essay on role of computer in education

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The Role of the Computer in Education

1. introduction.

The computer has become an integral part of education in the modern world, revolutionizing the way students learn and teachers instruct. This introduction will provide an overview of the role of the computer in education, highlighting its significance in enhancing the learning experience and addressing the challenges and opportunities it presents. The evolution of technology has propelled the integration of computers in educational settings, enabling personalized learning, access to vast resources, and the development of essential digital skills. As we delve into the following sections of this work, we will examine the impact of computer technology on education, the benefits and potential drawbacks, and the strategies for effective implementation. By understanding the crucial role of the computer in education, we can navigate the complexities and harness its potential to shape the future of learning and teaching.

2. Historical Development of Computers in Education

The integration of computers into education has a rich historical development that spans several decades. The use of computers in education initially began with the development of educational software in the 1960s. At this time, computers were large and expensive, and their use in education was limited to a few research institutions and universities. However, as technology advanced, computers became more accessible, leading to the widespread adoption of personal computers in the 1980s. This shift opened up new possibilities for integrating technology into educational settings, allowing educators to explore interactive ways of teaching and learning. The 1990s saw a significant expansion of computer use in education, with the emergence of the internet and multimedia technologies. This era marked a turning point in how computers were utilized in the classroom, as they became valuable tools for research, communication, and collaboration. Additionally, the development of learning management systems and online education further transformed the landscape of education, making it possible for students to engage in distance learning and access educational resources from anywhere with an internet connection. In recent years, the rise of mobile technologies and digital devices has continued to shape the role of computers in education, with a focus on personalized learning experiences and the integration of technology across all academic disciplines. As the historical development of computers in education continues to evolve, it is clear that technology will play an increasingly integral role in shaping the future of education.

3. Current Applications of Computers in Education

Section 3: Current Applications of Computers in Education One of the most significant developments in education in recent years has been the widespread use of computers and technology in the classroom. This has led to a number of current applications of computers in education that have transformed the way students learn and teachers teach. Firstly, online learning platforms have revolutionized the education landscape by providing students with access to a wide range of courses and educational resources. These platforms allow for flexible and self-paced learning, making education more accessible to students from diverse backgrounds. Additionally, educational software has become an integral part of the modern classroom, providing interactive and engaging learning experiences for students. From language learning programs to math and science simulations, educational software offers a diverse array of tools to support teaching and learning. Furthermore, the integration of virtual reality in education has opened up new possibilities for immersive and experiential learning. Virtual reality tools allow students to explore historical landmarks, conduct virtual science experiments, and even visit other countries without leaving the classroom. This technology has the potential to significantly enhance student engagement and understanding of complex concepts. In summary, the current applications of computers in education have profoundly impacted the way students learn and teachers instruct. From online learning platforms to educational software and virtual reality tools, these applications have the potential to create more inclusive, interactive, and effective learning environments for students of all ages.

3.1. 1.1 Online Learning Platforms

Online learning platforms have become an integral part of education, offering a wide range of courses and resources for learners of all ages and backgrounds. These platforms provide access to a wealth of educational materials, including lectures, videos, quizzes, and discussion forums, allowing students to learn at their own pace and convenience. Moreover, online learning platforms offer a flexible and cost-effective way to acquire new skills and knowledge, making education more accessible to a global audience. One of the key advantages of online learning platforms is their ability to personalize the learning experience, allowing students to tailor their studies to their individual needs and interests. Additionally, these platforms often incorporate interactive and multimedia elements, providing a more engaging and dynamic learning environment compared to traditional classroom settings. Furthermore, online learning platforms enable collaboration and networking among students and educators from around the world, fostering a sense of community and diversity in the learning process. In summary, online learning platforms have revolutionized the way we approach education, offering a flexible, personalized, and interactive learning experience for students worldwide. Through these platforms, learners have access to a wealth of resources and opportunities to develop their skills and knowledge, contributing to the democratization of education and the advancement of global learning.

3.2. 1.2 Educational Software

Educational software is a fundamental tool in modern education, providing a wide range of interactive and engaging resources for both students and teachers. This software encompasses various programs and applications designed to enhance the learning experience, covering subjects such as math, science, languages, and more. One of the key benefits of educational software is its ability to personalize the learning process, allowing students to progress at their own pace and focus on areas where they need additional support. Additionally, educational software often incorporates gamification elements, making the learning process enjoyable and motivating for students. Furthermore, educational software can also facilitate communication and collaboration among students and educators, creating a dynamic and interactive learning environment. Teachers can use these tools to create and deliver engaging lessons, track student progress, and provide personalized feedback. Moreover, educational software opens up new possibilities for distance learning, making education more accessible to students regardless of their geographical location. As technology continues to advance, the potential for educational software to transform the way we teach and learn is immense, paving the way for a more innovative and inclusive education system.

3.3. 1.3 Virtual Reality in Education

Virtual reality (VR) has emerged as an innovative and impactful tool in the field of education. This technology creates immersive, simulated environments that allow students to engage with educational content in ways that were previously impossible. VR offers students the opportunity to explore historical landmarks, conduct scientific experiments, and interact with complex mathematical concepts in a realistic and interactive manner. By using VR in education, students can experience hands-on learning that enriches their understanding of various subjects. This technology also caters to different learning styles, making it an inclusive tool for a diverse range of students. Furthermore, VR in education has the potential to enhance student engagement and motivation. The immersive nature of VR captivates students' attention and makes the learning process more memorable. It also provides a safe space for students to make mistakes and learn from them without real-world consequences. Additionally, VR can bridge the gap between theoretical knowledge and practical application, allowing students to apply their learning in simulated environments. As technology continues to advance, VR holds promise for transforming the educational landscape and creating new possibilities for teaching and learning. In conclusion, the integration of virtual reality in education represents a significant advancement that has the potential to revolutionize the way students engage with and comprehend academic material.

4. Benefits and Challenges of Using Computers in Education

The integration of computers in education offers numerous advantages, including enhanced engagement, personalized learning experiences, access to vast resources, and the development of essential technology skills. Students can benefit from interactive learning activities, simulations, and multimedia materials that cater to different learning styles and promote active participation. Additionally, computer-assisted instruction allows for individualized learning paths, accommodating diverse student needs and pacing. Moreover, computers provide access to digital libraries, educational websites, and online databases, expanding the range of information and learning opportunities available to students and educators. By using computers, students can also acquire essential digital literacy and technological competencies crucial for success in the 21st century. However, the use of computers in education also presents challenges and limitations that need to be addressed. One of the main challenges is the digital divide, as not all students have equal access to technology and reliable internet connections, resulting in disparities in educational opportunities. Moreover, there are concerns about the potential for technology to be a distraction, leading to decreased attention spans and reduced critical thinking skills. Additionally, the reliance on computers can lead to a lack of personal interaction and communication skills, as well as potential issues with overreliance on technology. It is important for educators to navigate these challenges and limitations effectively in order to harness the full potential of computers in education.

4.1. 2.1 Advantages of Computer Integration

The integration of computers in education brings about a multitude of advantages that contribute to the enhancement of the learning experience. One of the primary benefits is the accessibility to vast amounts of information and resources that computers provide. With access to the internet, students can delve into a wealth of knowledge beyond the confines of traditional textbooks, thus facilitating a more comprehensive understanding of various subjects. Additionally, computer integration promotes greater engagement and interactivity in the learning process. Interactive educational software and multimedia platforms enable students to actively participate in their learning, fostering a more dynamic and stimulating educational environment. Moreover, computers allow for personalized learning experiences, as they can adapt to individual student needs and provide targeted, tailored educational content. This individualized approach can help students learn at their own pace and address their specific learning requirements, ultimately leading to improved academic performance. Finally, the integration of computers in education also cultivates essential digital literacy skills, equipping students with the technical proficiency necessary for success in the modern, technology-driven world. Overall, the advantages of computer integration in education contribute to a more enriched and effective learning experience for students.

4.2. 2.2 Challenges and Limitations

In the realm of computer integration in education, there exist various challenges and limitations that educators and institutions must contend with. One of the primary challenges is the digital divide, wherein students from low-income families may not have equal access to computers and the internet, placing them at a disadvantage in terms of digital literacy and access to online educational resources. Moreover, the rapid pace of technological advancement means that educational technology can quickly become outdated, requiring ongoing investment and resources to keep pace with the latest developments. Another significant challenge is the potential for over-reliance on technology, which may lead to a reduction in critical thinking and problem-solving skills among students. It is essential for educators to strike a balance between utilizing technology as a tool for learning and ensuring that students develop essential cognitive abilities. Additionally, the integration of computers in education raises concerns about privacy and data security, as the collection and storage of student data become more prevalent. Safeguarding sensitive information and ensuring ethical use of technology are crucial considerations for educational institutions. Furthermore, the challenges of computer integration in education extend to the training and professional development of educators. Many teachers may lack the necessary skills and knowledge to effectively harness the potential of educational technology, highlighting the need for comprehensive training programs and ongoing support. Ultimately, while the integration of computers in education offers numerous benefits, it is imperative to address these challenges and limitations in order to maximize the potential of technology as a tool for learning and student development.

5. Future Trends and Innovations in Educational Technology

In recent years, the field of educational technology has seen rapid advancements and innovations, paving the way for future trends that are expected to transform the way education is delivered. One such trend is the increasing use of artificial intelligence (AI) in educational technology, which has the potential to personalize learning experiences for students by providing adaptive and customized learning materials based on their individual needs and abilities. Additionally, the integration of virtual and augmented reality technologies in education is gaining momentum, allowing students to immerse themselves in interactive and immersive learning environments that enhance their understanding of complex concepts. Another key trend in educational technology is the growing emphasis on digital literacy and computational thinking skills, as the demand for these skills continues to rise in the modern workforce. This has led to the development of coding and programming courses at various educational levels, aimed at equipping students with the necessary skills to thrive in an increasingly digital world. Furthermore, the use of collaborative online tools and platforms for remote learning and virtual classrooms has become essential, especially in light of recent global events that have accelerated the adoption of remote and online learning. As educational technology continues to evolve, it is essential for educators and policymakers to stay abreast of these future trends and innovations, in order to effectively integrate them into the education system and ensure that students are well-prepared for the challenges of the future. By embracing these advancements, educational institutions can foster a more engaging, interactive, and inclusive learning environment that meets the diverse needs of today's learners.

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REALIZING THE PROMISE:

Leading up to the 75th anniversary of the UN General Assembly, this “Realizing the promise: How can education technology improve learning for all?” publication kicks off the Center for Universal Education’s first playbook in a series to help improve education around the world.

It is intended as an evidence-based tool for ministries of education, particularly in low- and middle-income countries, to adopt and more successfully invest in education technology.

While there is no single education initiative that will achieve the same results everywhere—as school systems differ in learners and educators, as well as in the availability and quality of materials and technologies—an important first step is understanding how technology is used given specific local contexts and needs.

The surveys in this playbook are designed to be adapted to collect this information from educators, learners, and school leaders and guide decisionmakers in expanding the use of technology.

Introduction

While technology has disrupted most sectors of the economy and changed how we communicate, access information, work, and even play, its impact on schools, teaching, and learning has been much more limited. We believe that this limited impact is primarily due to technology being been used to replace analog tools, without much consideration given to playing to technology’s comparative advantages. These comparative advantages, relative to traditional “chalk-and-talk” classroom instruction, include helping to scale up standardized instruction, facilitate differentiated instruction, expand opportunities for practice, and increase student engagement. When schools use technology to enhance the work of educators and to improve the quality and quantity of educational content, learners will thrive.

Further, COVID-19 has laid bare that, in today’s environment where pandemics and the effects of climate change are likely to occur, schools cannot always provide in-person education—making the case for investing in education technology.

Here we argue for a simple yet surprisingly rare approach to education technology that seeks to:

Understand the needs, infrastructure, and capacity of a school system—the diagnosis;
Survey the best available evidence on interventions that match those conditions—the evidence; and
Closely monitor the results of innovations before they are scaled up—the prognosis.

Podcast: How education technology can improve learning for all students

To make ed tech work, set clear goals, review the evidence, and pilot before you scale

The framework.

Our approach builds on a simple yet intuitive theoretical framework created two decades ago by two of the most prominent education researchers in the United States, David K. Cohen and Deborah Loewenberg Ball. They argue that what matters most to improve learning is the interactions among educators and learners around educational materials. We believe that the failed school-improvement efforts in the U.S. that motivated Cohen and Ball’s framework resemble the ed-tech reforms in much of the developing world to date in the lack of clarity improving the interactions between educators, learners, and the educational material. We build on their framework by adding parents as key agents that mediate the relationships between learners and educators and the material (Figure 1).

