Educational Transformation Through Emerging Technologies: Critical Review of Scientific Impact on Learning

Abstract

Educational transformation is increasingly influenced by emerging technologies, which offer unique opportunities to redefine learning. This study aims to critically analyze the scientific production related to the use of emerging technologies in the educational field, focusing on their impact on the teaching–learning process. A systematic review of the scientific literature was carried out, analyzing a total of 1567 articles from 2000 to 2024. The results reveal that, although there is a growing interest in the integration of technologies such as artificial intelligence and augmented reality, concerns also emerge about their implementation and effectiveness. In addition, research trends are identified that suggest a multidimensional approach to the use of these technologies, highlighting the importance of teacher training and the educational context in which they are applied. The conclusions indicate that, to maximize the positive impact of these technologies, an informed pedagogical approach that considers the advantages and challenges they entail is essential. This analysis provides a foundation for future studies and guidance for educators and policy makers in effectively incorporating emerging technologies into the educational environment.

1. Introduction

Education in the 21st century has seen a profound transformation, driven by the accelerated advance of emerging technologies. In this context, tools such as artificial intelligence (AI), augmented reality (AR), adaptive learning, and gamification have begun to be integrated into classrooms, providing unprecedented opportunities to enrich teaching and learning (Goorney et al., 2023; Jaboob et al., 2024). This evolution is important because it improves access to information and facilitates the personalization of learning, but also because it fosters an environment in which students can interact more dynamically and collaboratively with educational content. However, the incorporation of these technologies poses significant challenges that must be addressed, especially in terms of their effectiveness, their alignment with pedagogical theories, and the preparation of educators to use them effectively (Kantak et al., 2024).

The purpose of this paper is to conduct a comprehensive analysis of the scientific production related to emerging technologies in education, highlighting current trends, the theoretical currents that support them, and the pedagogical implications derived from their use. It is essential to understand how these tools are being researched and applied in diverse educational contexts, as well as to assess their impact on learning. In this regard, the existing literature has identified different approaches and theories that support the use of emerging technologies in education. For example, connectivism, proposed by George Siemens (2005, as cited in Mozelius et al., 2016), highlights the importance of networks and connections in learning, arguing that knowledge resides in the individual, and the social and technological context in which he or she finds himself or herself. Likewise, constructivism, defended by authors such as Vygotsky (1978) and Piaget (1954, as cited in Piaget & Castrillo, 1973), underlines the relevance of social interaction and the active construction of knowledge, which resonates with the potential of technological tools to facilitate collaborative learning experiences (Mozelius et al., 2016; Piaget & Castrillo, 1973).

The review of the current state of the research field reveals several highlights in the application of emerging technologies. On the one hand, there has been an increase in the use of AI for personalization of learning, allowing content to be tailored to individual student needs. Research such as Woolf et al. (2013, as cited in Raffnsøe et al., 2019) has shown that AI can provide immediate and adaptive feedback, enhancing educational experience. On the other hand, gamification has gained ground as an effective strategy to motivate students and foster their engagement with learning. Studies by Deterding et al. (2011, as cited in Raffnsøe et al., 2019) suggest that incorporating game elements into the educational environment can increase student motivation and engagement (Hofstetter, 2004; Raffnsøe et al., 2019).

However, the field of emerging technologies in education also presents controversies and divergences in the literature. Some researchers warn that excessive reliance on these tools can have adverse effects on learning. For example, a study by Hwang and Chen (2017, as cited in Eva et al., 2024) points out that while technologies can enrich the learning experience, their inappropriate use can lead to a decrease in social interaction and critical thinking. These concerns highlight the need for a balanced approach in the integration of emerging technologies, considering their benefits, but also potential limitations and challenges (Eva et al., 2024; Orozco et al., 2012).

The main objective of this work is to provide a comprehensive framework that summarizes the current scientific production on emerging technologies in education, while identifying areas that require additional attention. This analysis seeks to contribute to the development of a solid knowledge base that guides the implementation of these tools in educational contexts and promotes effective pedagogical practices. It is hoped that this study will foster an informed dialogue on the role of emerging technologies in education, highlighting their potential to improve the quality of learning in an ever-changing global environment.

Likewise, the aim is to provide a clear overview of how emerging technologies are being researched and applied in education, and to serve as a valuable resource for educators, educational policy makers, and future researchers. By identifying trends, challenges, and opportunities, it is hoped to contribute to the advancement of an educational approach that is adapted to the demands of the 21st century and that fosters meaningful and effective learning.

In the analysis of the reviewed literature, several methodological limitations are identified that should be considered in future studies on the integration of emerging technologies in education. Firstly, it will be noted that many studies are based on small samples or specific populations, which limits the ability to generalize the results to broader educational contexts. In addition, the lack of longitudinal studies that allow the evaluation of the long-term effects of emerging technologies on teaching–learning processes is highlighted, since many of the analyzed studies are cross-sectional and do not allow observing the evolution of phenomena over time. Biases in data collection are also identified, especially in those studies that are based on self-reports of participants or on data from institutions with specific interests, which may affect the objectivity of the results. Finally, a scarce methodological diversity will be observed, with quantitative approaches predominating instead of qualitative approaches, which could offer a more complete and richer view of how emerging technologies impact the educational context.

The structure of the article is organized as follows: Section 2 presents the theoretical framework, where the educational trends and relevant concepts on emerging technologies in education are analyzed. Section 3 details the methodology used to carry out the research, including the design and the tools used. Section 4 presents the results of the data analysis, providing a quantitative and qualitative interpretation of the findings. Section 5 links these results with the theoretical framework. Finally, Section 6 offers conclusions that summarize the main findings and their implications for future education.

2. Framework

Emerging technologies are revolutionizing the education sector by introducing tools such as AI, AR, and data analytics. These innovations allow for personalized learning, improved interaction, and facilitating new ways of teaching. This theoretical framework explores the concept and importance of these technologies, their key applications in education, as well as the challenges and opportunities they create.

2.1. Emerging Technologies in Education: Concept and Evolution

In the context of education, technological advancement has significantly transformed teaching and learning dynamics. Emerging technologies are defined as those technological innovations that, although they have not yet reached widespread penetration, have the potential to profoundly impact various sectors, including education. These technologies offer new tools, and foster a paradigm shift in the way educators design, deliver, and assess learning (Crawford et al., 2024).

The importance of emerging technologies in education lies in their ability to adapt teaching processes to the demands of an increasingly digitalized, connected, and complex problem-solving society. In this sense, the use of emerging technologies is not limited to the mere adoption of devices or software, and implies a reconfiguration of traditional pedagogical models, providing new learning methods based on interaction, personalization, and collaboration.

Among the most prominent examples of emerging technologies in education are AI, AR, data-driven learning, VR, and machine learning. Each of these technologies offers a range of possibilities and applications that transform both classroom teaching and individualized learning, opening new avenues to address educational needs (Vázquez-Cano et al., 2020).

AI has emerged as a key tool for personalizing learning. AI systems make it possible to analyze large volumes of data on student performance, identify patterns, and offer individualized recommendations. For example, learning platforms that use AI can adjust content based on a student’s progress, suggesting activities or additional readings based on areas in which they need more support. In addition, AI-powered virtual tutors can offer real-time feedback, contributing to a more dynamic and effective learning process.

Another emerging technology with a significant impact on education is AR. AR allows students to interact with digital content overlaid on their physical environment, creating immersive learning experiences. In areas such as natural sciences, history, or art, AR facilitates the visualization of complex phenomena or structures that would otherwise be difficult to represent in a traditional environment (Marín-Gutiérrez et al., 2020). This technology enriches the learning process and stimulates students’ curiosity and critical thinking by allowing them to explore and manipulate information in a three-dimensional space.

