. Schools, as a synonym for society as a whole, are undergoing radical changes in the 21st century, and this is happening for at least two key reasons:

• Technologies enable completely different approaches, strategies, and possibilities for learning and teaching;
• Learners of the 21st century are (going to be) completely different than learners from the period when the fundamental digital pedagogical doctrines were still developing.

Considering the second reason, today’s learners are different, and they acquire knowledge and skills in different ways; therefore, further hypotheses must be articulated. When broadly analyzing the Alpha Generation period, the following conclusions may be formed:

•

The world is not one; there are two worlds.

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Intelligence is not one (bio-intelligence); there are two (diametrically opposed) intelligences (artificial intelligence—e-intelligence), ignoring all the other improved hybrid forms of intelligence, for example, those enabled by neural implants, which create new kinds of beings/entities—cyborgs.

•

Learning environments are changing at an exponential rate (transition from the Internet—Web 1.0 (1989) to the Internet Web 4.0 (2020–2022)).

•

The education process is performed in a different manner: from face-to-face education within the school space to combined education and the transition from formal to different kinds of non-formal education.

•

Individuals of the Alpha Generation have/will have different goals, require different motivation models, and learn, acquire, and process information differently, necessitating a transition from the existing digital pedagogy to some kind of new, generative pedagogy.

•

Teachers encounter different problems, from being “transferers” of knowledge in the past to becoming more and more interpreters of knowledge, i.e., those who give meaning to this knowledge. This necessitates a transition from teacher-centered lessons to placing the learner at the forefront in contemporary pedagogy. Finally, considering the connection between the teacher and technology, teachers will no longer only apply technology as a tool, as in digital pedagogy; instead, technology will become their “colleague” in this new, generative pedagogy.

2. Materials and Methods—Technology and Society

Let us focus initially on the first premise, i.e., on the technological changes, on the transition from the present to the future, or, rather, on the technological possibilities that enable completely different approaches, strategies, and learning and teaching possibilities.

Focusing on the interactions between technology and contemporary society, the fundamental premise is that there is not just one world. There are two intertwined worlds, namely, the physical world and the cyber world, which complement and support each other. It should also be emphasized that humans are no longer the only intelligent beings. Artificial intelligence (AI) and physical forms based on AI, i.e., cybernetic physical systems (robots), exist alongside humans, which are increasingly taking over part of both human psychomotor activities (robots and other cybernetic physical systems) and cognitive activities (intelligent agents). Physical cybernetic beings (physical, visible e-beings) exist in the physical and cyber worlds. On the other hand, human existence is limited to the physical world. Incorporeal (invisible) forms of e-beings (various intelligent agents, such as ChatGPT-4o) exist only in the cybernetic world. It is also worth mentioning that we are all time travelers, experiencing new things and acquiring new experiences as we travel forward through time. As we have new experiences, the numerous links between our brains’ nerve cells adjust to integrate them. It is almost as if we rebuild ourselves every day, sustaining a mental model of ourselves over time, where memory acts as the glue for our core identity. We can travel beyond physical time: we engage in mental time travel by recalling the past and past memories, and we engage in future time travel by envisioning tomorrow and what the future may bring. In doing so, we reflect on our current selves and remember who we were in the past and who we might be in the future. The other new kind of intelligence, i.e., AI, is also based on similar premises, but AI analyzes our past experiences and, based on those, creates (anticipates) potential results. This basis serves as the starting point of our research [2].