Figure 1: The instructional core

Adapted from Cohen and Ball (1999)

As the figure above suggests, ed-tech interventions can affect the instructional core in a myriad of ways. Yet, just because technology can do something, it does not mean it should. School systems in developing countries differ along many dimensions and each system is likely to have different needs for ed-tech interventions, as well as different infrastructure and capacity to enact such interventions.

The diagnosis:

How can school systems assess their needs and preparedness.

A useful first step for any school system to determine whether it should invest in education technology is to diagnose its:

Specific needs to improve student learning (e.g., raising the average level of achievement, remediating gaps among low performers, and challenging high performers to develop higher-order skills);
Infrastructure to adopt technology-enabled solutions (e.g., electricity connection, availability of space and outlets, stock of computers, and Internet connectivity at school and at learners’ homes); and
Capacity to integrate technology in the instructional process (e.g., learners’ and educators’ level of familiarity and comfort with hardware and software, their beliefs about the level of usefulness of technology for learning purposes, and their current uses of such technology).

Before engaging in any new data collection exercise, school systems should take full advantage of existing administrative data that could shed light on these three main questions. This could be in the form of internal evaluations but also international learner assessments, such as the Program for International Student Assessment (PISA), the Trends in International Mathematics and Science Study (TIMSS), and/or the Progress in International Literacy Study (PIRLS), and the Teaching and Learning International Study (TALIS). But if school systems lack information on their preparedness for ed-tech reforms or if they seek to complement existing data with a richer set of indicators, we developed a set of surveys for learners, educators, and school leaders. Download the full report to see how we map out the main aspects covered by these surveys, in hopes of highlighting how they could be used to inform decisions around the adoption of ed-tech interventions.

The evidence:

How can school systems identify promising ed-tech interventions.

There is no single “ed-tech” initiative that will achieve the same results everywhere, simply because school systems differ in learners and educators, as well as in the availability and quality of materials and technologies. Instead, to realize the potential of education technology to accelerate student learning, decisionmakers should focus on four potential uses of technology that play to its comparative advantages and complement the work of educators to accelerate student learning (Figure 2). These comparative advantages include:

Scaling up quality instruction, such as through prerecorded quality lessons.
Facilitating differentiated instruction, through, for example, computer-adaptive learning and live one-on-one tutoring.
Expanding opportunities to practice.
Increasing learner engagement through videos and games.

Figure 2: Comparative advantages of technology

Here we review the evidence on ed-tech interventions from 37 studies in 20 countries*, organizing them by comparative advantage. It’s important to note that ours is not the only way to classify these interventions (e.g., video tutorials could be considered as a strategy to scale up instruction or increase learner engagement), but we believe it may be useful to highlight the needs that they could address and why technology is well positioned to do so.

When discussing specific studies, we report the magnitude of the effects of interventions using standard deviations (SDs). SDs are a widely used metric in research to express the effect of a program or policy with respect to a business-as-usual condition (e.g., test scores). There are several ways to make sense of them. One is to categorize the magnitude of the effects based on the results of impact evaluations. In developing countries, effects below 0.1 SDs are considered to be small, effects between 0.1 and 0.2 SDs are medium, and those above 0.2 SDs are large (for reviews that estimate the average effect of groups of interventions, called “meta analyses,” see e.g., Conn, 2017; Kremer, Brannen, & Glennerster, 2013; McEwan, 2014; Snilstveit et al., 2015; Evans & Yuan, 2020.)

*In surveying the evidence, we began by compiling studies from prior general and ed-tech specific evidence reviews that some of us have written and from ed-tech reviews conducted by others. Then, we tracked the studies cited by the ones we had previously read and reviewed those, as well. In identifying studies for inclusion, we focused on experimental and quasi-experimental evaluations of education technology interventions from pre-school to secondary school in low- and middle-income countries that were released between 2000 and 2020. We only included interventions that sought to improve student learning directly (i.e., students’ interaction with the material), as opposed to interventions that have impacted achievement indirectly, by reducing teacher absence or increasing parental engagement. This process yielded 37 studies in 20 countries (see the full list of studies in Appendix B).

Scaling up standardized instruction

One of the ways in which technology may improve the quality of education is through its capacity to deliver standardized quality content at scale. This feature of technology may be particularly useful in three types of settings: (a) those in “hard-to-staff” schools (i.e., schools that struggle to recruit educators with the requisite training and experience—typically, in rural and/or remote areas) (see, e.g., Urquiola & Vegas, 2005); (b) those in which many educators are frequently absent from school (e.g., Chaudhury, Hammer, Kremer, Muralidharan, & Rogers, 2006; Muralidharan, Das, Holla, & Mohpal, 2017); and/or (c) those in which educators have low levels of pedagogical and subject matter expertise (e.g., Bietenbeck, Piopiunik, & Wiederhold, 2018; Bold et al., 2017; Metzler & Woessmann, 2012; Santibañez, 2006) and do not have opportunities to observe and receive feedback (e.g., Bruns, Costa, & Cunha, 2018; Cilliers, Fleisch, Prinsloo, & Taylor, 2018). Technology could address this problem by: (a) disseminating lessons delivered by qualified educators to a large number of learners (e.g., through prerecorded or live lessons); (b) enabling distance education (e.g., for learners in remote areas and/or during periods of school closures); and (c) distributing hardware preloaded with educational materials.

Prerecorded lessons

Technology seems to be well placed to amplify the impact of effective educators by disseminating their lessons. Evidence on the impact of prerecorded lessons is encouraging, but not conclusive. Some initiatives that have used short instructional videos to complement regular instruction, in conjunction with other learning materials, have raised student learning on independent assessments. For example, Beg et al. (2020) evaluated an initiative in Punjab, Pakistan in which grade 8 classrooms received an intervention that included short videos to substitute live instruction, quizzes for learners to practice the material from every lesson, tablets for educators to learn the material and follow the lesson, and LED screens to project the videos onto a classroom screen. After six months, the intervention improved the performance of learners on independent tests of math and science by 0.19 and 0.24 SDs, respectively but had no discernible effect on the math and science section of Punjab’s high-stakes exams.

One study suggests that approaches that are far less technologically sophisticated can also improve learning outcomes—especially, if the business-as-usual instruction is of low quality. For example, Naslund-Hadley, Parker, and Hernandez-Agramonte (2014) evaluated a preschool math program in Cordillera, Paraguay that used audio segments and written materials four days per week for an hour per day during the school day. After five months, the intervention improved math scores by 0.16 SDs, narrowing gaps between low- and high-achieving learners, and between those with and without educators with formal training in early childhood education.

Yet, the integration of prerecorded material into regular instruction has not always been successful. For example, de Barros (2020) evaluated an intervention that combined instructional videos for math and science with infrastructure upgrades (e.g., two “smart” classrooms, two TVs, and two tablets), printed workbooks for students, and in-service training for educators of learners in grades 9 and 10 in Haryana, India (all materials were mapped onto the official curriculum). After 11 months, the intervention negatively impacted math achievement (by 0.08 SDs) and had no effect on science (with respect to business as usual classes). It reduced the share of lesson time that educators devoted to instruction and negatively impacted an index of instructional quality. Likewise, Seo (2017) evaluated several combinations of infrastructure (solar lights and TVs) and prerecorded videos (in English and/or bilingual) for grade 11 students in northern Tanzania and found that none of the variants improved student learning, even when the videos were used. The study reports effects from the infrastructure component across variants, but as others have noted (Muralidharan, Romero, & Wüthrich, 2019), this approach to estimating impact is problematic.

A very similar intervention delivered after school hours, however, had sizeable effects on learners’ basic skills. Chiplunkar, Dhar, and Nagesh (2020) evaluated an initiative in Chennai (the capital city of the state of Tamil Nadu, India) delivered by the same organization as above that combined short videos that explained key concepts in math and science with worksheets, facilitator-led instruction, small groups for peer-to-peer learning, and occasional career counseling and guidance for grade 9 students. These lessons took place after school for one hour, five times a week. After 10 months, it had large effects on learners’ achievement as measured by tests of basic skills in math and reading, but no effect on a standardized high-stakes test in grade 10 or socio-emotional skills (e.g., teamwork, decisionmaking, and communication).

Drawing general lessons from this body of research is challenging for at least two reasons. First, all of the studies above have evaluated the impact of prerecorded lessons combined with several other components (e.g., hardware, print materials, or other activities). Therefore, it is possible that the effects found are due to these additional components, rather than to the recordings themselves, or to the interaction between the two (see Muralidharan, 2017 for a discussion of the challenges of interpreting “bundled” interventions). Second, while these studies evaluate some type of prerecorded lessons, none examines the content of such lessons. Thus, it seems entirely plausible that the direction and magnitude of the effects depends largely on the quality of the recordings (e.g., the expertise of the educator recording it, the amount of preparation that went into planning the recording, and its alignment with best teaching practices).

These studies also raise three important questions worth exploring in future research. One of them is why none of the interventions discussed above had effects on high-stakes exams, even if their materials are typically mapped onto the official curriculum. It is possible that the official curricula are simply too challenging for learners in these settings, who are several grade levels behind expectations and who often need to reinforce basic skills (see Pritchett & Beatty, 2015). Another question is whether these interventions have long-term effects on teaching practices. It seems plausible that, if these interventions are deployed in contexts with low teaching quality, educators may learn something from watching the videos or listening to the recordings with learners. Yet another question is whether these interventions make it easier for schools to deliver instruction to learners whose native language is other than the official medium of instruction.

Distance education

Technology can also allow learners living in remote areas to access education. The evidence on these initiatives is encouraging. For example, Johnston and Ksoll (2017) evaluated a program that broadcasted live instruction via satellite to rural primary school students in the Volta and Greater Accra regions of Ghana. For this purpose, the program also equipped classrooms with the technology needed to connect to a studio in Accra, including solar panels, a satellite modem, a projector, a webcam, microphones, and a computer with interactive software. After two years, the intervention improved the numeracy scores of students in grades 2 through 4, and some foundational literacy tasks, but it had no effect on attendance or classroom time devoted to instruction, as captured by school visits. The authors interpreted these results as suggesting that the gains in achievement may be due to improving the quality of instruction that children received (as opposed to increased instructional time). Naik, Chitre, Bhalla, and Rajan (2019) evaluated a similar program in the Indian state of Karnataka and also found positive effects on learning outcomes, but it is not clear whether those effects are due to the program or due to differences in the groups of students they compared to estimate the impact of the initiative.

In one context (Mexico), this type of distance education had positive long-term effects. Navarro-Sola (2019) took advantage of the staggered rollout of the telesecundarias (i.e., middle schools with lessons broadcasted through satellite TV) in 1968 to estimate its impact. The policy had short-term effects on students’ enrollment in school: For every telesecundaria per 50 children, 10 students enrolled in middle school and two pursued further education. It also had a long-term influence on the educational and employment trajectory of its graduates. Each additional year of education induced by the policy increased average income by nearly 18 percent. This effect was attributable to more graduates entering the labor force and shifting from agriculture and the informal sector. Similarly, Fabregas (2019) leveraged a later expansion of this policy in 1993 and found that each additional telesecundaria per 1,000 adolescents led to an average increase of 0.2 years of education, and a decline in fertility for women, but no conclusive evidence of long-term effects on labor market outcomes.

It is crucial to interpret these results keeping in mind the settings where the interventions were implemented. As we mention above, part of the reason why they have proven effective is that the “counterfactual” conditions for learning (i.e., what would have happened to learners in the absence of such programs) was either to not have access to schooling or to be exposed to low-quality instruction. School systems interested in taking up similar interventions should assess the extent to which their learners (or parts of their learner population) find themselves in similar conditions to the subjects of the studies above. This illustrates the importance of assessing the needs of a system before reviewing the evidence.

Preloaded hardware

Technology also seems well positioned to disseminate educational materials. Specifically, hardware (e.g., desktop computers, laptops, or tablets) could also help deliver educational software (e.g., word processing, reference texts, and/or games). In theory, these materials could not only undergo a quality assurance review (e.g., by curriculum specialists and educators), but also draw on the interactions with learners for adjustments (e.g., identifying areas needing reinforcement) and enable interactions between learners and educators.

In practice, however, most initiatives that have provided learners with free computers, laptops, and netbooks do not leverage any of the opportunities mentioned above. Instead, they install a standard set of educational materials and hope that learners find them helpful enough to take them up on their own. Students rarely do so, and instead use the laptops for recreational purposes—often, to the detriment of their learning (see, e.g., Malamud & Pop-Eleches, 2011). In fact, free netbook initiatives have not only consistently failed to improve academic achievement in math or language (e.g., Cristia et al., 2017), but they have had no impact on learners’ general computer skills (e.g., Beuermann et al., 2015). Some of these initiatives have had small impacts on cognitive skills, but the mechanisms through which those effects occurred remains unclear.