Data-driven learning is another key example of emerging technology in education. By collecting and analyzing data, this technology allows educators to make informed decisions about the effectiveness of teaching methods and student progress. Tools such as learning management systems (LMSs) collect a wealth of information about student behavior, such as the time they spend on each task, their level of participation in discussions, and their results on assessments. These data provide a detailed view of the learning process, allowing the adaptation of pedagogical strategies to improve performance and knowledge retention.

On the other hand, VR is gaining ground as a powerful tool for creating immersive learning environments. VR offers students the opportunity to “travel” to remote locations or explore abstract concepts in an interactive environment (Marin-Diaz et al., 2022). For example, science students can explore the inside of a cell or navigate through outer space, while history students can immerse themselves in a virtual recreation of an ancient civilization. These experiences enhance understanding of concepts, and allow students to develop practical skills in a safe, controlled environment (Zhang et al., 2023).

Furthermore, machine learning, a branch of AI, enables educational platforms to continuously improve their services by analyzing data and predicting trends. Systems that integrate machine learning can learn from students’ interactions with content and automatically adjust to improve educational outcomes. This adaptive capability is especially useful in personalized learning environments, where each student follows a unique educational path based on their strengths and weaknesses.

However, the implementation of these emerging technologies in the educational field also presents significant challenges. One of the main challenges is the digital divide, which refers to the disparity in access to quality emerging technologies between different regions and socioeconomic sectors. Despite the advances, many educational institutions, especially in rural areas or developing countries, still face difficulties in integrating these technologies due to the lack of adequate infrastructure or the lack of digital skills in both teachers and students (Aguirre & Mitschke, 2011; Ayuso del Puerto & Gutiérrez Esteban, 2022).

Likewise, teacher training is a critical aspect for the successful adoption of emerging technologies in the classroom. Many educators lack the skills necessary to use these tools effectively, which can limit their ability to harness the full potential of emerging technologies. Ongoing training and professional development are essential for teachers to stay up-to-date on new tools and to be able to integrate them into their pedagogical practices effectively.

Another challenge to consider is educational regulation and policies. The rapid evolution of emerging technologies often outstrips the ability of educational institutions to develop policies and regulatory frameworks that ensure their ethical and effective implementation (Caerio Rodríguez et al., 2020). It is critical that educational authorities work in collaboration with technology experts to develop standards and guidelines that guide the appropriate use of these tools, protecting student privacy and ensuring equitable use.

Despite these challenges, the potential of emerging technologies to transform education is immense. The future of education will depend on the ability of educational institutions to effectively integrate these tools, ensuring that students acquire the skills necessary to face the challenges of an increasingly complex and digitalized world. The evolution of these technologies offers the opportunity to create more inclusive, personalized, and interactive learning environments, where each student can develop their full potential.

Emerging technologies, such as AI, AR, and machine learning, have generated a profound change in the educational field. To understand their impact and application, it is key to know the contributions of prominent researchers who have led studies in this field.

In AI applied to education, authors such as Ryan Baker and Vincent Aleven have been influential. Baker has worked on the development of predictive models that use data to identify learning patterns, helping to personalize teaching. His research in Learning Analytics explores how AI can improve educational outcomes through real-time analysis (Lawrence et al., 2024). On the other hand, Aleven, known for his contributions in intelligent tutors, has advanced the integration of systems that provide automatic and adaptive feedback to students.

Chris Dede, professor at Harvard University, is another pioneer in AR and VR research in education. His studies have highlighted how these emerging technologies can create immersive learning environments that increase student engagement and enhance their understanding of complex concepts. In his work, Dede has developed AR experiences that allow students to solve real-world problems through interactive simulations (Dieterle et al., 2024).

In terms of data-driven learning, researcher George Siemens has been one of the pioneers in using data analytics to improve teaching processes. Siemens is known for his work on connectivism, a learning theory that highlights the importance of networks and technology to acquire knowledge in the digital age. He has proposed that by analyzing educational data, more personalized pedagogical strategies can be created that respond to the needs of each student (Tulauan & Maguddayao, 2019).

The field of VR has also been the subject of attention by researchers such as Jeremy Bailenson, director of the Virtual Human Interaction Lab at Stanford University. Bailenson has investigated how VR can be used for education, but also to foster empathy and improve decision-making in educational contexts. His studies have shown how interaction in virtual environments can provide deeper and more meaningful learning experiences, compared to traditional methods.

In the field of adaptive technologies and machine learning, Carolyn Rosé of Carnegie Mellon University has made key contributions. Rosé has worked on developing educational chatbots and tutoring systems that employ machine learning to adapt to the needs of each student (Kumar et al., 2024). Her research has shown how these technologies can provide personalized support in real time, improving learning outcomes and increasing student engagement.

In the field of adaptive learning, Neil Heffernan also stands out, known for his work in creating intelligent educational systems. Heffernan has developed platforms such as ASSISTments, which uses AI to provide teachers with real-time information on student progress, allowing lessons to be adjusted based on their individual needs (Decker-Woodrow et al., 2023).

In gamification and the use of technology-based educational games, James Paul Gee has been a reference. Gee has explored how the principles of game design can be applied to education to make learning more interactive and motivating. His work has focused on creating game-based learning environments that encourage problem solving and critical thinking, integrating emerging technologies such as AI and AR (Isaías, 2018).

2.2. Digital Competencies for Teachers in the Use of Emerging Technologies

Education has undergone a profound transformation in recent decades, driven by the increasing adoption of emerging technologies. These technologies, including AI, AR, VR, blockchain, and learning analytics, are redefining the way we teach and learn. However, the effective integration of these tools requires teachers to develop advanced digital skills. It is not just about managing the tools, but about understanding their pedagogical impact and transformative potential in the classroom. Therefore, the development of digital skills for teachers has become a key factor in ensuring an adequate and effective implementation of these emerging technologies (Suárez-Guerrero et al., 2020; Wymbs & Kijne, 2003).

Digital skills in education have evolved in recent decades, in response to the rapid adoption of Information and Communication Technologies (ICTs) and, more recently, emerging technologies. As technological tools have become more sophisticated and their integration into educational systems has increased, the demands on teachers have changed. While in the early stages of digital transformation, teachers’ digital competence focused on the use of basic tools such as word processors or presentation platforms, the current landscape demands a much more complex set of skills (Cheng et al., 2022).

A widely used framework to guide this evolution is the European Framework for the Digital Competence of Educators (DigCompEdu). This framework provides a structure for the development of digital competences in six key areas: professional engagement, creation and use of digital resources, teaching and learning, assessment, student empowerment, and developing students’ digital competence (Cabero Almenara et al., 2022; Inamorato dos Santos et al., 2023). This comprehensive approach recognizes that digital competences are not limited to technical skills, and include the ability to pedagogically integrate emerging technologies to improve learning outcomes.

As emerging technologies, such as AI and learning analytics, are introduced into the educational environment, digital competences are expanding into previously unexplored areas. For example, learning analytics allows teachers to gain deeper insights into student performance through data collected in real time, making it easier to personalize teaching. However, to use these tools effectively, teachers need not only technical skills in using analytics platforms, but also the ability to interpret the data and implement pedagogical changes based on that information (Antón-Sancho et al., 2024).

AI, for its part, is shifting the focus of teaching toward more adaptive and intelligent models, where algorithms can adjust learning content and activities based on individual student progress. This requires teachers to understand how AI works in education, but also to be able to design learning experiences that maximize the benefits of this technology without losing sight of fundamental pedagogical goals.

The integration of emerging technologies presents several challenges for teachers, the most prominent of which is the training gap. Although many teachers have basic ICT skills, emerging technologies such as AI, AR, and learning analytics require specialized knowledge that many education professionals do not yet possess (Andretta, 2005). This lack of training creates resistance to change and a perception of insecurity about how to use these technologies effectively.