2.1. The Waves of Techno-Social Transformation

In their book, The Coming Wave [3], Suleyman and Bhaskar argue that society is not ready for the surge of upcoming potent new AI and that we are therefore showing resistance. They identified this “problem of containment” and denial of reality as a fundamental challenge of our time. However, they are certainly not the first to utilize such a thesis for describing the societal upheaval that accompanies the adoption of new and groundbreaking technologies. This upheaval encompasses a spectrum of diametrical possibilities, advocates, and opponents: for some, it is a chance for a fresh start, a time of new beginnings, while for others, it signifies that we are becoming swept away by an immense force that will overcome, enslave, and ultimately destroy us. The phrase “creative destruction” was coined to describe the technological upheaval dynamics driving the “innovative” waves in our current age [4]. Every wave in this sequence is characterized by the dominant and leading technologies of the era. From 1785 to 1845, the first wave introduced the steam engine, starting the Industrial Revolution; the second wave (1845 to 1900) continued with the introduction of electricity, starting the Second Industrial Revolution; whereas the third wave (1900–1950) sustained the Third Industrial Revolution with the arrival of computers. With each new technological wave came change, where creative destruction ushered in new approaches to work, life, and education as an integral part of society while simultaneously destroying the pre-existing structures. Western societies were transformed by the initial three waves of innovation, which displaced human labor with machines and the so-called “knowledge workers”. Historically, the economy of creative knowledge was driven by successive generations of students, researchers, teachers, experts, writers, intellectuals, and creators. Following the Third Industrial Revolution, the era of computing and electronics signified the fourth wave (1950–1990). It also marked the beginnings of AI research, with the goal of creating intelligent, “thinking machines”, i.e., computers with the potential of surpassing simple data manipulation. The aim of creating thinking machines was to develop computers capable of learning and utilizing natural language. In this study, we focus only on the development of technologies during the Net Generation era and investigate the period from the fifth wave onwards.

The digital age represents the fifth wave (1990–2020), which was characterized by the introduction of the Internet (1983), the World Wide Web (1989) [3], and the networks they enabled. The original web—Web 1.0—was a static environment consisting of HTML pages, also called the “read–write” web because users could only read or write to it [5]. By replacing Web 1.0’s static web pages with one-way information flows, Web 2.0 allowed users to collaborate and share data, comment, post opinions, blog, and communicate. This ushered in the era of the social web. The 1990s also witnessed the growth of machine learning and better neural network designs, which utilized the vast amounts of “big data” produced by Web 2.0 social networks, such as Facebook (2004), YouTube (2005), Twitter (2006), Instagram (2010), and TikTok (2016) [3]. Web 3.0, also known as the semantic web, allowed the networks to understand user preferences and thus personalize online experiences.

The sixth wave emerged with the decline of the fifth wave. It was marked by OpenAI, established in 2015 by a group dedicated to creating safe artificial general intelligence (AGI) that would benefit humanity. While there are several concepts of AGI, the crucial term in these debates is general. Although several specialized AI methods can carry out tasks as well as or even better than people in a particular domain, there is no general intelligence AI method that can currently outperform people across a diverse range of cognitive challenges. There is an ongoing debate in the AI community regarding the timeline for reaching AGI [6]. However, a large group of researchers predicts at least a 50% chance that several key milestones will be reached by 2028 and that machines will outperform humans in certain areas of ability. Regarding all possible tasks, researchers predict a 10% chance of outperforming humans by 2027 and a 50% chance by 2047 [7].

The introduction of a new transformer architecture by Google researchers in 2017 [8] made it possible to scale large language models (LLMs) to an unprecedented degree, leading to the emergence of generative AI (GAI). A 2018 paper by OpenAI introduced generative pretrained transformers (GPTs) [9], and the original GPT-1 was launched without much publicity. Since then, new versions of ChatGPT have been released regularly, now featuring billions of parameters and processing even more data with increasingly larger and complex models. Yet the public was not aware of GAI until November 2022, when ChatGPT-3.5 was released. At that moment, the dynamics of Schumpeter’s creative destruction were tangible, and Suleyman’s impending wave was becoming increasingly apparent. The division between humans and machines is now being erased by GAI and related Web 4.0 technologies (i.e., Augmented Reality/Virtual Reality/Mixed Reality (AR/VR/XR), Internet of Things (IoT), and robotics). Intelligent machines learn faster than we could ever imagine, altering the fundamental notion of knowledge along the way. Intelligence is no longer unique to humans; we face a world with another kind of intelligence, modeled according to the neural networks of our brains, yet made of semiconductors and silicone, not neurons and cells.