To our knowledge, the only successful deployment of a free laptop initiative was one in which a team of researchers equipped the computers with remedial software. Mo et al. (2013) evaluated a version of the One Laptop per Child (OLPC) program for grade 3 students in migrant schools in Beijing, China in which the laptops were loaded with a remedial software mapped onto the national curriculum for math (similar to the software products that we discuss under “practice exercises” below). After nine months, the program improved math achievement by 0.17 SDs and computer skills by 0.33 SDs. If a school system decides to invest in free laptops, this study suggests that the quality of the software on the laptops is crucial.

To date, however, the evidence suggests that children do not learn more from interacting with laptops than they do from textbooks. For example, Bando, Gallego, Gertler, and Romero (2016) compared the effect of free laptop and textbook provision in 271 elementary schools in disadvantaged areas of Honduras. After seven months, students in grades 3 and 6 who had received the laptops performed on par with those who had received the textbooks in math and language. Further, even if textbooks essentially become obsolete at the end of each school year, whereas laptops can be reloaded with new materials for each year, the costs of laptop provision (not just the hardware, but also the technical assistance, Internet, and training associated with it) are not yet low enough to make them a more cost-effective way of delivering content to learners.

Evidence on the provision of tablets equipped with software is encouraging but limited. For example, de Hoop et al. (2020) evaluated a composite intervention for first grade students in Zambia’s Eastern Province that combined infrastructure (electricity via solar power), hardware (projectors and tablets), and educational materials (lesson plans for educators and interactive lessons for learners, both loaded onto the tablets and mapped onto the official Zambian curriculum). After 14 months, the intervention had improved student early-grade reading by 0.4 SDs, oral vocabulary scores by 0.25 SDs, and early-grade math by 0.22 SDs. It also improved students’ achievement by 0.16 on a locally developed assessment. The multifaceted nature of the program, however, makes it challenging to identify the components that are driving the positive effects. Pitchford (2015) evaluated an intervention that provided tablets equipped with educational “apps,” to be used for 30 minutes per day for two months to develop early math skills among students in grades 1 through 3 in Lilongwe, Malawi. The evaluation found positive impacts in math achievement, but the main study limitation is that it was conducted in a single school.

Facilitating differentiated instruction

Another way in which technology may improve educational outcomes is by facilitating the delivery of differentiated or individualized instruction. Most developing countries massively expanded access to schooling in recent decades by building new schools and making education more affordable, both by defraying direct costs, as well as compensating for opportunity costs (Duflo, 2001; World Bank, 2018). These initiatives have not only rapidly increased the number of learners enrolled in school, but have also increased the variability in learner’ preparation for schooling. Consequently, a large number of learners perform well below grade-based curricular expectations (see, e.g., Duflo, Dupas, & Kremer, 2011; Pritchett & Beatty, 2015). These learners are unlikely to get much from “one-size-fits-all” instruction, in which a single educator delivers instruction deemed appropriate for the middle (or top) of the achievement distribution (Banerjee & Duflo, 2011). Technology could potentially help these learners by providing them with: (a) instruction and opportunities for practice that adjust to the level and pace of preparation of each individual (known as “computer-adaptive learning” (CAL)); or (b) live, one-on-one tutoring.

Computer-adaptive learning

One of the main comparative advantages of technology is its ability to diagnose students’ initial learning levels and assign students to instruction and exercises of appropriate difficulty. No individual educator—no matter how talented—can be expected to provide individualized instruction to all learners in his/her class simultaneously . In this respect, technology is uniquely positioned to complement traditional teaching. This use of technology could help learners master basic skills and help them get more out of schooling.

Although many software products evaluated in recent years have been categorized as CAL, many rely on a relatively coarse level of differentiation at an initial stage (e.g., a diagnostic test) without further differentiation. We discuss these initiatives under the category of “increasing opportunities for practice” below. CAL initiatives complement an initial diagnostic with dynamic adaptation (i.e., at each response or set of responses from learners) to adjust both the initial level of difficulty and rate at which it increases or decreases, depending on whether learners’ responses are correct or incorrect.

Existing evidence on this specific type of programs is highly promising. Most famously, Banerjee et al. (2007) evaluated CAL software in Vadodara, in the Indian state of Gujarat, in which grade 4 students were offered two hours of shared computer time per week before and after school, during which they played games that involved solving math problems. The level of difficulty of such problems adjusted based on students’ answers. This program improved math achievement by 0.35 and 0.47 SDs after one and two years of implementation, respectively. Consistent with the promise of personalized learning, the software improved achievement for all students. In fact, one year after the end of the program, students assigned to the program still performed 0.1 SDs better than those assigned to a business as usual condition. More recently, Muralidharan, et al. (2019) evaluated a “blended learning” initiative in which students in grades 4 through 9 in Delhi, India received 45 minutes of interaction with CAL software for math and language, and 45 minutes of small group instruction before or after going to school. After only 4.5 months, the program improved achievement by 0.37 SDs in math and 0.23 SDs in Hindi. While all learners benefited from the program in absolute terms, the lowest performing learners benefited the most in relative terms, since they were learning very little in school.

We see two important limitations from this body of research. First, to our knowledge, none of these initiatives has been evaluated when implemented during the school day. Therefore, it is not possible to distinguish the effect of the adaptive software from that of additional instructional time. Second, given that most of these programs were facilitated by local instructors, attempts to distinguish the effect of the software from that of the instructors has been mostly based on noncausal evidence. A frontier challenge in this body of research is to understand whether CAL software can increase the effectiveness of school-based instruction by substituting part of the regularly scheduled time for math and language instruction.

Live one-on-one tutoring

Recent improvements in the speed and quality of videoconferencing, as well as in the connectivity of remote areas, have enabled yet another way in which technology can help personalization: live (i.e., real-time) one-on-one tutoring. While the evidence on in-person tutoring is scarce in developing countries, existing studies suggest that this approach works best when it is used to personalize instruction (see, e.g., Banerjee et al., 2007; Banerji, Berry, & Shotland, 2015; Cabezas, Cuesta, & Gallego, 2011).

There are almost no studies on the impact of online tutoring—possibly, due to the lack of hardware and Internet connectivity in low- and middle-income countries. One exception is Chemin and Oledan (2020)’s recent evaluation of an online tutoring program for grade 6 students in Kianyaga, Kenya to learn English from volunteers from a Canadian university via Skype ( videoconferencing software) for one hour per week after school. After 10 months, program beneficiaries performed 0.22 SDs better in a test of oral comprehension, improved their comfort using technology for learning, and became more willing to engage in cross-cultural communication. Importantly, while the tutoring sessions used the official English textbooks and sought in part to help learners with their homework, tutors were trained on several strategies to teach to each learner’s individual level of preparation, focusing on basic skills if necessary. To our knowledge, similar initiatives within a country have not yet been rigorously evaluated.

Expanding opportunities for practice

A third way in which technology may improve the quality of education is by providing learners with additional opportunities for practice. In many developing countries, lesson time is primarily devoted to lectures, in which the educator explains the topic and the learners passively copy explanations from the blackboard. This setup leaves little time for in-class practice. Consequently, learners who did not understand the explanation of the material during lecture struggle when they have to solve homework assignments on their own. Technology could potentially address this problem by allowing learners to review topics at their own pace.

Practice exercises

Technology can help learners get more out of traditional instruction by providing them with opportunities to implement what they learn in class. This approach could, in theory, allow some learners to anchor their understanding of the material through trial and error (i.e., by realizing what they may not have understood correctly during lecture and by getting better acquainted with special cases not covered in-depth in class).

Existing evidence on practice exercises reflects both the promise and the limitations of this use of technology in developing countries. For example, Lai et al. (2013) evaluated a program in Shaanxi, China where students in grades 3 and 5 were required to attend two 40-minute remedial sessions per week in which they first watched videos that reviewed the material that had been introduced in their math lessons that week and then played games to practice the skills introduced in the video. After four months, the intervention improved math achievement by 0.12 SDs. Many other evaluations of comparable interventions have found similar small-to-moderate results (see, e.g., Lai, Luo, Zhang, Huang, & Rozelle, 2015; Lai et al., 2012; Mo et al., 2015; Pitchford, 2015). These effects, however, have been consistently smaller than those of initiatives that adjust the difficulty of the material based on students’ performance (e.g., Banerjee et al., 2007; Muralidharan, et al., 2019). We hypothesize that these programs do little for learners who perform several grade levels behind curricular expectations, and who would benefit more from a review of foundational concepts from earlier grades.

We see two important limitations from this research. First, most initiatives that have been evaluated thus far combine instructional videos with practice exercises, so it is hard to know whether their effects are driven by the former or the latter. In fact, the program in China described above allowed learners to ask their peers whenever they did not understand a difficult concept, so it potentially also captured the effect of peer-to-peer collaboration. To our knowledge, no studies have addressed this gap in the evidence.

Second, most of these programs are implemented before or after school, so we cannot distinguish the effect of additional instructional time from that of the actual opportunity for practice. The importance of this question was first highlighted by Linden (2008), who compared two delivery mechanisms for game-based remedial math software for students in grades 2 and 3 in a network of schools run by a nonprofit organization in Gujarat, India: one in which students interacted with the software during the school day and another one in which students interacted with the software before or after school (in both cases, for three hours per day). After a year, the first version of the program had negatively impacted students’ math achievement by 0.57 SDs and the second one had a null effect. This study suggested that computer-assisted learning is a poor substitute for regular instruction when it is of high quality, as was the case in this well-functioning private network of schools.

In recent years, several studies have sought to remedy this shortcoming. Mo et al. (2014) were among the first to evaluate practice exercises delivered during the school day. They evaluated an initiative in Shaanxi, China in which students in grades 3 and 5 were required to interact with the software similar to the one in Lai et al. (2013) for two 40-minute sessions per week. The main limitation of this study, however, is that the program was delivered during regularly scheduled computer lessons, so it could not determine the impact of substituting regular math instruction. Similarly, Mo et al. (2020) evaluated a self-paced and a teacher-directed version of a similar program for English for grade 5 students in Qinghai, China. Yet, the key shortcoming of this study is that the teacher-directed version added several components that may also influence achievement, such as increased opportunities for teachers to provide students with personalized assistance when they struggled with the material. Ma, Fairlie, Loyalka, and Rozelle (2020) compared the effectiveness of additional time-delivered remedial instruction for students in grades 4 to 6 in Shaanxi, China through either computer-assisted software or using workbooks. This study indicates whether additional instructional time is more effective when using technology, but it does not address the question of whether school systems may improve the productivity of instructional time during the school day by substituting educator-led with computer-assisted instruction.

Increasing learner engagement

Another way in which technology may improve education is by increasing learners’ engagement with the material. In many school systems, regular “chalk and talk” instruction prioritizes time for educators’ exposition over opportunities for learners to ask clarifying questions and/or contribute to class discussions. This, combined with the fact that many developing-country classrooms include a very large number of learners (see, e.g., Angrist & Lavy, 1999; Duflo, Dupas, & Kremer, 2015), may partially explain why the majority of those students are several grade levels behind curricular expectations (e.g., Muralidharan, et al., 2019; Muralidharan & Zieleniak, 2014; Pritchett & Beatty, 2015). Technology could potentially address these challenges by: (a) using video tutorials for self-paced learning and (b) presenting exercises as games and/or gamifying practice.

Video tutorials

Technology can potentially increase learner effort and understanding of the material by finding new and more engaging ways to deliver it. Video tutorials designed for self-paced learning—as opposed to videos for whole class instruction, which we discuss under the category of “prerecorded lessons” above—can increase learner effort in multiple ways, including: allowing learners to focus on topics with which they need more help, letting them correct errors and misconceptions on their own, and making the material appealing through visual aids. They can increase understanding by breaking the material into smaller units and tackling common misconceptions.

In spite of the popularity of instructional videos, there is relatively little evidence on their effectiveness. Yet, two recent evaluations of different versions of the Khan Academy portal, which mainly relies on instructional videos, offer some insight into their impact. First, Ferman, Finamor, and Lima (2019) evaluated an initiative in 157 public primary and middle schools in five cities in Brazil in which the teachers of students in grades 5 and 9 were taken to the computer lab to learn math from the platform for 50 minutes per week. The authors found that, while the intervention slightly improved learners’ attitudes toward math, these changes did not translate into better performance in this subject. The authors hypothesized that this could be due to the reduction of teacher-led math instruction.