Access to technological resources and tools is also a major challenge. Adopting emerging technologies, such as VR or learning analytics, often involves purchasing expensive equipment and upgrading the technological infrastructure of educational institutions. Not all schools or universities have sufficient financial resources to implement these technologies on a large scale, which can create significant disparities in access to advanced technological education between different educational institutions. Furthermore, in some contexts, lack of connectivity or limited access to appropriate devices by students exacerbates the digital divide (Krassmann et al., 2022; Meyen, 2015).

Another obstacle is the time required to learn and master these technologies, especially for teachers who already have a heavy workload. Adopting emerging technologies involves not only acquiring technical skills but also redesigning curricula and teaching methods to harness their full potential. This requires a considerable investment of time and effort that many teachers find difficult to manage, especially in environments where change is constant (Castillo & Del Castillo, 2015).

Despite these challenges, emerging technologies also offer enormous opportunities for teachers and students. One of the most promising aspects of these tools is their ability to personalize learning. Learning analytics, for example, can provide teachers with detailed data on each student’s strengths and weaknesses, allowing them to tailor their teaching strategies in a more precise and personalized way. This improves student performance, and creates a more inclusive and equitable learning environment, in which the individual needs of all students can be met. AI, meanwhile, can automate administrative tasks such as grading or reporting, freeing up time for teachers to focus on teaching and directly supporting students. In addition, AR and VR can transform the way students experience learning, offering immersive simulations and interactive experiences that make complex concepts easier to understand and apply (Cózar-Gutiérrez et al., 2016).

The increasing use of emerging technologies in education is driving a fundamental overhaul of how curricula are designed and structured. In a traditional educational setting, curricula tend to be focused on the transmission of knowledge through more static pedagogical methods. However, emerging technologies demand a more dynamic and flexible approach that allows for continuous adaptation to the changing needs of students and the technological environment (Goyal & Gupta, 2014).

One of the main implications is the need to incorporate digital skills into the curriculum. This means teaching students how to use technological tools but also developing skills such as critical thinking about the use of technology, solving complex problems with digital tools, and understanding ethical issues related to the use of AI and other emerging technologies. Digital literacy has become an essential competency in today’s world, and teachers must be prepared to help students develop it appropriately (El-Haggar et al., 2023).

Another important implication is the potential of emerging technologies to foster more active pedagogical methodologies, such as project-based learning or collaborative learning. Tools such as AR and VR can be used to create immersive learning environments that allow students to interact more actively with content, which promotes deeper and more meaningful learning. At the same time, digital collaboration platforms facilitate teamwork and group problem solving, skills that are increasingly valued in the job market.

Learning analytics also has the potential to transform the way student progress is assessed. Rather than relying solely on standardized tests or one-off tests, analytics enables continuous and more holistic assessment, based on data collected over time on student performance in various activities. This provides teachers with a more complete view of their students’ development, and allows them to adjust their pedagogical strategies more proactively (Johnson, 2018).

The integration of emerging technologies into curriculum design also raises important questions about equity in access to education. As these tools become more common in classrooms, it is critical that all students, regardless of their socioeconomic background, have access to the same learning opportunities (González-Zamar & Abad-Segura, 2021). This involves ensuring that all students have access to the necessary devices and connectivity but also developing educational policies that promote equity in the adoption of these technologies.

2.3. Historical Evolution and Theoretical Models

The use of technologies in education has followed a path of constant evolution, marked by significant milestones that have transformed pedagogical practices and teaching models. From the appearance of the first digital tools in classrooms to the adoption of emerging technologies such as AI, VR, and learning analytics, the educational field has experienced an unprecedented transformation. Throughout this evolution, different educational theories have played a key role in the integration of technologies, providing theoretical frameworks that have guided their pedagogical application. Models such as constructivism, connectivism, and project-based learning have supported the use of these tools, highlighting the importance of active and collaborative interaction in teaching–learning processes (Andayani et al., 2023).

The evolution of technologies in education dates to the first decades of the 20th century, when basic tools such as slide projectors and radios were used to complement traditional teaching. However, the most meaningful change began in the 1970s and 1980s with the introduction of personal computers into classrooms, which enabled the implementation of interactive educational programs (Falla-Falcón et al., 2023; Johann, 2016). At that stage, computers were used to support the learning of subjects such as mathematics or programming languages, although their widespread integration was limited by economic and infrastructure factors.

The development of the Internet in the 1990s marked the beginning of a new era for education. Access to information was expanded with the emergence of search engines and online resource platforms, which made it easier to consult educational materials in a more accessible way. In this context, the first distance education platforms emerged, which laid the foundations for the subsequent growth of virtual teaching and e-learning. Although at that time digital technologies were still perceived as complementary to face-to-face teaching, the paradigm shift towards a more flexible and globalized education was already underway. At the turn of the millennium, the integration of ICT accelerated, driven by the expansion of broadband and improved technological infrastructures (Ng et al., 2023). LMSs such as Moodle and Blackboard emerged, allowing educators to manage courses digitally, facilitating the distribution of materials, the completion of online activities, and the assessment of students. These tools transformed learning environments, allowing for greater interaction between teachers and students through digital means. Today, emerging technologies such as AI, AR, blockchain, and data analytics are further revolutionizing education. AI, for example, enables the personalization of learning through algorithms that analyze student performance and adapt content and activities to their specific needs (González-Zamar et al., 2021). VR and AR offer immersive experiences that facilitate practical and visual learning in areas such as science or medicine. On the other hand, blockchain is being explored to create more transparent and secure academic certification systems, while data analytics allows teachers to obtain detailed information on student progress, improving pedagogical decision-making (Kalantzis & Cope, 2010).

The use of technologies in education has not been a merely technical process, and has been supported by pedagogical theories that explain how and why these tools can improve learning. Among the most influential theories in this field are constructivism, connectivism, and project-based learning.

Constructivism, whose theoretical roots lie in authors such as Jean Piaget and Lev Vygotsky, maintains that learning is an active process in which students construct their own knowledge through interaction with their environment (Forero-Corba & Negre Bennasar, 2023). This approach has been fundamental in the adoption of technologies that encourage student participation, such as simulators and collaborative learning platforms. AR and VR, for example, allow students to explore and manipulate virtual objects or environments, which facilitates deeper learning by involving multiple senses in the educational process. Likewise, simulation platforms offer students the opportunity to safely experience real-world situations, which reinforces the concept of active learning proposed by constructivism (Silva-Quiroz et al., 2022).

Connectivism, on the other hand, is a more recent theory proposed by George Siemens and Stephen Downes that specifically addresses learning in the digital age. According to this theory, knowledge is distributed across a network of connections, and learning occurs when individuals can navigate these networks to find and apply the appropriate information. Connectivism has been especially relevant in the context of emerging technologies, given that many of them facilitate the creation of broad, decentralized learning networks. E-learning platforms, social media, and online forums allow students to connect with other learners, experts, and resources around the world, greatly expanding learning opportunities (González-Zamar & Abad-Segura, 2020).

Furthermore, connectivism underscores the importance of students’ ability to discern between valuable and irrelevant information, a skill that has become critical in the age of data overabundance (Magaña et al., 2023). In this context, technologies such as AI can play an important role, helping students filter information and providing them with personalized recommendations based on their interests and learning needs (El-Haggar et al., 2023).

Project-based learning (PBL) is another pedagogical approach that has gained popularity with the adoption of emerging technologies. This pedagogical model, which has its roots in the work of John Dewey and William Kilpatrick, promotes active learning through the completion of projects that connect academic content to real-life situations. Emerging technologies have facilitated the implementation of PBL by providing tools that enable students to collaborate more effectively, conduct deeper research, and present their projects in innovative ways. AR and VR tools, for example, enable students to create visual prototypes of their ideas, while online collaboration platforms facilitate teamwork, even among students who are in different geographic locations (Axhami & Axhami, 2023).

As emerging technologies continue to develop, they are likely to continue to transform the educational landscape. AI is set to play a key role in personalizing learning and optimizing teaching processes. AR and VR, meanwhile, offer opportunities to create immersive learning experiences that transcend the boundaries of the physical classroom.