The sixth wave brought us to the end of 2023, and its creative and destructive effects are now becoming increasingly apparent. Italy initially banned ChatGPT because of privacy concerns, and Canada launched an investigation. Although initially slow to act, governments are starting to establish committees to lay the groundwork for informing and reflecting on emerging national and international policies. Driven primarily by economic growth and competitive edge, the US adopted an innovation-first strategy. The international summit on AI security, hosted by the UK in November 2023, culminated in the Bletchley Declaration, which was signed by representatives of governments and international bodies from multiple continents [10].

Unlike the US and the UK, the EU focused on the safe and ethical use of AI, which resulted in mitigating some of the more unpredictable consequences of otherwise unrestricted innovation. The EU Law on Artificial Intelligence was adopted in March 2024, setting a global standard for ethical and responsible AI development and application. Although the US struggled with industry impact and Europe prioritized ethical and safe use, Asia advanced with remarkable speed. Singapore emerged as the global AI adoption leader after introducing its National AI Strategy 2.0 in 2023 (Smart Nation Singapore, 2023) [11], expanding on its initial 2019 national strategy. The OECD’s 2024 Global Digital Economy Report (OECD, 2024) [12] revealed a massive disparity in AI investment: the US at USD 300 billion, China at USD 91 billion, and the European Union (EU) at a relatively much lower USD 45 billion. What about the education sector? How did it respond to these changes?

2.2. Trends in Education Development

Education has recognized the need for change, but like most of society, it has been divided. There are many advocates for the use of GAI, especially among young people who consider it a useful tool for finding shortcuts. However, there are also many opponents, especially teachers, who have had to introduce huge changes in the area of assessment, similar to those introduced during the pandemic, especially in the initial phase. In this study, we shed light on the transition from the existing digital form of teaching, where technology is just a tool, to the current situation of teachers.

Constructivism, as a basis of digital pedagogy, has proven highly effective in explaining how children learn and how real-world information is produced. Furthermore, constructivist teaching methods are highly successful at promoting student learning and are becoming more common in teacher training programs [13,14]. By offering a dynamic, personalized, and interactive experience, Web 4.0 improves constructivist eLearning. By analyzing student data, AI algorithms have the potential to provide tailor-made content and activities, thus promoting self-directed learning. Virtual classrooms, social media platforms, and other collaborative tools allow learners to connect with peers and experts, promoting knowledge exchange and the joint creation of knowledge. Moreover, Web 4.0’s integration of IoT devices allows for real-world, hands-on learning experiences. For example, learners can use sensors and actuators to collect and analyze data, applying theoretical concepts in practical contexts.

The real challenge of any learning theory is determining what will be needed for the future, especially considering that curriculum theory stresses the importance of the learning process over content acquisition. The ability to understand and apply learned material in the real world with a positive outlook is the fundamental requirement of all learning theories, including connectivism [1,14]. Mentorships and apprenticeships have served as types of personalized learning for hundreds of years. As educational technologies advanced during the second half of the 20th century, personalized learning emerged in the form of intelligent tutoring systems [2,14]. Moreover, Web 4.0’s inclusion of IoT devices enabled practical and real-world learning opportunities. For instance, learners could use sensors and actuators to gather and examine data, putting theoretical ideas into practice. How do GAI and LLM fit into this?