More recently, Büchel, Jakob, Kühnhanss, Steffen, and Brunetti (2020) evaluated an after-school, offline delivery of the Khan Academy portal in grades 3 through 6 in 302 primary schools in Morazán, El Salvador. Students in this study received 90 minutes per week of additional math instruction (effectively nearly doubling total math instruction per week) through teacher-led regular lessons, teacher-assisted Khan Academy lessons, or similar lessons assisted by technical supervisors with no content expertise. (Importantly, the first group provided differentiated instruction, which is not the norm in Salvadorian schools). All three groups outperformed both schools without any additional lessons and classrooms without additional lessons in the same schools as the program. The teacher-assisted Khan Academy lessons performed 0.24 SDs better, the supervisor-led lessons 0.22 SDs better, and the teacher-led regular lessons 0.15 SDs better, but the authors could not determine whether the effects across versions were different.

Together, these studies suggest that instructional videos work best when provided as a complement to, rather than as a substitute for, regular instruction. Yet, the main limitation of these studies is the multifaceted nature of the Khan Academy portal, which also includes other components found to positively improve learner achievement, such as differentiated instruction by students’ learning levels. While the software does not provide the type of personalization discussed above, learners are asked to take a placement test and, based on their score, educators assign them different work. Therefore, it is not clear from these studies whether the effects from Khan Academy are driven by its instructional videos or to the software’s ability to provide differentiated activities when combined with placement tests.

Games and gamification

Technology can also increase learner engagement by presenting exercises as games and/or by encouraging learner to play and compete with others (e.g., using leaderboards and rewards)—an approach known as “gamification.” Both approaches can increase learner motivation and effort by presenting learners with entertaining opportunities for practice and by leveraging peers as commitment devices.

There are very few studies on the effects of games and gamification in low- and middle-income countries. Recently, Araya, Arias Ortiz, Bottan, and Cristia (2019) evaluated an initiative in which grade 4 students in Santiago, Chile were required to participate in two 90-minute sessions per week during the school day with instructional math software featuring individual and group competitions (e.g., tracking each learner’s standing in his/her class and tournaments between sections). After nine months, the program led to improvements of 0.27 SDs in the national student assessment in math (it had no spillover effects on reading). However, it had mixed effects on non-academic outcomes. Specifically, the program increased learners’ willingness to use computers to learn math, but, at the same time, increased their anxiety toward math and negatively impacted learners’ willingness to collaborate with peers. Finally, given that one of the weekly sessions replaced regular math instruction and the other one represented additional math instructional time, it is not clear whether the academic effects of the program are driven by the software or the additional time devoted to learning math.

The prognosis:

How can school systems adopt interventions that match their needs.

Here are five specific and sequential guidelines for decisionmakers to realize the potential of education technology to accelerate student learning.

1. Take stock of how your current schools, educators, and learners are engaging with technology .

Carry out a short in-school survey to understand the current practices and potential barriers to adoption of technology (we have included suggested survey instruments in the Appendices); use this information in your decisionmaking process. For example, we learned from conversations with current and former ministers of education from various developing regions that a common limitation to technology use is regulations that hold school leaders accountable for damages to or losses of devices. Another common barrier is lack of access to electricity and Internet, or even the availability of sufficient outlets for charging devices in classrooms. Understanding basic infrastructure and regulatory limitations to the use of education technology is a first necessary step. But addressing these limitations will not guarantee that introducing or expanding technology use will accelerate learning. The next steps are thus necessary.

“In Africa, the biggest limit is connectivity. Fiber is expensive, and we don’t have it everywhere. The continent is creating a digital divide between cities, where there is fiber, and the rural areas. The [Ghanaian] administration put in schools offline/online technologies with books, assessment tools, and open source materials. In deploying this, we are finding that again, teachers are unfamiliar with it. And existing policies prohibit students to bring their own tablets or cell phones. The easiest way to do it would have been to let everyone bring their own device. But policies are against it.” H.E. Matthew Prempeh, Minister of Education of Ghana, on the need to understand the local context.

2. Consider how the introduction of technology may affect the interactions among learners, educators, and content .

Our review of the evidence indicates that technology may accelerate student learning when it is used to scale up access to quality content, facilitate differentiated instruction, increase opportunities for practice, or when it increases learner engagement. For example, will adding electronic whiteboards to classrooms facilitate access to more quality content or differentiated instruction? Or will these expensive boards be used in the same way as the old chalkboards? Will providing one device (laptop or tablet) to each learner facilitate access to more and better content, or offer students more opportunities to practice and learn? Solely introducing technology in classrooms without additional changes is unlikely to lead to improved learning and may be quite costly. If you cannot clearly identify how the interactions among the three key components of the instructional core (educators, learners, and content) may change after the introduction of technology, then it is probably not a good idea to make the investment. See Appendix A for guidance on the types of questions to ask.

3. Once decisionmakers have a clear idea of how education technology can help accelerate student learning in a specific context, it is important to define clear objectives and goals and establish ways to regularly assess progress and make course corrections in a timely manner .

For instance, is the education technology expected to ensure that learners in early grades excel in foundational skills—basic literacy and numeracy—by age 10? If so, will the technology provide quality reading and math materials, ample opportunities to practice, and engaging materials such as videos or games? Will educators be empowered to use these materials in new ways? And how will progress be measured and adjusted?

4. How this kind of reform is approached can matter immensely for its success.

It is easy to nod to issues of “implementation,” but that needs to be more than rhetorical. Keep in mind that good use of education technology requires thinking about how it will affect learners, educators, and parents. After all, giving learners digital devices will make no difference if they get broken, are stolen, or go unused. Classroom technologies only matter if educators feel comfortable putting them to work. Since good technology is generally about complementing or amplifying what educators and learners already do, it is almost always a mistake to mandate programs from on high. It is vital that technology be adopted with the input of educators and families and with attention to how it will be used. If technology goes unused or if educators use it ineffectually, the results will disappoint—no matter the virtuosity of the technology. Indeed, unused education technology can be an unnecessary expenditure for cash-strapped education systems. This is why surveying context, listening to voices in the field, examining how technology is used, and planning for course correction is essential.

5. It is essential to communicate with a range of stakeholders, including educators, school leaders, parents, and learners .

Technology can feel alien in schools, confuse parents and (especially) older educators, or become an alluring distraction. Good communication can help address all of these risks. Taking care to listen to educators and families can help ensure that programs are informed by their needs and concerns. At the same time, deliberately and consistently explaining what technology is and is not supposed to do, how it can be most effectively used, and the ways in which it can make it more likely that programs work as intended. For instance, if teachers fear that technology is intended to reduce the need for educators, they will tend to be hostile; if they believe that it is intended to assist them in their work, they will be more receptive. Absent effective communication, it is easy for programs to “fail” not because of the technology but because of how it was used. In short, past experience in rolling out education programs indicates that it is as important to have a strong intervention design as it is to have a solid plan to socialize it among stakeholders.

Beyond reopening: A leapfrog moment to transform education?

On September 14, the Center for Universal Education (CUE) will host a webinar to discuss strategies, including around the effective use of education technology, for ensuring resilient schools in the long term and to launch a new education technology playbook “Realizing the promise: How can education technology improve learning for all?”

file-pdf Full Playbook – Realizing the promise: How can education technology improve learning for all? file-pdf References file-pdf Appendix A – Instruments to assess availability and use of technology file-pdf Appendix B – List of reviewed studies file-pdf Appendix C – How may technology affect interactions among students, teachers, and content?

About the Authors

Alejandro j. ganimian, emiliana vegas, frederick m. hess.

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713 Computers in education and learning

Published: April 2019
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As we have already hinted, computers and the internet have made profound changes in how we learn. We begin this topic by reviewing influential visions and early prototypes suggesting how technology could revolutionize education. Early on, computers were used by educators to deliver online tutorials about subject material, administer drill-and-practice exercises on rote skills, act as supportive environments for creatively exploring ideas through programming in English-like languages, and function as inexpensive, ubiquitous, and dynamic audio-visual resources. We shall then discuss other newer methods for using digital technologies to transform how students approach subject matter and how classrooms are organized. By using interactive simulation games, students learn by taking actions with respect to certain scenarios. Presentation aids such as PowerPoint and Prezi have replaced blackboards to present and elucidate concepts. Smart classrooms allow instructors and students access to technology that facilitates learning; inverted classrooms allow more effective use of classroom time by enabling students to prepare for lectures in advance and focus on working together with their teachers in class. Intelligent tutors are artificial intelligence (AI) programs that actively support student learning, diagnose student difficulties with the material, and then adapt tutoring strategies based on these findings. Next, we shall review how online learning has opened up new opportunities for adult and continuing education, whereby students can learn in their own time and at their own pace. The challenge online learning technology developers now face is to provide discussion forums, real-time chat capabilities, and methods for instructor feedback so that advantages of face-to-face interaction are not lost in web-based learning. Particularly exciting is the growth of worldwide learning communities via Massive Open Online Courses (MOOCs), an area of current expansion and creativity. While technology is now seen as instrumental in learning, there are still debates on the extent to which it should be used and how it should be used in education. A particularly prevalent dilemma is in middle and secondary schools. The issue is whether or not and how to encourage or disallow the use of mobile phones and other devices in classrooms.

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Information and communication technology (ICT) in education

Information and communications technology (ict) can impact student learning when teachers are digitally literate and understand how to integrate it into curriculum..

Schools use a diverse set of ICT tools to communicate, create, disseminate, store, and manage information.(6) In some contexts, ICT has also become integral to the teaching-learning interaction, through such approaches as replacing chalkboards with interactive digital whiteboards, using students’ own smartphones or other devices for learning during class time, and the “flipped classroom” model where students watch lectures at home on the computer and use classroom time for more interactive exercises.

When teachers are digitally literate and trained to use ICT, these approaches can lead to higher order thinking skills, provide creative and individualized options for students to express their understandings, and leave students better prepared to deal with ongoing technological change in society and the workplace.(18)

ICT issues planners must consider include: considering the total cost-benefit equation, supplying and maintaining the requisite infrastructure, and ensuring investments are matched with teacher support and other policies aimed at effective ICT use.(16)

Issues and Discussion

Digital culture and digital literacy: Computer technologies and other aspects of digital culture have changed the ways people live, work, play, and learn, impacting the construction and distribution of knowledge and power around the world.(14) Graduates who are less familiar with digital culture are increasingly at a disadvantage in the national and global economy. Digital literacy—the skills of searching for, discerning, and producing information, as well as the critical use of new media for full participation in society—has thus become an important consideration for curriculum frameworks.(8)

In many countries, digital literacy is being built through the incorporation of information and communication technology (ICT) into schools. Some common educational applications of ICT include:

One laptop per child: Less expensive laptops have been designed for use in school on a 1:1 basis with features like lower power consumption, a low cost operating system, and special re-programming and mesh network functions.(42) Despite efforts to reduce costs, however, providing one laptop per child may be too costly for some developing countries.(41)
Tablets: Tablets are small personal computers with a touch screen, allowing input without a keyboard or mouse. Inexpensive learning software (“apps”) can be downloaded onto tablets, making them a versatile tool for learning.(7)(25) The most effective apps develop higher order thinking skills and provide creative and individualized options for students to express their understandings.(18)
Interactive White Boards or Smart Boards : Interactive white boards allow projected computer images to be displayed, manipulated, dragged, clicked, or copied.(3) Simultaneously, handwritten notes can be taken on the board and saved for later use. Interactive white boards are associated with whole-class instruction rather than student-centred activities.(38) Student engagement is generally higher when ICT is available for student use throughout the classroom.(4)
E-readers : E-readers are electronic devices that can hold hundreds of books in digital form, and they are increasingly utilized in the delivery of reading material.(19) Students—both skilled readers and reluctant readers—have had positive responses to the use of e-readers for independent reading.(22) Features of e-readers that can contribute to positive use include their portability and long battery life, response to text, and the ability to define unknown words.(22) Additionally, many classic book titles are available for free in e-book form.
Flipped Classrooms: The flipped classroom model, involving lecture and practice at home via computer-guided instruction and interactive learning activities in class, can allow for an expanded curriculum. There is little investigation on the student learning outcomes of flipped classrooms.(5) Student perceptions about flipped classrooms are mixed, but generally positive, as they prefer the cooperative learning activities in class over lecture.(5)(35)

ICT and Teacher Professional Development: Teachers need specific professional development opportunities in order to increase their ability to use ICT for formative learning assessments, individualized instruction, accessing online resources, and for fostering student interaction and collaboration.(15) Such training in ICT should positively impact teachers’ general attitudes towards ICT in the classroom, but it should also provide specific guidance on ICT teaching and learning within each discipline. Without this support, teachers tend to use ICT for skill-based applications, limiting student academic thinking.(32) To support teachers as they change their teaching, it is also essential for education managers, supervisors, teacher educators, and decision makers to be trained in ICT use.(11)

Ensuring benefits of ICT investments: To ensure the investments made in ICT benefit students, additional conditions must be met. School policies need to provide schools with the minimum acceptable infrastructure for ICT, including stable and affordable internet connectivity and security measures such as filters and site blockers. Teacher policies need to target basic ICT literacy skills, ICT use in pedagogical settings, and discipline-specific uses. (21) Successful implementation of ICT requires integration of ICT in the curriculum. Finally, digital content needs to be developed in local languages and reflect local culture. (40) Ongoing technical, human, and organizational supports on all of these issues are needed to ensure access and effective use of ICT. (21)

Resource Constrained Contexts: The total cost of ICT ownership is considerable: training of teachers and administrators, connectivity, technical support, and software, amongst others. (42) When bringing ICT into classrooms, policies should use an incremental pathway, establishing infrastructure and bringing in sustainable and easily upgradable ICT. (16) Schools in some countries have begun allowing students to bring their own mobile technology (such as laptop, tablet, or smartphone) into class rather than providing such tools to all students—an approach called Bring Your Own Device. (1)(27)(34) However, not all families can afford devices or service plans for their children. (30) Schools must ensure all students have equitable access to ICT devices for learning.