However, for these technologies to be effectively integrated into the educational realm, it is essential that teachers are adequately trained in their use and understand the pedagogical foundations underlying their implementation (Flores-Castañeda et al., 2024). Theories such as constructivism, connectivism, and project-based learning will remain instrumental in guiding this integration, ensuring that emerging technologies are not merely tools, but enablers of meaningful learning experiences.

2.4. Technological Pedagogical Content Knowledge

The TPACK (Technological Pedagogical Content Knowledge) model, which establishes the intersection between pedagogical knowledge, content knowledge, and technological knowledge, provides a complementary perspective to understand how pedagogy and technology interrelate in educational practice. By integrating this approach, the relevance of teachers not only possessing competences in technology and pedagogy in isolation, but also being able to effectively integrate these knowledge into their daily teaching practice, is deepened (Ciriza-Mendívil et al., 2022; Demeshkant et al., 2022).

Likewise, the TPACK model also highlights the need for educators to develop a balanced understanding of three key areas: the content they teach, the most effective pedagogical strategies for that content, and the technological tools that can facilitate that learning. Thus, in the context of emerging technologies such as AI, AR, and gamification, the TPACK framework is key to ensuring that educators use these technological tools and integrate them in ways that support and enhance pedagogical processes (Jang et al., 2021). This approach allows us to better understand how teacher training should be designed to promote pedagogically sound use of emerging technologies, ensuring that these tools align with learning objectives and student needs.

Therefore, the integration of the TPACK framework deepens the understanding of how technology and pedagogy interrelate in educational contexts, and offers a more comprehensive and multidimensional approach to teacher preparation. This framework emphasizes the importance of equipping educators with the skills necessary to effectively integrate emerging technologies into their teaching practices, ensuring that they are competent in discrete areas such as technology or pedagogy, and able to synthesize these domains to foster enhanced teaching and learning experiences (Larsen, 2023).

3. Materials and Methods

This study seeks to gather and analyze the literature on emerging technologies in education, comprising 1567 articles published between 2000 and 2024 in the Scopus database. Its primary goal is to create a valuable reference framework for researchers, educators, and policy makers, offering insights into the evolution and trends within this dynamic field. The research questions that guide this work revolve around examining the development of research on emerging technologies in education from 2000 to the present, as well as exploring the main themes and future directions for research.

To conduct this study, relevant terms were searched in the Scopus database, chosen for its extensive coverage and ease of access. The initial phase of the methodology involved a qualitative systematic review of English-language publications. This process included identifying and analyzing all pertinent studies, synthesizing information on the subject, and clarifying the methods researchers used to obtain their results. The search targeted terms related to emerging technologies in education, and the “chain of references” method was employed to gather supplementary data when obtaining a representative sample proved challenging. While including government reports could provide additional context, the focus was intentionally limited to academic publications within the Scopus database.

In the second stage of the methodology, the sample was delimited based on parameters such as the search terms, the period considered, and the type of publication to be analyzed. This stage included a bibliometric review, recognized for its effectiveness in analyzing variables of interest within the publications. This analysis allowed us to identify the relevance of the research published in a specific field, evaluated through metrics. The search included the terms “emerging technology” and “educat*”. When searching Scopus, 1567 relevant publications in the field of emerging technologies in education were retrieved.

A sample purification process was subsequently conducted to address potential errors in data recording due to variations in author names or abbreviations. To ensure accuracy, we utilized the option of downloading complete records in RIS format from Scopus. This approach streamlined the sample processing, allowing for more efficient analysis using the Science Mapping Analysis Tool (SciMAT). Additionally, the PRISMA methodology was applied to further enhance the systematic review process, ensuring a transparent and replicable approach to literature selection and data extraction. This methodical procedure strengthened the reliability and comprehensiveness of the sample included in the study (Gutiérrez-Salcedo et al., 2018; McInnes et al., 2018). This step was key to avoiding duplications and ensuring the accuracy of the data analyzed. The variables studied included the keywords used and the related thematic areas. To complement the analysis, network maps were created using the VOSviewer tool (version 1.6.20, Leiden University, Leiden, The Netherlands), whose effectiveness in mapping scientific results has been widely verified (van Eck & Waltman, 2014).

In the analysis of co-occurrences of key terms in articles on emerging technologies in education, the VOSviewer tool was used to identify current and future lines of research. This analysis is based on several metrics that allow us to understand the relationship between key concepts in the field. Link indicates the connection between terms, while total link strength reflects the intensity of these connections, denoting how many articles present two terms together. Occurrence shows the number of articles that include a specific keyword, and the network map groups these terms and their connections. In addition, clusters organize related terms, although they do not necessarily cover all elements of the map (Irwanto et al., 2023).

The relevance score is used to rank key terms in the titles and abstracts of the analyzed articles, suggesting that those with higher scores might better indicate future lines of research. This score is calculated by considering the frequency of occurrence of a term in different areas of study, allowing its importance in the field of emerging technologies in education to be predicted (van Eck & Waltman, 2010).

However, the application of this methodology presents certain limitations that could be useful for future research. First, bibliometrics tends to focus on quantitative analysis, which may omit qualitative aspects relevant in the educational context. The integration of additional methods, such as Google Scholar, meta-analysis or data mining, or using AI and machine learning, could enrich the findings.

The research sample is limited to articles published in scientific journals, which, although it guarantees a high level of credibility, restricts the diversity of sources. Including other types of documents, such as books and conference proceedings, would allow a more complete analysis of trends in this field. Finally, exploring different subfields related to emerging technologies in education could offer a more detailed perspective on specific topics, facilitating a deeper understanding of their evolution and potential impact on the future of education.

Through this methodology, we seek to provide a clear and systematic overview of the evolution of emerging technologies in education, offering researchers and stakeholders a solid basis for future research and practice in this dynamic field. The bibliometric approach, combined with the systematic review of the literature, allows us to identify current trends, but also to anticipate where research could be directed in the future, considering the impact of these technologies on educational processes. This comprehensive analysis will contribute to the creation of a reference framework that is useful for the development of educational policies and the implementation of technologies in classrooms, thus promoting the training of professionals better prepared to face the challenges of the 21st century.

One of the potential biases in our analysis is the selection of articles based exclusively on a single database, Scopus. Although Scopus is one of the most recognized and widely used databases in academia, its exclusive use may have limited the scope of our review by excluding valuable publications that could be available in other relevant databases, such as Web of Science or Google Scholar. Furthermore, our research focused solely on English-language articles, excluding important studies published in other languages, which could have introduced a bias towards the predominant English-language literature.

Another significant challenge is the temporal scope of this study. While we included articles up to the year 2024, the search in this year was not fully comprehensive, which could have influenced the accurate representation of the most recent emerging trends. This limitation could have affected the identification of technological trends or pedagogical approaches that are still in the process of development or have recently emerged.

We have also acknowledged that this study does not cover other types of documents, such as government reports or books, which could have provided additional perspectives on the integration of emerging technologies in education. This also limits the overall view of the current landscape in technology education. Despite these limitations, we believe that the findings of this study remain relevant and valuable, and provide a solid foundation for future research in the field of emerging technologies in education.

4. Results

The results section of this article examines the temporal evolution of scientific production on emerging technologies in education from 2000 to 2024, highlighting trends and patterns in this ever-changing field. A keyword analysis is carried out to identify current research lines that dominate the literature, revealing the most relevant and active areas. Furthermore, future research directions are proposed, indicating fields that need further exploration and development, offering valuable perspectives for innovation and improvement of contemporary educational practices.

4.1. Temporal Evolution of Scientific Production (2000–2024)

The analysis of the data provided on scientific publications related to emerging technologies in education between the years 2000 and 2024 reveals clear trends regarding the evolution of academic production in this field. Over this 24-year period, 1567 articles have been published, with significant growth in recent years, particularly in the 2020s. This increase can be interpreted as a reflection of the growing interest in the integration of emerging technologies into educational processes, driven by technological progress and its impact on teaching and learning.