2.3. Generative Pedagogy (Generativism) and Generative Learning

GAI needs to be integrated into the everyday educational process to prepare students for the world of tomorrow, as the fundamental goal of school is still to prepare students for the life that awaits them. Teachers and students should be proficient in using GAI, not only to enhance teaching and learning but also to conduct research and create knowledge in their specific fields. The problem is creating educational experiences that cultivate crucial critical thinking skills needed for engaging with this emergent and still relatively flawed technology. What is the best way to utilize GAI’s generative and social advantages while fostering inquiry skills and critical thinking? We attempt to answer these questions using a digital design (Digital modeling in education refers to using computer-based simulations and models to teach and learn various concepts, skills, and processes. It leverages technology to create interactive and engaging learning experiences that can be more accessible and effective than traditional methods. Digital modeling and generative AI are transforming education, offering personalized learning, automating tasks, and enabling innovative teaching methods. Generative AI can analyze student data to tailor instruction, create simulations, and provide real-time feedback. Digital models allow educators to visualize complex concepts and create interactive learning environments) framework that is appropriate for the generative and social aspects of GAI, specifically in the educational context [15].

The theory of generative learning predates both Web 4.0 and the Internet. The approach is rooted in the idea of learning as a constructive act, drawing from educational theories of cognitive development and cognitive revolution. The concept of generative learning, introduced by Wittrock [16], emphasizes the requirement to connect students’ previous learning and new materials. Based on the associative model, the process utilizes the schemas and memories already present in our minds. In this way, learners connect new concepts with their existing knowledge in a process involving motivation, creation, attention, and memory. Even though the generative learning theory predates the digital era, there are striking similarities between concepts like “schemas” and “stored memory” and how computers function. Of course, this is no coincidence, considering that cognitivists, who influenced our knowledge of learning, conceptualized the mind as a computer, as evidenced by the work of cognitive psychologists and cognitive scientists. GAI, and particularly LLMs, built on neural networks, directly reflect the cognitivist idea of the mind as a computer.

Generative learning is a meaningful process in which students actively organize and connect new materials, provided by teachers or even GAI experts, with their pre-existing knowledge. Therefore, generative learning relies on both the way information is presented to students, i.e., teaching methods, and students’ attempts to make sense of it, i.e., learning strategies and activities designed to promote understanding. Because students must actively engage with and integrate knowledge from various sources (either by their teacher, text, or GAI results) to reach an understanding for later applications, this process is vital: generative learning is active learning, influenced by both constructivism and connectionism. Wittrock [16] called this knowledge construction process sensemaking; it is crucial for working with GAI as it helps us to think critically and question GAI’s results while actively participating in the sensemaking process. Generative learning is also evident in the select–organize–integrate (SOI) model of generative learning developed by Mayer [17], where “the processes of organization and integration are called generative processing, which involves the construction of a new mental representation based on relevant existing knowledge.” Within the SOI framework, learning involves three core cognitive processes:

• Students select the incoming sensory information.
• Students organize sensory information into a mental representation.
• Students connect the mental representation to their existing long-term memories.

Mayer’s SOI thesis (select–organize–integrate) closely resembles Wittrock’s initial ideas of attention (selection), internal connection-building (organization), and external connection-building (integration). Moreover, it recognizes the crucial role of metacognition and motivation in generative learning.

The generative learning approach was recently revitalized by Fiorella and Mayer. They transformed Wittrock’s original components of sensory creation (generation, motivation, attention, memory) into teaching methods for promoting student understanding. They created several generative learning strategies that help in “the process of transforming input information (e.g., words and images) into usable knowledge (e.g., mental models, schemas).” In this way, various generative learning strategies [18,19] help students understand outcomes through activities like summarizing, mapping, imagining, drawing, teaching, self-testing, self-explanation, and acting—all of which can probably incorporate GAI as a collaborative learning partner.

GAI is a new technology that is neither dependable nor trustworthy. It hallucinates and fabricates information, mimicking and sometimes reinterpreting our own biases in the Web 4.0 data on which it was trained, much like we do. It needs ongoing correction, monitoring, and fine-tuning. While technology’s capabilities are advancing quickly, including speed, memory, and power, its many problems mean that students must develop a critical approach in their interactions with it so that it supports, rather than hinders, their critical thinking. Students using GAI must learn to understand its products and place them within a wider context. They should recognize GAI as a critical opponent, disguised as an assistant, and engage with it from a detached and critical perspective. This encapsulates proper human-centered learning with GAI.