Inclusiveness Considerations

Digital Divide: The digital divide refers to disparities of digital media and internet access both within and across countries, as well as the gap between people with and without the digital literacy and skills to utilize media and internet.(23)(26)(31) The digital divide both creates and reinforces socio-economic inequalities of the world’s poorest people. Policies need to intentionally bridge this divide to bring media, internet, and digital literacy to all students, not just those who are easiest to reach.

Minority language groups: Students whose mother tongue is different from the official language of instruction are less likely to have computers and internet connections at home than students from the majority. There is also less material available to them online in their own language, putting them at a disadvantage in comparison to their majority peers who gather information, prepare talks and papers, and communicate more using ICT. (39) Yet ICT tools can also help improve the skills of minority language students—especially in learning the official language of instruction—through features such as automatic speech recognition, the availability of authentic audio-visual materials, and chat functions. (2)(17)

Students with different styles of learning: ICT can provide diverse options for taking in and processing information, making sense of ideas, and expressing learning. Over 87% of students learn best through visual and tactile modalities, and ICT can help these students ‘experience’ the information instead of just reading and hearing it. (20)(37) Mobile devices can also offer programmes (“apps”) that provide extra support to students with special needs, with features such as simplified screens and instructions, consistent placement of menus and control features, graphics combined with text, audio feedback, ability to set pace and level of difficulty, appropriate and unambiguous feedback, and easy error correction. (24)(29)

Plans and policies

India [ PDF ]
Detroit, USA [ PDF ]
Finland [ PDF ]
Alberta Education. 2012. Bring your own device: A guide for schools . Retrieved from http://education.alberta.ca/admin/technology/research.aspx
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"Burk, R. 2001. 'E-book devices and the marketplace: In search of customers.' Library Hi Tech 19 (4)."
Chapman, D., and Mählck, L. (Eds). 2004. Adapting technology for school improvement: a global perspective. Paris: International Institute for Educational Planning.
Cheung, A.C.K and Slavin, R.E. 2012. ‘How features of educational technology applications affect student reading outcomes: A meta-analysis.’ Educational Research Review . 7.
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Deuze, M. 2006. 'Participation Remediation Bricolage - Considering Principal Components of a Digital Culture.' The Information Society . 22 .
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Enyedy, N. 2014. Personalized Instruction: New Interest, Old Rhetoric, Limited Results, and the Need for a New Direction for Computer-Mediated Learning . Boulder, CO: National Education Policy Center.
Golonka, E.M., Bowles, A.R., Frank, V.M., Richardson, D.L. and Freynik, S. 2014. ‘Technologies for foreign language learning: A review of technology types and their effectiveness.’ Computer Assisted Language Learning . 27 (1).
Goodwin, K. 2012. Use of Tablet Technology in the Classroom . Strathfield, New South Wales: NSW Curriculum and Learning Innovation Centre.
Jung, J., Chan-Olmsted, S., Park, B., and Kim, Y. 2011. 'Factors affecting e-book reader awareness, interest, and intention to use.' New Media & Society . 14 (2)
Kenney, L. 2011. ‘Elementary education, there’s an app for that. Communication technology in the elementary school classroom.’ The Elon Journal of Undergraduate Research in Communications . 2 (1).
Kopcha, T.J. 2012. ‘Teachers’ perceptions of the barriers to technology integration and practices with technology under situated professional development.’ Computers and Education . 59.
Miranda, T., Williams-Rossi, D., Johnson, K., and McKenzie, N. 2011. "Reluctant readers in middle school: Successful engagement with text using the e-reader.' International journal of applied science and technology . 1 (6).
Moyo, L. 2009. 'The digital divide: scarcity, inequality and conflict.' Digital Cultures . New York: Open University Press.
Newton, D.A. and Dell, A.G. 2011. ‘Mobile devices and students with disabilities: What do best practices tell us?’ Journal of Special Education Technology . 26 (3).
Nirvi, S. (2011). ‘Special education pupils find learning tool in iPad applications.’ Education Week . 30 .
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Related information

Information and communication technologies (ICT)

Importance of ICT in Education Essay

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ICT: Introduction

Teachers and their role in education, impact of ict in education, use of ict in education, importance of ict to students, works cited.

Information and Communication Technology is among the most indispensable tools that the business world relies on today. Virtually all businesses, in one way or another, rely on technology tools to carry out operations. Other organizations like learning institutions are not left behind technology-wise. ICT is increasingly being employed in contemporary learning institutions to ease the work of students and teachers.

Among the most commendable successes of employing ICT in learning institutions is e-learning, in which the ICT tools are used to access classrooms remotely. This paper explores the importance of the tools of the tools of ICT in education and the roles that these tools have played in making learning better and easier.

Teachers are scholars who have mastered specific subjects that form part of their specialty and help in imparting knowledge to students. Some of the roles that teachers play in academic institutions include designing syllabuses, preparing timetables, preparing for lessons and convening students for lessons, and carrying out continuous assessments on students.

Others include keeping records of academic reports, disciplinary records, and other records related to the activities of students in school, like the participation of students in games and other activities.

In cases where there are limitations such that it is impossible to convene people and resources together for learning. E-learning provides a very important and convenient way of teaching people. In such a case, a teacher provides learning materials and lessons online, which can be accessed by his/her students at their convenience.

The materials can be audio files of recorded classroom lessons, audio-visual files for lessons requiring visual information like practical or even text documents, and hypertext documents (Tinio 1). This method of teaching is also convenient for teachers because they are able to record lessons at their convenience, and the assessment of students involves less documentation.

This is because with the use of the internet, teachers are able to upload assignments and continuous assessments on the e-learning systems, and after students are done with the assignments, they use the system or emails to send their completed assignments to their teachers. This comes with a number of advantages which are brought about by having students complete assignments in soft copies.

One of these advantages is that feedback from teachers will be timely and it will be convenient for the teachers. Teachers can also use technology tools such as plagiarism software to check if students have copied the works of other scholars and thus establish the authenticity of the assignment. It can thus be argued that although e-learning systems have their disadvantages, they are very instrumental in teaching people whose schedules are tight and who may have limitations as far as accessing the classroom is concerned.

Therefore technology has been an influential and essential tool in the career of education, and several innovations have been made that have made teaching a much easier career. The paragraph below discusses other ways in which technology has been employed in the education career.

Teachers can also use the tools of ICT in other functions. One such function is keeping records of student performances and other kinds of records within the academic institution. This can be done by uploading the information to a Management Information System for the school or college, which should have a database for supporting the same. The information can also be stored in soft form in Compact Disks, Hard Drives, Flash Disks, or even Digital Video Disks (Obringer 1).

This ensures that information is properly stored and backed up and also ensures that records are not as bulky as they would have been in the absence of the tools of ICT. Such a system also ensures that information can easily be accessed and also ensures that proper privacy of the data is maintained.

Another way in which teachers can use the tools of ICT to ease their work is by employing tools like projectors for presentations of lessons, iPads for students, computers connected to the internet for communicating to students about continuous assessments, and the like (Higgins 1). This way, the teacher will be able to reduce the paperwork that he /she uses in his/her work, and this is bound to make his/her work easier.

For instance, if the teacher can access a projector, he/she can prepare a presentation of a lesson for his/her students, and this way, he will not have to carry textbooks, notebooks, and the like to the classroom for the lesson. The teacher can also post notes and relevant texts for a given course on the information system for the school or on an interactive website, and thus he/she will have more time for discussions during lessons.

Teachers can also, in consultation with IT specialists, develop real-time systems where students can answer questions related woo what they have learned in class and get automated results through the system (Masie 1).

This will help the students understand the concepts taught in class better, and this way, teachers will have less workload. Such websites will also help teachers to show the students how questions related to their specialty are framed early enough so that students can concentrate on knowledge acquisition during class hours.

This is as opposed to a case where the students remain clueless about the kind of questions they expect in exams and spend most of their time preparing for exams rather than reading extensively to acquire knowledge. ICT can also be sued by teachers to advertise the kind of services they offer in schools and also advertise the books and journals they have written. This can be achieved by using websites for the school or specific teachers or professors.

As evidenced in the discussion above, ICT is a very instrumental tool in education as a career. The specific tools of ICT used in education, as discussed above, include the use of ICT in distance learning, storage of student performance and other relevant information in databases and storage media, and the use of tools of ICT in classroom like projectors, iPads and the like. Since the invention of the internet and the subsequent popularity of computers, a lot of functions of education as a career have been made simpler.

These include the administration of continuous assessments, marking continuous assessments, giving feedback to students, and even checking the originality of the ideas expressed in the assignments and examinations. All in all, the impact that ICT has had in educational institutions is so much that school life without ICT is somehow impossible for people who are accustomed to using ICT.

Higgins, Steve. “Does ICT improve learning and teaching in schools”. 2007. Web.

Masie, Shank. “What is electronic learning?” 2007. Web.

Obringer, Ann. “ How E-Learning Works ”. 2008. Web.

Tinio, Victoria. “ICT in Education”. 2008. Web.

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Technological Advances in Education
Computer Based Training Verses Instructor Lead Training
Smart Classroom and Its Effect on Student Learning
Use of Technology as a Learning Tool
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Chicago (A-D)
Chicago (N-B)

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The Role of Computer in Education

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Importance of computer education in our life

What is computer education.

A computer is an electronic machine which can be used to solve a problem or perform a certain function according to the instructions given to it. Today computers are used almost everywhere. Computer education is the process of learning about or teaching about the computers. It includes the basic knowledge of computer system, skills, ideas, and the basic terminologies related to the computer system. It also includes the advantages and disadvantages of computers, the potential of a computer system, how a computer can be used to solve different problems of day to day life or how can a computer be programmed to solve the extreme problem. Computer education has become an integral part of the twenty-first century. It has gained a lot of importance in today’s life. Today, computers are used in almost every field. Therefore, it has become necessary to learn about computers.

Importance of Computer Education in Our Life

Computers help the students to learn about the world and know what is happening in it. It helps them to aim for excellent jobs in the future and succeed in it. The computer has become a standard of education throughout the world. This makes computer education important. Some importance of computer education are:

Computer Education Improves Research Skills: A computer provides the most important tool for research in today’s life that is the internet. The Internet is defined as a network which is formed by connecting different networks. Today the internet can help us with almost anything. Most importantly, the internet helps us in research. Starting from the students studying in school to the scientist working in laboratories, computer, or more precisely the internet, helps everybody in research. The Internet is flooded with abundant information on almost all the topics of which we are aware of. In the summer vacations, students are given some holiday’s homework to research or make projects on the topics they do not know about. For these topics, of which the students do not have any idea, the internet helps them. The Internet can give them a lot of information on the required topic. A scientist can take the help of internet to search the already present discoveries to create a new one. Therefore computers can help a lot in research. So the knowledge about computers is necessary. Everyone should know how to use the computer system and the resources associated with it to improve their own research skills.
Computer Education helps in getting Good Jobs: Today the computer industry is growing at a very fast rate. Computers are needed everywhere. They have become an essential part of each industry. Today almost every work is dependent on computers. So the industries or companies hire those workers which are trained to use computers or have some knowledge as to how to use computers. Computer education should be taught to the students from the very beginning. They should gain a good command in the field of computers. In the whole academic life of the student, they should become so trained in the field of computer education that every company will hire. So we can conclude that for those who aspire for a good job, computer education is a must. The salary package offered to those who have all the knowledge of a computer system is much higher than those who do not have any idea about computers.
Computer Education helps in Enhancing Technology: Today, most of the technology depends on the computer system. From the basic electronic devices to astronomical devices, everything needs a computer. So if someone wants to create a new technology he should have known about the computers. For example, a person wants to create a machine which could be used in medical science. The machine will require some device to formulate the results. This device is a modified version of a computer system. So the person should be able to modify the computer. For this, the person must be educated in the field of computer science. When a person studies computer science, he feels motivated to create new technologies. It fills his mind with new ideas to create some new technology which could be used for the betterment of the society.
Computer Education Increases the Efficiency of a Person: Consider a person who does not have any knowledge of the computer. The person works in the accounts department of some company. The person has to keep track of all the financial records of the company, he needs to maintain the record of all profits and loss of the company from the very beginning. This will require a lot of time, concentration, speed and memory. This is a very difficult task. This task is very tiring for the person as all the records have to be prepared using pen and paper. On the other hand, consider a person who has the knowledge of computer system. He will use the computer to maintain all the accounts of the company. He will take less time to maintain the records as everything used by him will be computerized. He will not require any physical space to store his records which are required by the person who does not know computer. It will require less time. The work done will be fast. Comparing both the cases, the efficiency of the person who knows the compute r will be more than that of the person who does not know the compute r. Therefore it becomes important to have computer education.
Computer Education helps in Creating a Better Education Environment: Smart classrooms are emerging these days. Every school uses computers to teach their students. It creates a more effective learning and teaching environment. Learning becomes easier with the use of technology. Along with becoming easier, it becomes all the way more fun. To use the facilities available in a smart classroom, computer education is necessary. Every school prefers to employ those teachers who can use computers as a teaching tool. Computers can be used to teach a lot of things. With multimedia available in the computer system, the difficult topics can be easily understood. The information delivered to the students via a computerized is much more easily retained by them than the regular delivery of information. So for imparting proper and effective education to the students , the teachers must possess a fair education about the computer system and their usage.
Computer Education makes Communication Easy: The world is very large. All our loved ones do not live with us. We all want to communicate with our loved ones who reside in the different parts of the world or country. The communication started with a letter and came till telephones. Letters did not offer effective communication over a very long distance and the communication was only text-based. The telephonic conversation was one step ahead. We could hear the voice of our loved ones. In today’s technology, we can use computers for communication. It provides us with facilities like chatting, calling, video conferencing which has helped a lot in communication. The function of video conferencing or we can say video chat or video calling is being used widely these days. It helps us to see the person we are talking to. It has become very useful for the person who resides very far from their families as they can now communicate with them as they are just sitting in front of them. To use these facilities of communication using the computer, computer education is required. Nowadays, children who reside far from their parents are teaching their parents how to use the computer for the communication so that they can have an easy and cheap communication with them.
Computer Education Connects us to the Online World: Everything today is becoming online. This is just done for our convenience. Today, we do not have to visit a bank for transferring money, nor we have to go to the market to do shopping .it is available to us online as online banking and online shopping. We can fill examination and other kinds of the form online. Now we do not have to run to the theatre or railways station to buy movie tickets and train tickets, we can book them online. We can plan our tours online. We can connect with our friends online. The online world also provides us with entertainment. All this could not be possible without computers. But to use all these facilities, computer education is required. Without a computer , we cannot use such facilities which are specially designed for our convenience.

Conclusion Computers have occupied a very important place in our lives. We cannot imagine our life without computers. They are being used in each and every field to make work easier. The work is done in an efficient manner and consumes less time. However, computer systems have a few disadvantages also. Computers have no brain. They cannot take a decision on their own. They need human guidance. Computers can affect health. They may affect the eyes of the person using it. Also, the computers which are not in working condition and cannot be repaired accumulate as non-biodegradable waste. Despite these disadvantages, a computer does not lose its importance and created the need for computer education. With the growing uses of the computer, the requirement of computer education is also. With such vas usage of computers system, it has become necessary that each person should have the knowledge to use the computer systems. Now computer education is being taught in schools and colleges. The elderly people are also trying to learn how to use a computer. As the time is passing, technology is increasing. So for our own convenience, it has become important for all of us to gain computer education.

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Role Of Computer In Education

What is Computer Education?

Gaining basic knowledge as well as skills to operate computers to perform better jobs. Computer in education is all about extending to its various branches of study in different fields & sectors.

A computer, along with an internet facility is the most powerful device that children can use to learn new skills & abilities in education. The computer plays a significant role in each and every field of life. They help us in several ways.

For example, they find applications in medicine, industrial processes, the aviation industry, making bills in various big shops & malls, creating presentation slides in application software for making notes & delivering lectures in colleges, universities, and a lot more. In short, not only in just one, but the Computer plays an all-rounded role in the field of education of students.

Computers in Teaching & Learning Process (CAL)

Computer-based training (cbt).

Benefits Of Computer Education:

Uses Of Computers in Education

How does computer help in the education process.

Innovation in Computer technology has a profound impact on education. It forms a part of the school curriculum as it is an essential part of every individual today. Computer education in schools plays a major noteworthy role in the career development of young children.

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Being actively used in various educational institutes like schools, colleges & big universities, computers are used to aid the learning process of students. Professors in colleges & teachers in schools take the help of audio-visual techniques to prepare lesson plans for children. For this, they use Microsoft PowerPoint to prepare electronic presentations about their lectures.

These electronic presentations can be shown on multimedia and sound projectors in classrooms. It is an interesting and simple method to learn for students. Multimedia (Sight and sound) presentations are easy to deliver for teachers also as these presentations spare a great deal of time and effort.

Computers can be used for online education & research. With the help of the internet, students can find useful information about their projects, and assignments and also can take useful help from other researchers as they store & organize their research materials on computers.

In CBT (Computer Based Training), various projects & educational programs are prepared or set up with the assistance of expert educators and audio-visual media help. These educational programs are generally set up in the shape of lectures on a specific subject/ topic & are given on CDs. Students can learn when they wish at their homes.

Benefits Of Computer Education :

It enhances creativity & thinking skills.
Provides efficient & better use of IT Technology.
Proves beneficial for career aspiration.
Improves research work & helps in communicating with different education providers.
Gives instant information on any topic in just a single click, & many more.

1. Huge & Organized Store Of Information

Vast or Immense storage is yet another great characteristic of a computer. Students and teachers can download and store a lot of educational materials, books, presentations, lecture/ address notes, question papers, and so on on computers.

Students can find many different ways to solve a certain problem given to them. Through the Computer, they can interact with people having the same issues & decisions.

2. Quick Processing Of Data

Speed is the fundamental attribute of a computer. We can easily find information with just a single touch of a button.

3. Audio-Visual Guides in the Teaching Process For A Viable Learning

One of the primary uses of computers in education is ‘Access to the Internet’ for information search about any topic.

Appealing and Better introduction (presentation) of data through applications programming software like Microsoft PowerPoint to introductions for creating splendid presentations for lectures & notes.

4. Parents Can Know Their Ward’s Progress

The Computer has helped parents & guardians a lot as they can likewise know by checking every minute progress of their children through computers and the web by browsing the school’s website. They can check different assessment results, attendance reports, participation in curricular and co-curricular activities, and significantly more.

5. Quick Communication & Correspondence

Another main advantage of using computers in the education field is the improvement in the quality of the teaching-learning process and communication between students & teachers. For this, they use Microsoft PowerPoint to prepare electronic presentations about their lectures.

Computer revolutionizes the way of study while making education smoother and quicker. It also connects us to different sources, which show us different ways to understand a particular topic or idea. In general, a computer has helped the education world and also has changed the way we work & learn.

Also Read, Role of Social Media In Education

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Essay on Computer Education

Students are often asked to write an essay on Computer Education in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Computer Education

Introduction.

Computer education is the process of learning about the use of computers. It involves understanding the basic concepts of computer operations.

In today’s digital world, computer education is essential. It helps us in completing tasks efficiently, saving time and effort.

Computer education enhances creativity and encourages logical thinking. It also opens up a wide range of opportunities in different fields.

Therefore, computer education is a crucial part of modern education. It equips students with necessary skills for the future.

250 Words Essay on Computer Education

The necessity of computer education.

In the 21st century, computer literacy is no longer a luxury but a necessity. It is a vital tool in the globalized world, where information processing and management are pivotal. Computer education enhances the understanding of complex problem-solving, logical reasoning, and creativity, which are essential skills in the current job market.

Impact on Learning

Computer education has revolutionized the learning process. With e-learning platforms, students can access a plethora of resources anytime, anywhere. It promotes self-paced learning, allowing students to learn at their convenience.

Role in Professional Fields

In professional fields, computer knowledge is indispensable. Whether it’s medical diagnostics, architectural designing, financial analysis, or scientific research, computers play a critical role. Hence, computer education is a stepping stone to a successful career.

In conclusion, computer education is an essential part of modern education. It equips students with necessary skills to navigate the digital world and opens up a myriad of opportunities in various professional fields. Therefore, it is not just about learning to use a computer, but understanding its potential to create, innovate, and transform the world.

500 Words Essay on Computer Education

Computer Education, in the modern era, is not just a luxury but a necessity. It refers to the process of learning about the fundamental concepts of computers, their operations, and applications. This education is a vital tool for the comprehensive development of individuals and societies, fostering innovation, creativity, and problem-solving skills.

The Importance of Computer Education

Integration of computer education in curriculum.

The integration of computer education into the curriculum enhances the learning experience. It provides a platform for students to explore, learn, and create. Teaching programming languages, data analysis, artificial intelligence, and machine learning at the college level prepares students for the future, equipping them with the necessary skills to thrive in the digital economy.

The Role of Computer Education in Career Development

Computer education opens up a plethora of career opportunities. From software development, data analysis, cybersecurity to AI specialist, the list is endless. Proficiency in computer skills not only increases employment prospects but also fosters innovation and entrepreneurship.

Challenges in Computer Education

In conclusion, computer education is a critical component of modern education. It equips students with the necessary skills to navigate the digital world, fosters creativity, and opens up numerous career opportunities. Despite the challenges, efforts should be made to integrate computer education into the curriculum and ensure its accessibility to all. The future belongs to those who are prepared to embrace the digital revolution, and computer education is the key to unlocking that future.

That’s it! I hope the essay helped you.

Happy studying!

Computers in Education

Computers in education are used to provide Audio-Visual learning, research, online learning, maintain records, make documents, and learn new evolving technologies.

10 Uses of Computer in Education

1. new era of classroom teaching .

Audio-Visual teaching on Computer

Computers have evolved and enhanced Teacher-student interaction. Now teachers can teach and demonstrate the practical aspects of theories to the students with the help of various tools in Computers. Students are now able to easily understand various difficult topics in an interesting way.

2. Easy to access information

Internet on Computer

In the contemporary educational system, computer education is crucial. Internet research is more convenient for students than looking for information in large books. Information is considerably more easily accessible and available on the internet. Computers make it simpler to store information than to keep handwritten notes when it comes to retrieval.

3. Online Learning

Online learning on Computer

The educational environment has been transformed by online learning . Distance learning is now a possibility because of computer technology. Education is not just about classrooms anymore. Thanks to computers, it has spread widely. Therefore, even if they are not in the same location, students and teachers may still interact effectively. They can study in the convenience of their own homes and change their schedule as needed.

4. Student Research

Student research on Computer

With the aid of the internet, we are able to do an advanced study on computers. This form of research simply entails utilising a computer to access the world wide web libraries and a variety of websites that are all connected to the internet.

5. Learn New Technologies

Learning new technology on Computer

As the technologies are constantly evolving, the use of Computers in education enables the students to learn new tools and knowledge that will prepare them for the potential technological changes in the coming time.

6. Simplify Record Keeping

Student records on Computer

A computer can be used as a tool for managing data on schools, enrollment, courses, exams, results, infrastructure, finances, library records, etc.

7. Easy to Create Any Documents

Documents on Computer

Students do not need a paper copy of any documents because they may quickly make them on a computer. One of the greatest programs for students to use to produce various types of documents, including resumes, notes, and presentations, is Microsoft Office.

8. Online Library

Library on Computer

In the era of Computers, you do not have to visit the library. There are a lot of online libraries available these days; you can effortlessly read books from the online library with the help of the Computer.

9. Track the performance of students

Monitoring progress on Computer

The computation of the marks that are formulated with the aid of a Computer is uploaded on the school's website. The parent and teacher can very easily check the performance of students.

10. Computer-Based Training (CBT)

Training on Computer

With the aid of knowledgeable educators and audio-visual media, numerous projects and educational programs are created or set up for CBT (Computer Based Training). These instructional programs are often delivered on CDs in the form of lectures on a particular subject or topic. Students are free to study whenever they choose at home.

Solved Questions

1. What are the advantages of using Computers in Education?

Ans: The main advantages of using Computers in education are:

Storage of information about student records.

Quick data processing of tests and Exams.

Audio-visual help in teaching.

Better management of information.

Use of the Internet.

Rapid communication among students, teachers, and parents.

2. Give five applications of Computers in education?

Ans: The five main applications of Computers in education are:

Easy to maintain records of the attendance, marks, and performance of the students.

Effortlessly look for any topic-related information in minutes with the help of the Computer.