The detailed analysis of the distribution of publications by year shows that 49.46% were published in the last four years (2021–2024). This exponential increase in publications coincides with the rapid adoption of emerging technologies such as AI, machine learning, AR, and blockchain technologies in the education sector.

In 2024, the highest number of articles was recorded in the period analyzed, with 240 publications, representing 15.32% of the total. This year is especially relevant, as it also marks the consolidation of these technologies in education, both in face-to-face and virtual contexts. Recent research is likely to focus on the impact of the use of AI tools in personalizing learning and improving accessibility for students with diverse needs (Farrell, 2022).

The year 2023 was also very productive, with 227 articles published, representing 14.49% of the cumulative total. This year continued the trends established in previous years, highlighting the use of emerging technologies to address educational challenges exacerbated by the COVID-19 pandemic, such as the implementation of hybrid learning tools, the use of online education platforms enhanced with VR technologies, and the gamification of learning. It is important to highlight the change that occurred in 2020, when 122 articles were published (7.79% of the total), followed by 143 articles in 2021 (9.13%). These years are critical due to the impact of the COVID-19 pandemic, which forced many educational institutions to quickly adopt technological solutions to ensure the continuity of teaching (Emelogu et al., 2022). Distance learning and virtual educational platforms experienced an unprecedented increase in their implementation, which also drove a greater volume of research on the effectiveness of these technologies and the new methodologies that emerged because of the health crisis (Martínez-Pérez et al., 2022).

As of 2019, with ninety-nine articles (6.32%), an upward trend in interest in emerging technologies was already beginning to emerge. This period coincides with the increased availability of technologies such as AI and big data in the education sector. Research during this time focused on how these technologies could personalize learning for students and optimize decision-making for teachers and educational administrators.

Between 2015 and 2018, a stable but moderate growth in the number of publications is observed. In 2018, 55 articles (3.51%) were published, while in 2017, there were 62 (3.96%) and in 2016, 51 (3.25%). These years saw a focus on technologies such as mobile-based education (m-learning) and the use of cloud-based educational applications. It is also during this period that learning analytics tools began to gain popularity, providing researchers with new ways to measure the impact of emerging technologies on academic performance and the learning experience (Bothe, 2023).

Before 2015, the production of scientific publications was lower. In 2014 and 2013, 51 and 39 articles were published, respectively, with percentages of 3.25% and 2.49%. This period is characterized by research focused on online learning technologies and the use of LMSs. In addition, research on the use of social networks and virtual environments to foster collaborative learning began to take shape, although with a still limited adoption compared to what would come later (Omran Zailuddin et al., 2024).

During the first decade of the 21st century (see Figure 1), the number of publications related to emerging technologies in education was quite low compared to more recent years. In 2000, only eleven articles were published (0.70%), and growth was slow during the first years, reaching twenty articles in 2002 (1.28%) and remaining at low figures until 2010, when 36 articles were published (2.30%).

This first stage of the analysis coincides with the first attempts to introduce ICT in educational environments. Research during these years focused on the use of computers and educational software, as well as on the integration of the Internet in classrooms as a tool to access educational resources. However, more advanced technologies, such as those we now consider emerging, were not yet part of the main discussion at that time.

4.2. Keyword Analysis: Identification of Current Lines of Research

This co-occurrence analysis allowed us to recognize the lines of research during the period 2020–2024, developed by the main driving agents of this field of study. Five clusters were identified through the VOSviewer 1.6.20 software, each represented by a distinctive thematic focus. “Learning” reflects a focus on diverse pedagogical methods and technologies that enhance educational experiences; “Artificial Intelligence” highlights the significant interest in the integration of emerging technologies in different sectors, including education and commerce; “Teaching” focuses on innovative educational practices and the role of technology in shaping teaching; this cluster underlines the importance of methods that encourage active student participation; “Virtual Reality” shows the exploration of immersive technologies in educational and training environments, highlighting their potential to enrich the learning experience; and “3D Printing” focuses on the use of three-dimensional technologies; this cluster illustrates the growing importance of these tools in fields such as health and engineering. Each cluster reveals the thematic concentration of research efforts, and points out emerging trends and the evolution of knowledge in these domains. The tables below provide detailed information on the connections and impact within the respective research areas.

Table 1. Top 30 keywords (2000–2024).

R	Keyword	N	L	TLS	Cluster	R	Keyword	N	L	TLS	Cluster
1	artificial intelligence	147	143	380	2	16	deep learning	29	71	121	2
2	teaching	132	165	473	3	17	blockchain	28	48	82	2
3	virtual reality	131	160	427	4	18	training	27	51	81	4
4	higher education	118	111	273	4	19	big data	27	48	94	2
5	e-learning	108	145	405	3	20	sustainable development	26	53	74	2
6	augmented reality	105	125	273	4	21	ethics	25	44	59	1
7	learning	105	137	381	1	22	distance education	25	43	61	1
8	educational technology	61	100	208	4	23	information technology	24	61	72	1
9	emerging technology	58	99	153	3	24	metaverse	23	40	73	4
10	learning systems	57	99	240	2	25	perception	23	55	72	1
11	education computing	43	76	164	3	26	social media	23	52	78	1
12	online learning	40	54	103	3	27	educational innovation	22	37	66	4
13	decision making	36	91	139	2	28	chatgpt	22	26	51	3
14	internet of things	36	64	141	2	29	motivation	22	51	73	1
15	innovation	35	60	83	4	30	communication	22	39	60	1

R: Rank, N: Occurrences, L: Links, TLS: Total link strength.

shows the top thirty keywords, according to the number of occurrences. The interaction between the clusters suggests that education is in a process of transformation driven by emerging technologies. There is a growing tendency to integrate ethics and sustainability into the discussion on the use of technology in education. The lines of research are constantly evolving, reflecting a response to the changing needs of the educational and professional environment. This analysis highlights the diversity of approaches and the interconnection between different areas of study, which can contribute to a deeper understanding of current trends in educational and technological research.

A co-occurrence analysis of the keywords that identify the sample articles on the study topic was performed. Figure 2 shows the network visualization of these keywords. From the analysis, it was possible to identify that the keywords were classified into five clusters with homogeneous characteristics.

Cluster 1 (pink), characterized by a focus on education and technology, encompasses a wide range of interrelated concepts that reflect the current dynamics of learning and teaching. This cluster includes terms such as “learning”, “distance education”, “information technology”, and “professional development”, indicating an emphasis on how technology transforms traditional education. The inclusion of words such as “motivation”, “perception”, and “communication” suggests an interest in the psychological and social aspects of learning, highlighting the importance of interaction and collaboration in educational settings.

In addition, terms such as “simulation”, “assessment”, and “educational measurement” point to innovative methods for evaluating learning and the effectiveness of educational programs. The presence of words related to “assistive technology”, “user-computer interaction”, and “privacy” reflects the need to consider ethical and practical issues in the design of educational tools. Taken together, the cluster shows how contemporary education faces challenges and opportunities through the integration of technology and pedagogy.

Cluster 2 (green) focuses on the intersection between advanced technology and education, highlighting the critical role that emerging technologies play in transforming learning systems and decision-making. Keywords such as “artificial intelligence”, “machine learning”, and “decision systems” suggest an emphasis on how these advances can optimize educational and training processes. Furthermore, the inclusion of terms such as “big data”, “data analytics”, and “data mining” indicates a focus on collecting and using large volumes of information to improve the personalization and effectiveness of learning.

This cluster also addresses concepts of “sustainable development”, “digital transformation”, and “industry 4.0 technologies”, highlighting the importance of aligning education with current trends in the labor market and technological innovation. Terms such as “cloud technology”, “5G mobile communication”, and “smart cities” reflect the need to prepare students for a technology-driven future. Overall, the cluster highlights the increasing integration of disruptive technologies in education, as well as their implications for efficiency and sustainability.