3. Results

Following the emergence of smart technologies, artificial intelligence, and robotics, and especially the recognition of the existence of GAI and LLM, in the mature period of Society 5.0, we have had to adapt and develop Education 4.0 towards Education 5.0. The foundation of Education 4.0 was predominantly digital pedagogy [20]. Education 4.0 was aligned with the requirements, challenges, and opportunities of the early 21st century, but we must be aware that nothing has been the same since 2022. At the beginning of the 21st century, education was no longer based on a set of fixed rules or concepts, but it became an increasingly dynamic process. Instead, visions for the future development Education 5.0’s system and practices based on new knowledge have increasingly come to the fore. Education 5.0 represents a fresh approach to learning based on staying in tune with the demands and possibilities of today’s world. It is not a rigid set of rules but a progressive vision for the direction of development, both of society and of education, which is one of the most important elements of any society [21].

In Education 4.0, learning processes can be technically realized using learning programs (intelligent tutor) (Frank and Mader, 1971) [22,23]; in this case, they should include a learning algorithm, B, expressed symbolically using a mathematical logical function:

B = f (Z, L, M, P, S)

(1)

These five conditional variables, namely, L (learning material), M (media), P (psychological structure), S (social structure), Z (setting learning goals), and B (teaching or learning algorithm), are linked to create a unified whole.

During the development of Education 4.0, however, significant discontinuities occurred that required revisions to the motion equations in the dynamic process. In education, it is no longer enough to follow modern trends in the development of society with a system of continuous improvements (e.g., Education 4.1—4.k), but a comprehensive reconstruction of all fundamental principles is required. This essential discontinuity has become the social awareness of GAI’s entry into our social system in 2022. As we pointed out, since the basic thesis that school is a school for life must remain the same, a complete transformation of the fundamental paradigms of education and a transition from digital pedagogy to generative pedagogy is required. This includes the transition of Education 4.0 to modern, generative Education 5.0, which is shown in Figure 1.

We need a new educational paradigm, i.e., Education 5.0. Based on this, we can redefine the learning process algorithm [25] as follows:

B = f₁ (I, T, L f₂ (I), UP, ME) f₂ (SK, A, Y).

(2)

Equation (2) includes two kinds of functional dependencies. f₁ represents the symbol system (primarily decision-making areas), whereas f₂ represents the net system (GAI), covering intentionality and the area of conditions. The equation expresses the learning process algorithm as a mathematical–logical function with seven conditional variables:

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I—intentionality, which is difficult to define considering its goals are inherently linked to the topic.

•

T—topic.

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L—learning environment.

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UP—learning aids.

•

ME—didactics or, strictly speaking, the methodology of lessons. Various topics can be presented in various ways. This conditional variable serves as the primary factor for the optimization of the learning process.

•

SK—socio-cultural character.

•

A—anthropogenic character.

•

Y—psycho-social characteristics.

Therefore, we cannot use identical tools and methods to symbolize both areas (intentionality being the only exception). While symbol systems can still model decision-making areas, as was the case historically (function f₁), the conditions area requires modeling using network systems (function f₂) or other AI methods (e.g., GAI or ML). These are necessary for enabling the complex individualization and differentiation required in the learning process. Nowadays, generative pedagogy 5.0 should be understood as a hybrid system, not a mere symbol system, as (EQ. 1) was the case in the past, because it integrates two distinct formalization methods from the cognitive platform—symbolic and connectionist. A hybrid model would minimize the drawbacks and maximize the benefits of both systems [25]. Web 4.0 and Education 5.0 brought extensive changes to education, and today, several key aspects reveal how these new technologies are redefining online learning, including advancements in personalization, real-time feedback, and increasingly interactive and collaborative environments. These will be described in the following section.