The computer enables Distance teaching.

Computer-based online training, it’s a low-cost solution for educating people.

Modern technology is used in the education method so that students can without difficulty understand any topic.

3. How is a Computer used in making PPT ?

Ans:

Step 1: Launch PowerPoint from the start menu.

Step 2: Choose New from the left pane.

Step 3: Choose an option: Select Blank Presentation to start from scratch when making a presentation.

Step 4: Choose one of the templates if you want to utilize a ready-made design.

Step 5: Choose Take a Tour, then choose to Create to get some PowerPoint pointers.

Learning by Doing

Choose the correct answer:.

1. Which of the statements is correct?

a. A computer is ineffective as an instructor.

b. The computer is used to store student information.

c. Exam papers are checked on computers as well.

b and c

a and c

All of these

None of these

2. In schools, what are computers used for?

Sending money

Keeping records

Watching movies

Write True or False:

1. In schools computers are used to access the internet. (T/F)

2. Online learning is not possible on Computers. (T/F)

3. Computers help in research. (T/F)

4. Computers do not help in Audio-visual learning. (T/F)

The computer may be used as a teacher or tutor. Through educational CDs, a computer can instruct nearly any topic in an easier and more engaging way. It's fun to learn with the aid of a multimedia computer. On computers, we can create scientific diagrams and resolve mathematical sums.

Computers may be used to store significant historical and scientific information. Computers may be used to create and print question papers, mark lists, letters, posters, and banners as well as to keep track of things like student fees and attendance information. Computers are also employed in the field of education to develop syllabi and timetables, verify test papers, and create results.

FAQs on Using Computers in Education

1. How are students using computers now?

Computers are one of the most important educational resources, they can be used for so many beneficial purposes. Students now have access to a lot of knowledge thanks to computers and the internet, which may help them improve their research and communication abilities while preparing them for future employment in a workforce that depends more and more on computer technology.

2. How are computers used in education?

3. How have computers helped in online education?

The educational environment has been transformed by online learning. Distance learning is now a possibility because of computer technology. Education is not just about classrooms anymore. Thanks to computers, it has spread widely. Therefore, even if they are not in the same location, students and teachers may still interact effectively. They can study in the convenience of their own homes and change their schedule as needed.

Navigating the metaverse: unraveling the impact of artificial intelligence—a comprehensive review and gap analysis

Open access
Published: 20 August 2024
Volume 57 , article number 264 , ( 2024 )

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Mohammed A. Fadhel 1 ,
Ali M. Duhaim 2 ,
A. S. Albahri 3 ,
Z. T. Al-Qaysi 4 ,
M. A. Aktham 4 ,
M. A. Chyad 5 ,
Wael Abd-Alaziz 1 ,
O. S. Albahri 6 , 11 ,
A.H. Alamoodi 7 , 8 ,
Laith Alzubaidi 9 , 10 ,
Ashish Gupta 10 &
Yuantong Gu 9 , 10

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In response to the burgeoning interest in the Metaverse—a virtual reality-driven immersive digital world—this study delves into the pivotal role of AI in shaping its functionalities and elevating user engagement. Focused on recent advancements, prevailing challenges, and potential future developments, our research draws from a comprehensive analysis grounded in meticulous methodology. The study, informed by credible sources including SD, Scopus, IEEE, and WoS, encompasses 846 retrieved studies. Through a rigorous selection process, 54 research papers were identified as relevant, forming the basis for a specific taxonomy of AI in the Metaverse. Our examination spans diverse dimensions of the Metaverse, encompassing augmented reality, virtual reality, mixed reality, Blockchain, Agent Systems, Intelligent NPCs, Societal and Educational Impact, HCI and Systems Design, and Technical Aspects. Emphasizing the necessity of adopting trustworthy AI in the Metaverse, our findings underscore its potential to enhance user experience, safeguard privacy, and promote responsible technology use. This paper not only sheds light on the scholarly interest in the Metaverse but also explores its impact on human behavior, education, societal norms, and community dynamics. Serving as a foundation for future development and responsible implementation of the Metaverse concept, our research identifies and addresses seven open issues, providing indispensable insights for subsequent studies on the integration of AI in the Metaverse.

Artificial intelligence powered Metaverse: analysis, challenges and future perspectives

Exploring the Landscape of Metaverse: A Comprehensive Analysis of Existing Research and Future Research Directions

Translating Virtual Reality Research into Practice as a Way to Combat Misinformation: The DOVE Website

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1 Introduction

The notion of the Metaverse has recently garnered significant attention and fascination from individuals globally (Cipresso et al. 2018 ; Stephenson 1992 ). The term “metaverse” was introduced by Neal Stephenson in his science fiction novel "Snow Crash" and has since gained popularity through its portrayal in films such as “Ready Player One” (Anon 2023 ). It refers to a virtual reality environment surpassing the physical world’s limitations (Anon 2023 ). The digital world presents an expansive interconnected domain where individuals can engage in diverse activities, interact with others, and fully immerse themselves (Soliman et al. 2024 ). The Metaverse is a dynamic virtual environment that seamlessly incorporates augmented reality, virtual reality, and internet technologies (Wang et al. 2024 ; Carrión, 2024 ). “metaverse” refers to a complex network of interconnected virtual spaces, worlds, and experiences rather than a singular platform or application. Consider a hypothetical scenario where individuals have the ability to transport themselves to vibrant urban centres instantaneously, traverse imaginative terrains, participate in simulated musical performances, partake in immersive gaming encounters, or construct and manipulate personalized digital domains (Wang et al. 2022 ). Within the Metaverse, individuals have the ability to embody digital avatars, which are personalized depictions of themselves, thereby facilitating engagement with the virtual environment and other users. The avatars can be customized to reflect the users’ distinct characteristics, inclinations, and ambitions. The Metaverse strives to achieve a heightened sense of presence by leveraging sophisticated technological capabilities such as haptic feedback, motion tracking, and realistic graphics, thereby blurring the boundary between the physical and digital worlds. A central focus on collaboration and social interaction characterizes the metaverse experience. Individuals hailing from diverse geographical locations have the ability to converge, interact, and exchange personal encounters instantaneously. The Metaverse facilitates a global sense of community and connectivity through virtual meetings, collaborative projects, or socializing with friends (Bansal, 2024 ).

The phenomenon extends beyond geographical boundaries, allowing individuals to establish significant connections and cultivate relationships regardless of their physical location (Anon 2023 ). The Metaverse offers a plethora of opportunities for commerce, entertainment, and education without any apparent limits. Enterprises have the capacity to create virtual storefronts and offer immersive shopping experiences. In contrast, artists and creators have the opportunity to exhibit their work and interact with their audience in novel and stimulating manners (Anon 2023 ). Virtual events, concerts, and conferences have the potential to reach a worldwide audience without the limitations of physical locations. Academic institutions have the potential to utilize the Metaverse as a tool for developing interactive learning environments, which can facilitate students’ exploration of various subjects through immersive and captivating means. Furthermore, scholarly inquiry has extensively investigated the incorporation of AI and blockchain technologies into the Metaverse, in addition to virtual reality (Mu et al. 2024 ). These studies explore the implementation of AI applications within virtual environments and the potential for blockchain-based technologies to improve the capabilities of the Metaverse. Through an analysis of the convergence of these technologies, scholars endeavour to furnish a holistic comprehension of their contributions to the formation of the Metaverse and the realization of its potential utilities.

Additionally, the scholarly inquiry has been undertaken to comprehend the technical intricacies and system architecture necessary for the efficacious establishment and functioning of the Metaverse (Suo et al. 2024 ). The aforementioned studies tackle crucial concerns about the precision of motion capture, the stability of systems, and the safeguarding of data and privacy in virtual environments. Through comprehensive exploration of these facets, scholars endeavour to develop resilient and effective systems that can facilitate the multifarious functionalities of the Metaverse. Besides technical aspects, scholarly investigations have also delved into the ethical and cultural ramifications of the Metaverse (Cao et al. 2024 ). Scholars have conducted thorough investigations into the intersection of simulation and reality, elucidating the workings of novel profit models and their effects on cultural production. Through the analysis of practical applications and conceptual models, scholars endeavour to attain a more profound comprehension of the ethical facets of the Metaverse and its wider societal ramifications (Gokasar et al. 2023 ; Mohammed et al. 2023 ). The Metaverse harbours prospects surpassing entertainment and technology, as evidenced by its potential to offer substantial opportunities in the healthcare sector (Mozumder et al. 2022 ). Research has been conducted to examine the amalgamation of the IoT, AI, and other technological advancements within the Metaverse to enhance healthcare services (Saihood et al. 2024 ; Alammar et al. 2024 ). By utilizing the functionalities of this digital world, scholars aim to augment patient-focused healthcare, medical pedagogy, cooperation, and inclusivity in the healthcare sector. Additionally, scholars have conducted investigations into the social and cultural aspects of the Metaverse, examining its influence on the development of identity and interpersonal relationships (Cheng et al. 2022 ). The present research endeavours to scrutinize the ways in which individuals maneuver and project their personas in digital worlds while assessing the potential consequences for their self-concept and interpersonal connections. Researchers aim to gain insight into the transformative capacity of the Metaverse, in terms of shaping human behaviour and societal norms, by comprehending the dynamics of social interactions within this virtual world. Moreover, the economic facets of the Metaverse have garnered considerable interest. Research has been conducted to explore the possibility of novel business models and sources of income in this digital world (Jafar et al. 2024 ). Scholars examine the potentialities of virtual commerce, digital assets, and virtual currencies alongside the difficulties linked to commercializing and safeguarding intellectual property rights.

The objective of these investigations is to offer perspectives on the economic mechanisms of the Metaverse and their probable consequences for sectors such as gaming, entertainment, and e-commerce. Furthermore, the education sector has been investigating the incorporation of the Metaverse into educational settings. Scholars have investigated using virtual reality and immersive encounters to augment educational methodologies (Rogdakis et al. 2024 ). The authors investigate the potential of the Metaverse in facilitating interactive and immersive learning environments that promote collaboration, creativity, and knowledge acquisition. The objective of these investigations is to reveal the capacity of the Metaverse to revolutionize conventional pedagogical methods and equip students with the necessary skills for the contemporary digital era. Additionally, scholars have explored the potential of the Metaverse in tackling societal issues and advancing inclusiveness. The authors investigate the potential of virtual environments in mitigating geographical and social disparities, as outlined in (Chuang et al. 2021). The Metaverse has the capacity to facilitate cultural exchange, collaboration, and understanding among individuals from various backgrounds by offering accessible and immersive experiences. The objective of these investigations is to examine the societal ramifications of the Metaverse and its capacity to foster a more comprehensive and interconnected community. Recently, in the discourse of the metaverse concept, Apple’s Vision Pro presentation employed a distinctive methodology in contrast to other corporations such as Meta. Apple’s approach was centred on leveraging their pre-existing applications to augment the user’s experience rather than prioritizing virtual environments and total immersion. The Vision Pro headset was introduced to enhance routine tasks and endeavours.

During its presentation, Apple demonstrated the potential of mixed reality technology to enhance FaceTime functionality Anon (2022a). This innovation lets users simultaneously engage in a video chat while accessing other pertinent windows. The authors additionally exhibited the utilization of Safari in a virtual environment, wherein numerous sizable windows were exhibited, thereby obviating the necessity for multiple monitors. The availability of several widely used applications such as photo browsing, Disney + streaming, Microsoft Office, and Adobe Lightroom on VisionOS has been confirmed by Apple. Apple’s strategy distinguishes its mixed reality headsets from others by making users perceive that the device would yield tangible advantages in their everyday routines. Despite persistent concerns regarding the elevated cost and restricted battery longevity, Apple has prioritized marketing the Vision Pro headset and the comprehensive notion and potential of spatial computing. In contradistinction to the nomenclature "metaverse" employed by other corporations, Apple has introduced the phrase "spatial computing" as their chosen terminology. The notion appears to be more tangible and comprehensible, given its emphasis on incorporating digital material into the physical world, as opposed to developing a wholly virtual environment. The notion of spatial computing has been posited as a more compelling prospect for the future of computing when juxtaposed with the previously promoted concept of the Metaverse. The marketing strategy employed by Apple exhibited a comprehension of user requirements and a dedication to the smooth incorporation of technology into daily routines (Anon 2023 ). To conclude, the notion of the Metaverse has generated significant interest and scholarly inquiry across diverse fields. The investigation of virtual reality technology, artificial intelligence, blockchain, healthcare, societal implications, and other related areas has brought to the fore the extensive possibilities and difficulties linked with this digital world Anon (2022b). Scholars strive to comprehend the ramifications of this phenomenon on various aspects of human conduct, financial systems, academic institutions, societal norms, and the broader community. The results of these investigations provide a foundation for the future advancement, application, and conscientious execution of the Metaverse.