Cluster 3 (red) focuses on the intersection of education and technology, highlighting contemporary teaching methodologies and the impact of emerging technologies on learning. Keywords such as “teaching”, “e-learning”, and “online learning” reflect a focus on digital education and the various platforms that facilitate distance learning. The inclusion of terms such as “gamification”, “game-based learning”, and “active learning” suggests a trend toward interactive pedagogical methods that foster student participation and engagement.

The cluster also emphasizes the role of emerging technologies, such as “chatgpt”, “artificial intelligence”, and “mobile technology”, in education, highlighting their potential to personalize the learning experience and enhance teaching. Concepts such as “competence”, “critical thinking”, and “collaborative learning” point to the need to prepare students for a dynamic and evolving educational environment, emphasizing key skills for the 21st century.

Additionally, the cluster includes references to specific educational levels, such as “k-12 education” and “higher education institutions”, indicating a broad application of these strategies and technologies in different educational contexts. The cluster highlights how the integration of advanced technologies in education can enhance learning experiences and prepare students for future challenges.

Cluster 4 (yellow) focuses on VR and other immersive technologies, exploring their application in education and the development of innovative learning environments. Terms such as “virtual reality”, “augmented reality”, and “mixed reality” highlight the growing relevance of these tools in higher and secondary education, promoting more interactive and immersive learning experiences.

This cluster also addresses the concept of the “metaverse” and its potential to transform education through collaborative and dynamic digital environments. The inclusion of words such as “educational innovation”, “experiential learning”, and “blended learning” suggests an interest in methodologies that combine traditional and digital approaches, optimizing student learning.

Reference to concepts such as “learning analytics”, “assessment”, and “feedback” implies a focus on the continuous evaluation and improvement of educational processes, using data to personalize the learning experience. In addition, the importance of “digital competence” and “digital literacy”, essential in an increasingly technology-mediated world, is underlined.

Cluster 5 (cyan) focuses on 3D printing and its application in various areas, especially in the field of health and medicine. Terms such as “3D printing”, “additive manufacturing”, and “three-dimensional imaging” reflect the evolution and impact of these technologies in the creation of three-dimensional objects through successive layers of material.

The inclusion of concepts such as “minimally invasive surgery” and “interventional radiology” highlights how 3D printing is used to improve surgical procedures, allowing doctors to create personalized models that facilitate the planning and execution of interventions. This implies a significant advance in the personalization of treatment and the improvement of outcomes for patients.

Additionally, the cluster mentions “user interfaces” and “workflow”, indicating a focus on user interaction with 3D printing technologies and optimizing work processes in clinical and research settings. “Image processing” is also considered for the handling and analysis of three-dimensional images, essential in the production of accurate models.

Finally, the concept of “informed consent” suggests an interest in ethics and communication with patients, ensuring that they are fully informed about the technologies and procedures in which 3D printed models are used. This cluster highlights the intersection between 3D printing, healthcare, and technological innovation, offering an overview of how these tools are transforming medical practice and patient care.

4.3. Future Directions of Research

This section aims to answer the following question: what are the emerging research directions in this topic? After reviewing the literature and performing an analysis of the keywords that allowed identifying current trends, the main future and emerging directions of this field of research were identified. International research on secure accounting management through emerging technologies evolves by incorporating new concepts and approaches that establish new lines of inquiry. The latest terms associated with this research were identified, allowing them to be related to emerging research directions.

Cluster analysis is an effective process for discovering emerging trends and problems in a scientific discipline. This process consists of (i) grouping the units of analysis into groups of similar elements; (ii) determining the most recent terms from the relevance score; and (iii) the identified terms can be assimilated to emerging thematic lines in this field of research.

Table 2. Top 5 future research directions ranked by relevance score.

Rank	Future Direction of Research	Relevance Score
1	Compound Intelligent Navigation Talent	19.567
2	Multi Modal Visual Features Perception Technology	15.761
3	Metaverse-Integrated Learning Environment	15.437
4	Virtual Augmented Reality	14.202
5	Digital Learning Environments	13.086

shows the future research directions detected by the relevance score, whose detailed description is provided below.

4.3.1. Compound Intelligent Navigation Talent

The integration of multiple emerging technologies into educational environments allows students to navigate more efficiently and in a personalized way through their learning experiences. This approach combines AI, data analytics, and adaptive teaching methodologies to deliver a more student-centered educational experience. Through systems that monitor progress, analyze behaviors, and provide real-time feedback, it is easier to identify resources and activities that best align with each student’s individual needs and interests.

As a line of future research, this approach presents multiple areas of exploration. One of the most relevant is the development of algorithms that optimize learning personalization, allowing systems to adapt to different learning styles and paces. It is also essential to investigate how these technologies can be integrated into existing educational platforms, thus improving the usability and effectiveness of such platforms (Al-Ali et al., 2024).

Furthermore, the impact of these tools on student engagement and motivation must be evaluated, as well as their effectiveness in improving academic outcomes. Another area of interest is training educators to effectively use these technologies in the classroom, ensuring they can maximize their potential (Thiriet et al., 2002). In this context, exploring the interaction between students and intelligent navigation systems becomes fundamental to understanding how to optimize learning in an increasingly digital educational environment.

4.3.2. Multi Modal Visual Features Perception Technology

Technology that combines multiple sensory and visual modalities aims to enrich the learning experience by integrating different forms of visual information, such as images, videos, graphics, and even VR and AR. This approach allows students to interact more deeply with educational content, as they receive visual information, and perceive it through various perspectives and formats, facilitating a richer and more complex understanding.

Through the perception of visual features from multiple modalities, this technology allows learning to be more immersive and multisensory. Students can explore concepts from different dimensions, which improves information retention, and supports the development of cognitive skills such as analysis, visual interpretation, and problem solving. By integrating various sources of stimuli, this technology reinforces learning in both theoretical and practical contexts (Craig, 2021).

In the educational context, this technology is used to address different learning styles, allowing students to view and perceive the same content from different angles or sensory modalities. For example, a biology lesson that uses 3D images alongside interactive animations and immersive videos allows students to pick up on details that might otherwise go unnoticed, and which are further reinforced by the simultaneous use of a variety of visual and sensory stimuli (Abad-Segura et al., 2020).

This technology focuses on visualization, and on students’ processing capacity, empowering an educational experience that better responds to individual cognitive needs. As emerging technologies such as AI, machine learning, and AR become more integrated into learning environments, the use of multi modal visual perception technology will transform the way educators present information and students assimilate it.

4.3.3. Metaverse-Integrated Learning Environment

An educational environment that integrates virtual, augmented, and physical spaces uses emerging technologies such as VR, AR, and AI to create immersive and interactive learning experiences. These environments allow students to interact with content in three dimensions, collaborate in real time, and experience hands-on learning situations.

This transformation in education stems from the need to leverage the advantages of digital technologies. In a virtual classroom, students can explore scientific concepts in 3D, collaborate on projects, and receive instant feedback from virtual tutors. Key features include immersion, which allows students to immerse themselves in virtual worlds; interactivity, which encourages collaborative learning; personalization, which tailors the experience to individual needs; gamification, which increases motivation; and accessibility, which facilitates distance learning (J. E. M. Díaz et al., 2020; Hwang & Chien, 2022).

Research in this area presents several opportunities. The impact of immersive learning on information retention and conceptual understanding can be studied compared to traditional methods. Strategies can also be developed to create content tailored to these environments and assess acquired skills through advanced analytics.

Furthermore, it is important to investigate how collaboration in virtual environments influences interpersonal skills, and to ensure that these spaces are accessible and inclusive. Finally, the effects of gamification on student engagement and the effectiveness of virtual tutors in personalizing learning can be explored. This line of research has the potential to transform education and improve the learning experience globally (George-Reyes et al., 2023).