3.1. Enhanced Personalization

By integrating AI, ML, big data analytics, and adaptive learning systems, Web 4.0 and Education 5.0 enhance the online learning experience. This enables platforms to analyze student data on behavior and performance and create personalized content and learning pathways [26]. Additionally, using big data analytics, online learning environments can extract relevant information from extensive datasets, including student engagement, test scores, and course materials. Teachers can utilize this information to inform their instructional design choices and construct personalized learning experiences that cater to students’ unique needs and preferences. The widespread adoption of Learning Experience Platforms (LXPs) in the Web 4.0 and Education 5.0 era has further improved the personalization of online learning experiences [27].

3.2. Real-Time Feedback and Assessment

Chatbots powered by AI can provide immediate feedback on quizzes or explain course content, thus improving students’ engagement and understanding [28]. Teachers can adapt their teaching methods based on student needs and behaviors, as real-time assessment tools track how students interact with learning content instantly. Moreover, integrating sensor technologies, like eye tracking and facial recognition, can offer additional insights into student engagement and attention during online learning [29]. By using these technologies, online learning environments can offer better personalized and effective learning experiences that align with the principles of Web 4.0 and Education 5.0.

3.3. Collaborative and Interactive Learning

New technology can also be used to create interactive discussion platforms, which can simplify and improve collaboration. These interactive and collaborative online platforms create a flexible discussion space, allowing students from different departments or even different cities to meet at their convenience, alleviating the difficulties of scheduling and coordinating the meetings [30].

The existing and well-researched Education 4.0 model [20,21,31] was based on the lessons learned during the first two decades of the 21st century and detailed in the works of various 21st-century authors [31,32,33] and research by international institutions, such as OECD [12] and NAIS [11]. This model has transitioned into the post-2022 period, where we are aware of the existence of large language models and the general conclusion that nothing will ever be the same again. Thus, education also needs a new paradigm, which we call Education 5.0 for short. The transition from Education 4.0 to Education 5.0 is shown in Figure 2.

Next, we briefly highlight the advantages of such an approach using a case study. The presented case study builds on previous research published in the open-access journals Applied Sciences and Education Research [27,34].

3.4. Case Study

In this study, we aim to illustrate this transition from digital to generative pedagogy and Web 4.0, as presented in Figure 2 [27,34]. This example highlights contemporary technologies, such as VR/AR, and the creation of virtual classrooms through generative pedagogy.

To help students achieve higher educational goals (in-depth understanding and critical evaluation), the educational system must be designed to increase students’ motivation. To achieve this motivation, we must create learning models that support educational and technological progress. This concept has already been noted by various authors, who argue that most frameworks are models that investigate the causal relationship between immersive learning factors that influence learning outcomes. Although this theoretical aspect is essential for advancing research, the area still lacks more practical frameworks. The use of immersive virtual environments [35,36] is also highlighted in the proceedings of the 9th International Conference of the Immersive Learning Network, iLRN 2023 [37].

In our previous research, [27,34] we developed and evaluated an efficient immersive virtual environment and evaluated the cybernetic physical learning model (CPLM), a modern learning and teaching model based on the immersive education model (a precursor to Education 5.0), as shown in Figure 3 [34]. The introductory motivation of the CPLM was enriched with VR 360 films that enhance user experience. We added cyber–physical training, EEG analysis (with which we measured cognitive activities and systematically compared the results in the context of the learning model [27]), and VR evaluation and evaluated the results based on the results. We found that VR experiences help students understand basic concepts, as shown by other similar research [36,37], especially [31], which analyzed Education 4.0 features, principal components, and characteristics. These results highlight the adaptation of teaching and learning practices to the changed requirements of Industry 4.0 and Society 5.0.