4.3.4. Virtual Augmented Reality

Augmented VR combines elements of VR and AR to create immersive experiences that enrich interaction and perception in the educational field. This technology allows students to interact with digital objects superimposed on the real world through AR or to fully immerse themselves in three-dimensional environments through VR. The fusion of both modalities offers a wider variety of learning experiences, as students can manipulate digital objects while remaining in their physical surroundings or explore virtual environments that simulate real situations, such as exploring the solar system or performing medical practices (Hajirasouli et al., 2024).

Research in augmented VR as a future line can address several aspects. One area of interest is learning effectiveness, investigating how these technologies impact information retention and understanding of concepts compared to traditional methods. It is also key to develop effective strategies to design educational experiences in VR and AR, including the creation of interactive three-dimensional models.

Another point to explore is accessibility, ensuring that these technologies are inclusive for all students. Likewise, the interaction between humans and machines in virtual environments must be studied, as well as applications in various disciplines, such as science, mathematics, and history. Finally, examining gamification and its effect on student engagement will be essential. Hence, augmented virtual reality has the potential to transform learning, offering more interactive and personalized experiences that redefine teaching in various areas of knowledge (Delello et al., 2015).

4.3.5. Digital Learning Environments

Digital learning environments refer to educational spaces that integrate digital technologies to facilitate the teaching–learning process. These environments may include online platforms, collaboration tools, multimedia resources, and interactive applications, all designed to enhance educational experience. Through these environments, students can access a variety of educational materials, interact with their peers and teachers, and participate in learning activities tailored to their individual needs. The flexibility and accessibility of digital learning environments allow education to adapt to different learning styles and paces, fostering more personalized and autonomous learning (Griesel & Price, 2017).

As a future line of research, digital learning environments offer assorted opportunities. One of them is the analysis of their effectiveness in improving academic outcomes compared to traditional teaching methods. Research can be performed on how these platforms affect students’ motivation, engagement, and knowledge retention. Furthermore, the development of strategies for the creation of interactive and multimedia educational content is key to maximize the potential of these environments (Dallis, 2016).

It is also essential to explore issues related to accessibility and equity in digital education, ensuring that all students have access to the necessary technologies. Likewise, the dynamics of online interaction and collaboration can be studied, as well as the impact of AI on the personalization of teaching. Thus, digital learning environments have the potential to transform education and represent a promising field of research that can improve educational practices in the future.

5. Discussion

The integration of emerging technologies in education is not only a recent trend but also reflects an evolution in educational theories that have been discussed and explored by various contemporary authors. As we analyze the impact of these technologies, it is essential to link our findings with the ideas and proposals of current researchers in the field of education.

The COVID-19 pandemic has caused a drastic change in education, a phenomenon that has been the subject of study by several authors. For example, García and Rojas (2021, as cited in Toquero & Talidong, 2021) highlight that the health crisis forced educators to adapt their teaching methods quickly and effectively. This change has demonstrated the need for a more flexible and adaptive educational approach. The rapid response to this crisis illustrates the theories of connectivism, defended by authors such as Siemens (2005, as cited in Toquero & Talidong, 2021), which emphasizes the importance of networks and connections in modern learning. According to Siemens, learning occurs in a context of uncertainty, where knowledge is constantly evolving. This is particularly relevant in an educational environment that now includes digital resources such as online learning, where educators and students must navigate a sea of changing information (Toquero & Talidong, 2021).

AI and big data are transforming the way learning is understood. Authors such as Kukulska-Hulme (2020) underline the importance of learning analytics to personalize education. By analyzing large volumes of data on student behavior and performance, educators can adapt their teaching strategies and methods. This perspective aligns with data-driven pedagogy, promoted by researchers such as Hattie (2012, as cited in Dawson & Hubball, 2014), who argues that data-informed educational decisions can lead to significant improvements in student performance. However, Hattie also warns about the need to use these data ethically and responsibly, highlighting that transparency in the use of personal information is essential to maintain the trust of students and their families (Dawson & Hubball, 2014).

VR and gamification are technologies that are revolutionizing experiential learning. Gee (2003, as cited in Escolano & Doldán, 2021) argues that video games and gamification can be powerful allies in learning, as they foster engagement and motivation. According to Gee, games offer narrative-rich contexts and allow players to experience complex situations that could not be easily simulated in a traditional classroom (Tang et al., 2020). This is especially pertinent in the context of contemporary education, where educators are looking for ways to engage students more effectively. On the other hand, Dede (2009, as cited in Escolano & Doldán, 2021) argues that VR can transform the way students interact with content, allowing them to experience real-world situations in a controlled environment. By using these technologies, educators can provide learning experiences that are both practical and theoretical, contributing to a deeper and more meaningful understanding of the concepts addressed in class (Escolano & Doldán, 2021).

Inclusion and equity are essential elements in modern education. Authors such as Slee (2011) advocate for an inclusive approach that not only considers the integration of students with disabilities but also values cultural and socioeconomic diversity in the classroom. Slee highlights the importance of an approach that recognizes and celebrates diversity in learning. In this regard, emerging technologies can play a key role by providing resources that adapt to the diverse needs of students. For example, Rose and Meyer (2002), in their work on Universal Design for Learning (UDL), emphasize the need to create learning environments that are accessible and that offer multiple forms of representation and expression. The personalization of learning, facilitated by technology, aligns with these principles and allows all students to fully participate in their education (Roig-Vila et al., 2019).

Ethics in education is a recurring theme in the work of contemporary authors. Holland and Rees (2020) argue that the implementation of technologies in education should be guided by ethical principles that ensure the privacy and security of student data. These authors highlight the importance of establishing clear guidelines on how data are collected and used in the educational context, promoting responsible use of technology that respects students’ rights (Calvo-Sotelo, 2008).

Furthermore, education for sustainable development has become a fundamental approach in contemporary educational discourse. Researchers such as Wals (2011) emphasize the need to integrate sustainability into the educational curriculum. Education should not only prepare students for the present but also empower them to address future challenges related to sustainability. This implies an interdisciplinary and collaborative approach that addresses education from multiple perspectives.

The evolution of educational research requires methodologies that reflect the complexity of contemporary learning. Design-based research, advocated by Cobb et al. (2003), provides a framework for researching educational practice through collaboration between researchers and educators. This approach allows for iteration and continuous improvement of educational practices, aligning with the constant change in the use of emerging technologies (N. S. Díaz et al., 2018).

Qualitative approaches, such as case studies and educational ethnography, are also essential to understanding how learning is experienced in technological contexts. Authors such as Merriam and Tisdell (2016) argue that qualitative research allows for capturing the complexities and nuances of learning in digital environments, providing a richer and more contextualized view of the student experience.

As we look to the future, it is vital that educational institutions continue to explore and adapt to emerging technologies. Authors such as Richardson and Mancabelli (2011) emphasize that the future of education must be framed in connected learning, where educational institutions foster the use of digital technologies to create more effective learning networks (Rodríguez et al., 2022). This connected learning allows students to access a variety of resources and perspectives, fostering more inclusive and collaborative learning.

Furthermore, emotional intelligence and socio-emotional skills are gaining ground in the discussion about the future of education. Researchers such as Goleman (1995, as cited in Ota et al., 1995) have shown that emotional intelligence is key for personal and academic success. Integrating these skills into the educational curriculum, through emerging technologies, will allow students to develop competencies necessary for the 21st century (Ota et al., 1995).

Collaboration between different actors in the educational field is essential for the future of education. Brusilovsky and Millán (2007) highlight that collaboration between educators, students, and communities can enrich learning and foster educational innovation. Professional learning communities can be valuable spaces where educators share experiences and develop new practices based on critical reflection (Demchenko, 2001).

In the digital field, Siemens (2013) underlines the importance of building personal learning networks, where educators and students can connect and collaborate in a global environment. This collaboration not only improves educational experience, but also allows educators to learn from best practices in different contexts.