Our approach involves a variety of educational methods, using traditionally written materials and electronically written materials with closed and open hypertexts integrated with modern online classrooms, collaborative learning tools, smartphones, and assessment tools. For the third part of modern education, we propose using technologies, such as 360° video and XR immersive technologies, to further enhance student motivation and help them achieve higher educational goals. We built a new classroom philosophy based on immersive education, as presented in Figure 4.

In vocational and high school education, we taught robotics in automation and robotics learning modules. The reference sample included 30 students. The research was conducted using the classical education approach with a control group (n = 15). In the second part, we introduced an experimental group (n = 15) based on a new classroom philosophy considering immersive education and introducing a modern teaching model for robotics education, as presented in Figure 4. Figure 5 shows the questions we asked the experimental group of students. Overall, 90% successfully completed the experimental exercise (only 85% successfully completed the control exercise in the control group), consider the shorter time needed to complete the learning training than the control group. Based on the analysis of all results, there is a high probability that the innovative CPLM learning model effectively influences cognitive activities, leading to faster perception of given information and more effective learning of vocational skills. The entire research is described in our previous open-access research [27,34].

4. Discussion

The introduction of mobile devices in the 2010s increased the accessibility of education. Recent progress in AI, VR/AR, and adaptive learning has transformed eLearning, offering personalized experiences that match the individual needs and interests of students. As AI and VR/AR are anticipated to completely revolutionize education, eLearning will also most likely continue to evolve, offering more and more interactive, engaging, and accessible learning experiences for all students worldwide [26]. Constructivist eLearning is improved by Web 4.0, which offers dynamic, personalized, and interactive opportunities. AI algorithms analyze student data and deliver personalized content and activities, thus encouraging self-directed learning. The arrival of Web 4.0, focused on advanced data analysis and semantic web technologies, has led eLearning to previously unimaginable levels of personalization, effectiveness, and engagement. By employing technologies such as IoT, ML, and AI, Web 4.0 seeks to transform the field of education. AI algorithms analyze the behavior and preferences of learners to create personalized learning pathways and content recommendations tailored to individual needs [26].

The gap in technological development between the AI industry and schools was profound. Overnight, AI “gurus” declared that the imminent “transformation” of education was upon us, while skeptical voices from pedagogical academia (pedagogical gurus) responded by dismissing any benefits and focusing entirely on the potential downsides. After 2022, when society became aware of the existence of AI and LLMs, critics often countered the rapid advancement of the AI industry by referencing Gartner’s wave of enthusiasm and forecasting the certain arrival of the “trough of disillusionment”. Conversely, the innovation wave in pedagogical academia seemed to resemble Kübler-Ross’s cycle of grief [38]. This process began with shock and denial of AI’s capabilities, progressed to anger, followed by depression when LLMs proved to be a genuine problem, and finally culminated in a reluctant acceptance that GAI may be here to stay.

The introduction of mobile devices in the 2010s increased the accessibility of education. Recent progress in AI, VR/AR, and adaptive learning has transformed eLearning, offering personalized experiences that match students’ individual needs and interests. As AI and VR/AR are anticipated to revolutionize education completely, eLearning will most likely continue to evolve, offering more interactive, engaging, and accessible learning experiences for all students worldwide [26]. Constructivist eLearning is improved by Web 4.0, which offers dynamic, personalized, and interactive opportunities. AI algorithms analyze student data and deliver personalized content and activities, thus encouraging self-directed learning. The arrival of Web 4.0 [33], focused on advanced data analysis and semantic web technologies, has led eLearning to unimaginable levels of personalization, effectiveness, and engagement. By employing technologies such as IoT, ML, and AI, Web 4.0 seeks to transform the field of education. AI algorithms analyze the behavior and preferences of learners to create personalized learning pathways and content recommendations tailored to individual needs [26,33].

The gap in technological development between the AI industry and schools was profound. Overnight, the AI “gurus” declared that education’s imminent “transformation” was upon us. In contrast, skeptical voices from pedagogical academia (pedagogical gurus) responded by dismissing any benefits and focusing entirely on the potential downsides. After 2022, when society became aware of the existence of AI and LLM, critics often countered the rapid advancement of the AI industry by referencing Gartner’s wave of enthusiasm and forecasting the certain arrival of the “trough of disillusionment”. Conversely, the innovation wave in pedagogical academia seemed to resemble Kübler-Ross’s cycle of grief [38]. This process began with shock and denial of AI’s capabilities, progressed to anger, followed by depression when LLMs proved to be a genuine problem, and finally culminated in a reluctant acceptance that GAI may be here to stay.

We cannot and must not deny the thesis that we have already entered the sixth wave of technological transformation. We no longer discuss the use of AI in various processes, but we increasingly discuss the consequences of its use [32]. We ask ourselves how AI is changing the world and, above all, in which direction AI is evolving. When we discuss the development of AI in the sixth wave, less focus is given to generative artificial intelligence or large language models, which, according to many, have somehow reached the edge of the actor, as their development is slowing down significantly. For example, ChatGPT-5 is only a gradual improvement of its predecessors, rather than the promised major turning point in performance that previous versions have brought, as reported in the Financial Times on 7 August 2025. When discussing the direction of AGI and Web 4.0 [33], we encounter a fundamental problem of emotions and consciousness, which is also a basic philosophical problem in human research. The question of whether AGI should feel and have the same consciousness as humans is increasingly being asked. However, if this is unimportant in the economic world, it is undoubtedly a fundamental educational issue. In education, we must not only develop cognitive components (intelligence) but also the affective area, which includes relationships, empathy, and emotional intelligence—human, not machine, emotional intelligence. If we do not develop this in school, humanity, with its limited intelligence, will very soon, if not today, not be able to compete with GAI, and certainly not AGI. Therefore, schools must foster the development of people who will not only be “machines” but will be, above all, people. This is what our Education 5.0 concept is all about.

5. Conclusions—What Are the Implications for Education?

Our economies and political systems are already feeling the considerable effects of Web 4.0-based GAI, which will have an even greater impact on our cultures and future work methods. The precise nature of these changes remains unclear, yet we can say that the development will be extremely rapid and incomparable to any development in the past. The stakes will be especially high for education, given that its core output is knowledge, driven by human intelligence. Artificial intelligence raises crucial questions regarding the very essence of knowledge and, thus, the worth of the original knowledge workers: teachers. This leads to the following fundamental questions:

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What role will teachers play in a world where machines have access to all created, written, or shared human knowledge?

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What value does producing educational content have when machines can instantly recall all existing teaching and writing?

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What is the purpose of assessment in an environment where machines can autonomously determine and execute their tasks?

Currently, we cannot answer these and many other unknown questions. What we do know is that the AI evolution of creating, producing, and disseminating knowledge necessitates that the role of the teacher is to be radically transformed. Despite the precise nature of these upcoming changes, we must prepare for a future in which we work and coexist with machines symbiotically. In this new environment, the teacher will be, for better or worse, one of the many voices of authority, not the sole one, as was the case so far. In this transition to the future, which we can call digital pedagogy, generative pedagogy will be discrete, requiring a quantum leap in an extremely short time frame.

Integrating GAI into education must ultimately be guided by serious pedagogical principles rather than driven solely by technological enthusiasm. As this paper has shown, the transformation from digital to generative pedagogy is not merely a technological upgrade—it represents a paradigmatic shift in how we understand learning, teaching, and the purpose of education itself. Future teachers will have to be more than facilitators and interpreters of knowledge; they will have to become reflective co-navigators in a hybrid human–machine learning environment. By embracing this responsibility with a hefty dose of critical awareness and pedagogical integrity, we can make sure that education remains a human-centered endeavor—even in the era of intelligent machines. The school must therefore foster the development of people who will not only be “machines” but will be, above all, people. This is what our Education 5.0 concept is all about.