The evolution of emerging technologies in education is an ever-changing phenomenon that offers both opportunities and challenges. As we move into the future, it is critical for the educational community to reflect on how these technologies can be used responsibly and effectively to enrich learning.

The integration of emerging technologies in education should not be an end, but rather a means to achieve meaningful and transformative learning (Tlili et al., 2021). By linking our observations with the ideas of current authors, it becomes clear that the education of the future must be anchored in ethical and sustainable principles, ensuring that all students have the opportunity to learn and grow in an environment that fosters equity and inclusion (Fernández-Batanero et al., 2022).

Through a holistic and multidisciplinary approach, education can leverage emerging technologies to prepare students for the challenges of the future, fostering critical and creative skills that allow them to navigate an increasingly complex and dynamic world. Collaboration, ongoing training, and critical reflection will be fundamental in this process, ensuring that education remains a driver of social change and a space for personal and professional growth.

The results obtained in this study show a growing interest in the integration of emerging technologies, such as AI and AR, in the educational field. This trend is consistent with previous studies that point out that these technologies can have a significant impact on personalizing learning and improving the educational experience. However, despite this enthusiasm, the findings also reveal several concerns about their effective implementation.

First, although AI and AR have shown considerable potential in terms of personalizing and motivating students, our findings coincide with previous research that warns about the lack of adequate preparation of teachers to effectively integrate these technologies into the classroom. This disparity between the potential of technology and the reality of its implementation underlines the urgent need for a more structured approach in teacher training. Teachers should be trained in how to use these tools in a pedagogically effective way and how to adapt them to the specific educational context (Cain, 2023; Cardin & Wolowski, 2021).

Furthermore, when comparing our findings with the existing literature, we found that while the integration of emerging technologies offers opportunities for personalization and enhancement of collaborative learning, it also presents challenges related to equity in access to these technologies. Previous studies have indicated that unequal access to technological infrastructure remains a significant barrier, especially in resource-limited educational contexts (Cain, 2023; Luttrell et al., 2020). This lack of access may exacerbate existing learning gaps and limit the benefits of these technologies in disadvantaged educational settings.

On the other hand, regarding ethical concerns, such as data privacy and surveillance through technologies such as AI, our literature review suggests that these issues have been insufficiently addressed in the reviewed studies. While some researchers have begun to address these issues, most studies focus primarily on the benefits of emerging technologies, without paying sufficient attention to the potential risks they may entail in terms of protecting students’ privacy and security (Carceller, 2024; Ma et al., 2024). It is important to note that while our review has focused primarily on articles published in Scopus, this may have limited the inclusion of relevant studies in other languages or with lower visibility. The exclusivity of this database could introduce bias into the results, and future research should broaden its scope to other sources and types of publications, such as government reports or books, to gain a more comprehensive view of the trends and challenges associated with emerging technologies in education (Ghnemat et al., 2022; Lee, 2024).

6. Conclusions

Data analysis reveals an accelerated growth in research on emerging technologies in education, especially in recent years. This increase has been driven by the need to adapt educational methodologies to new technological realities and by the challenges imposed by the COVID-19 pandemic. As educators faced the transition to online and hybrid teaching, the search for new tools and approaches became a priority, leading to significant interest in incorporating emerging technologies.

Historically, educational research focused on the use of ICT. However, attention has evolved towards the adoption of more advanced technologies such as AI, AR, and adaptive learning. These tools not only facilitate teaching but also allow for personalizing learning and offering more immersive and engaging experiences. For example, AR has proven effective in enriching hands-on and visual learning, while AI helps teachers analyze student performance and adjust their teaching based on their individual needs.

This trend is expected to continue, given the rapid development of emerging technologies and their potential to revolutionize education. The integration of these technologies in the classroom not only creates opportunities to improve student engagement and motivation, but also poses challenges in terms of teacher training, accessibility, and equity. It is therefore key for educational institutions to prepare to address these challenges and take advantage of the opportunities offered by emerging technologies.

Future research in this area will focus on several key aspects. First, the impact of these technologies on the teaching–learning process will be explored, analyzing how they affect student academic performance, participation, and satisfaction. It is critical to understand how these tools can be effectively used to improve educational outcomes and foster more meaningful learning.

Furthermore, it will be essential to investigate how emerging technologies can be implemented in an inclusive and equitable manner in different educational contexts. Equity in access to technology and training is a critical aspect that must be addressed to ensure that all students, regardless of their socioeconomic background, can benefit from the opportunities that these tools offer. Including all students in the educational process is a fundamental principle that must guide the adoption of emerging technologies.

The need for educators to receive adequate training to integrate these technologies into their pedagogical practices is another key point. Continuing training and professional development are essential for teachers to not only become familiar with new tools but also learn to use them effectively in their teaching. Communities of practice and collaborative learning among educators can be effective strategies to facilitate this process and foster an environment of innovation.

Likewise, collaboration between educational institutions, technology companies, and government agencies will be essential to promote the development and implementation of emerging technologies in education. By joining forces, these actors can create an ecosystem that favors the research, development, and adoption of technological solutions that benefit all students and educators.

In this context, reflection on the future of learning and teaching becomes vital. Assessment methods are also expected to transform to adapt to these emerging technologies, allowing educators to gain a deeper understanding of student progress. The use of data and advanced analytics can enable the personalization of learning and the creation of individualized educational paths, which align with the needs and abilities of each student.

With the increasing integration of emerging technologies, educators are now facing ethical challenges regarding student data privacy and security. The collection and analysis of performance data can enhance personalized learning, but it also raises concerns about data misuse and unauthorized access. It is therefore essential to develop ethical frameworks and regulations that ensure transparency, informed consent, and the protection of students’ rights.

By implementing robust security measures, training educators in ethical digital practices, and fostering collaboration among schools, policy makers, and technology experts, we can strike a balance between leveraging data for educational improvements and safeguarding individual privacy.

Some practical applications of this work for educators involve creating learning environments that are not only more personalized but also more immersive. By leveraging emerging technologies such as virtual reality, augmented reality, and adaptive learning systems, teachers can tailor lessons to individual student needs and learning styles. This approach not only boosts student engagement but also helps in addressing diverse educational requirements in real time. To fully benefit from these innovations, educators must engage in continuous professional development that combines technical and pedagogical skills. Additionally, they must address ethical challenges, particularly those concerning the privacy and security of student data.

For policy makers, supporting ongoing teacher training and fostering collaboration among educational institutions, technology developers, and government agencies are essential for accelerating the adoption of innovative educational tools. This coordinated approach not only equips educators with the skills needed to integrate new technologies effectively into their classrooms but also creates a dynamic ecosystem where best practices and insights are continuously shared. Moreover, such partnerships can ensure that technological solutions are closely aligned with real classroom needs, while government support can streamline regulatory processes and funding, ultimately leading to a more efficient and impactful transformation of educational practices. Also, ensuring equitable access to technology is essential to prevent disparities in education, and policy makers need to establish ethical guidelines and develop adaptive strategies to integrate technologies sustainably, ensuring education systems are prepared for future advancements.

The importance of continuous and adaptive training for educators is increasingly evident. Training in the use of emerging technologies must go beyond the technical and include a pedagogical component that allows teachers to understand how these tools can transform their practice. The effective integration of these technologies in teaching requires not only technical skills, but also a change in educational mindset and philosophy.

The educational transformation driven by emerging technologies presents an unprecedented opportunity to create learning environments that are both inclusive and interactive, perfectly tailored to the demands of 21st-century students. These technologies enable educators to design curricula that engage diverse learning styles and promote collaboration, thereby breaking down traditional barriers and fostering a more dynamic, student-centered approach to education.

By proactively addressing the challenges and leveraging the benefits offered by these advancements, educational institutions can equip students with the critical skills needed not only to excel academically but also to navigate future personal and professional landscapes. This holistic approach to learning ensures that students are prepared to tackle complex real-world issues with creativity, adaptability, and resilience.