The correlation between AI advancements and the emergence of the Metaverse is unmistakable, but it’s crucial to recognize AI’s causative role in shaping and enabling its development. The interaction between AI and the Metaverse is an important topic of research, particularly since AI has a significant impact on the Metaverse’s development and functioning. When looking at the impact of AI on Metaverse realisation, it is crucial to discern between correlation and causality. While it is widely acknowledged that AI technologies are intrinsically tied to the development of the Metaverse and play an important role in enabling many of its features and capabilities, it is necessary to recognise that correlation does not necessarily imply causation. In other words, just because AI and the Metaverse are inextricably linked does not mean that AI is the fundamental cause of their formation or development. Instead, further study is required into the specific ways in which AI contributes to the Metaverse’s realisation, such as an analysis of the causal links between AI technologies and various aspects of the Metaverse’s design, operation, and user experience. This might entail investigating how AI-powered algorithms manage virtual environments, streamline user interactions, tailor experiences, generate content, and increase immersion in the Metaverse (Soliman et al. 2024; Fadhel et al. 2024a , b , c ).

The developing Metaverse is inextricably tied to advances in AI, a connection destined to transform digital interaction. At its heart, AI enables the seamless integration of virtual surroundings with human encounters. Users may easily interact with virtual creatures, navigate immersive environments, and collaborate with others using spoken or written language thanks to powerful NLP and conversational AI. Furthermore, AI-powered virtual characters and NPCs improve realism by reacting dynamically to user activities and participating in realistic discussions. This integration of AI and the Metaverse goes even further, as machine learning algorithms analyse user behaviour to personalise experiences, produce varied virtual worlds using procedural generation methods, and govern virtual economies using predictive analytics. In the world of immersive experiences, AI enhances VR surroundings with realistic physics, animations, and visuals, creating a stronger sensation of presence. Furthermore, AI-powered social algorithms foster communities in the Metaverse by suggesting content and enabling meaningful interactions (Zhuk 2024 ). The contributions of AI in the Metaverse can be summarized as follows:

Incorporating AI into the Metaverse facilitates a heightened level of immersion by generating captivating and lifelike virtual environments. Integrating AI-based elements, such as dynamic environments, intelligent NPCs, and interactive objects, significantly enhances the overall user experience and interaction quality.

AI holds a pivotal position in the Metaverse, enabling the progression of complex NPCs with lifelike behaviours and decision-making capabilities. As a result, this phenomenon gives rise to the development of virtual environments that exhibit increased interactivity and captivation, consequently augmenting the overall user experience.

The AI algorithms employed in the Metaverse are designed to examine user preferences and behaviours to produce personalized content customized to individual interests. This process ultimately enhances the relevance and enjoyment of virtual experiences for users.

Utilizing AI-powered chatbots and virtual assistants within the Metaverse is essential as they facilitate seamless and genuine social interactions among users. This, in turn, fosters a sense of community and elevates user engagement levels.

AI is critical in optimizing resource allocation and utilization within the Metaverse, ensuring the efficient distribution of computational power, storage, and network resources, and enhancing overall performance and scalability.

The AI algorithms utilized by the Metaverse proficiently analyze large volumes of data in real-time, enabling the capability to adjust to virtual environments based on user interactions, feedback, and changes in the environment.

The incorporation of AI technologies within the Metaverse significantly impacts the preservation of safety protocols and the observance of ethical standards. AI-driven content moderation, detection and prevention of harmful activities, and safeguarding users’ privacy and personal information are essential roles fulfilled by social media platforms.

This paper makes a significant contribution by conducting a thorough analysis of studies to create a comprehensive taxonomy of AI in the Metaverse. Emphasizing the importance of trustworthy AI, the paper underscores its role in enhancing user experiences, safeguarding privacy, and promoting responsible technology use within the Metaverse. Also, we aim to dissect the causal mechanisms underlying AI’s influence on the Metaverse, shedding light on its profound impact and ethical considerations. Furthermore, it highlights the substantial scholarly interest and research on the Metaverse, examining its implications for human behaviour, education, societal norms, and community. The findings not only serve as a foundation for further development and implementation of the Metaverse concept but also identify and address seven critical open research issues, providing valuable insights to guide future studies on the integration of AI in the Metaverse.

The organization of this paper is structured in the following manner. The technique of doing a systematic literature review is outlined in Sect. 2 . Section 3 encompasses three comprehensive scientific mapping studies that use a bibliometric approach to identify trends and deficiencies within the existing literature, therefore enhancing the understanding of the subject matter. Section 4 delineates the study’s findings, with particular emphasis on seven significant domains. Section 5 delves into the enrichment of incentives, challenges, and recommendations pertaining to AI inside the Metaverse. Section 6 of the paper undertakes an evaluation of five distinct traits and identifies research gaps within the domain of AI in the Metaverse. This analysis serves as a foundation for proposing potential areas of investigation and advancement in the future. Lastly, the concluding section, Sect. 7 , brings this contribution to a conclusion.

2 Methodology

The methodology used in this work followed the recommended reporting guidelines for a systematic review and meta-analysis, as seen in Fig. 1 (Khaw et al. 2022 ; Sohrabi et al. 2021 ). The research approach included using many bibliographic citation databases, including various medical, scientific, and social science articles across various disciplines. The researchers used four prominent digital databases, namely SD, Scopus, IEEE, and WoS, to conduct a comprehensive search for the desired articles. These databases provide valuable insights to scholars by offering comprehensive coverage of research across several scientific and technology fields.

Depicts an overview of the technique used to discover, select, and incorporate essential contributions

2.1 Search methodology

A comprehensive search was conducted across four databases, namely SD, Scopus, IEEE, and WoS, in order to identify academic papers written in English. This search included all articles published from the beginning of scientific output until April 2023. The search conducted in this study used a boolean query consisting of a single operator (AND) to connect the terms "metaverse" and " Artificial Intelligence " (refer to Fig. 1 ). These keywords were selected by the collaboration of domain specialists specializing in AI and Metaverse. There are further prospects for using metaverse components inside artificial intelligence, including augmented and virtual reality.

2.2 The concept of inclusion and exclusion criteria

Within systematic literature reviews, the inclusion and exclusion criteria have a crucial function by offering explicit guidance for the selection of research based on specified criteria. These criteria are crucial for ensuring that the studies included in the review are in line with the study aims and scope, thereby improving the rigour and relevance of the results. The current research took into account the following criteria:

The article was authored in English and published in either an academic journal or a conference paper.

The chosen research must have a strong correlation with both the metaverse and the components of Artificial Intelligence.

The article should enhance data fusion in the metaverse by using Artificial Intelligence and ML/DL, guaranteeing information of superior quality and free from errors.

On the other hand, the article’s concentration and significance were maintained by using exclusion criteria to exclude research that did not fit within the specified scope.

Articles are written in a non-English language.

This article specifically examines the metaverse and Artificial Intelligence, excluding studies that merely briefly touch upon these topics.

Any review or empirical research that do not include a significant recommendation related to a particular hypothesis are rejected.

2.3 Study selection

Similar to previous studies (Khaw et al. 2022 ; Sohrabi et al. 2021 ) this study used the PRISMA guidelines to conduct a comprehensive literature review. This technique consists of many steps, with the first stage removing duplicate documents. The Mendeley tool was used to conduct a comprehensive scan of the titles and abstracts of the submitted materials. This methodology included the whole of the writers and entailed the removal of several irrelevant literary works. The author addressed and resolved inconsistencies and conflicts within their work. The subsequent phase included thoroughly reviewing the whole text and eliminating content not aligning with the predetermined inclusion criteria. Three experts evaluated the filtering technique’s effectiveness (refer to Fig. 1 ).

The papers that satisfied the specified criteria were included in this research. The search yielded 846 papers, with the majority (523) sourced from SD. Scopus contributed 162 articles, while IEEE and WoS accounted for 86 and 75, respectively. The search included all publications from the inception of scientific output until April 2023. After removing around 45 duplicate entries from the four databases, the overall count of articles was reduced to 801. Upon careful examination of the titles and abstracts, 706 articles were excluded from consideration. A thorough and rigorous assessment of the remaining 95 submissions found that 41 articles needed to meet the eligibility requirements. Consequently, only 54 research papers were judged relevant and subsequently included in the final selection of publications based on the predefined inclusion criteria. The subsequent part delineates using several bibliometric methodologies to monitor the analysis of acquired articles.

3 Comprehensive science mapping analysis

Numerous researchers have proposed methodologies to enhance the comprehensiveness of scientific mapping analysis by using R-tool and VOSviewer (Wu and Zhang 2024 ; Fadhel et al. 2024). These approaches aim to improve the transparency of presenting the findings from the 54 investigations. The bibliometric technique is characterized by its excellent reliability and transparency, enabling the production of dependable results, identification of research gaps, and derivation of conclusions from the existing literature. Therefore, the bibliometric technique outlined in the subsequent subsections was used in this investigation.

3.1 Annual scientific production

Over the previous decade, the Metaverse has grown significantly, including AI advancements. Figure 2 depicts the annual scientific output, demonstrating the evolution of prior theoretical and practical investigations on the Metaverse. Figure 2 displays the yearly academic output concerning AI’s influence on the Metaverse. The amount of publications has increased significantly in recent years, as seen by the small number of papers published in 2011 and 2013. The number of articles increased steadily during 2021, reaching a high of 39 articles in the following year, 2022. The aforementioned trend held true for 2021 and 2022, with 3 and 39 papers published, respectively. As of now, in the early stages of the year of 2023, the number of articles published stands at a modest nine. The available statistics show a steady and ongoing growth in scholarly papers about the Metaverse.

Annual scientific production

During the years 2021 and 2022, the Metaverse witnessed a significant resurgence, which was propelled by technological progress and industry momentum, resulting in a surge in scholarly investigations. Nevertheless, progress reached a halt in 2023 as a result of technological impediments, regulatory ambiguity, and alterations in economic objectives. Notwithstanding these obstacles, academics persisted in participating in critical dialogue and cooperating to tackle ethical concerns in an attempt to actualize the Metaverse’s capacity for profound change.

3.2 Word cloud

Word cloud analysis has enabled the identification of the most prevalent and significant keywords within prior scholarly research. Figure 3 presents a comprehensive depiction of the essential concepts that have been derived from prior research findings. Its purpose is to summarize and reorganize the existing body of knowledge succinctly.

The relatively small size of keywords implies a reduced likelihood of their occurrence. Based on the term frequencies depicted in Fig. 3 , it is apparent that numerous significant topics pertaining to the ‘metaverse’ domain are frequently the subject of discussion. The subjects that demonstrate the highest frequencies are ‘artificial intelligence’, ‘deep learning’, ‘virtual reality’, ‘machine learning’, ‘blockchain’, and ‘augmented reality’. Moreover, the data presented demonstrate the substantial relevance of the topics of 'deep learning' and 'virtual reality' within the given domain. Other relevant terms that are closely linked to the subject matter encompass “image classification”, “immersive experience”, “virtual worlds”, “CNN”, “gesture recognition”, and “human–machine interfaces”. The notable prevalence of these terms highlights the importance of considering these factors when designing and deploying AI systems. Figure 3 showcases various applications of AI in diverse domains, such as ‘Avatar’, ‘3D human reconstruction’, ‘3D model’, ‘3D point cloud’, and ‘3D virtual world’. Additionally, the text emphasizes different methodologies utilized within the domain of AI, such as CNNs, natural language processing, and robotics. The examination of the word cloud generated from Metaverse based on AI papers reveals a wide-ranging scope within the discipline, encompassing various subjects such as the technical aspects of AI and strategies for its implementation.

3.3 Co-occurrence

A co-occurrence network is an additional tool utilized in bibliometric analysis. Previous scholarly investigations have undertaken the task of identifying and examining commonly utilized terms and conducting their analysis. The aforementioned studies have primarily concentrated on examining a semantic network, which has proven to be a valuable resource for professionals, policymakers, and scholars in understanding the conceptual framework within a particular knowledge domain. The data presented in Fig. 4 pertains to a co-occurrence network that has been constructed utilizing the titles of scholarly articles focused on Metaverse based on AI.

Co-occurrence network

The network comprises nodes, which symbolize the individual words in the titles. The edges that establish connections between the nodes represent the frequency with which these words co-occur within the same title. The diagram in Fig. 4 depicts several nodes and their corresponding clusters and closeness centrality values. These values measure the degree of interconnectedness between a node and other nodes in the network. The nodes demonstrate a noticeable arrangement, displaying 14 separate clusters. Every cluster exhibits a set of words with a thematic or conceptual association with Metaverse AI. Cluster 1 comprises Metaverse, artificial intelligence, blockchain, immersive experience, virtual worlds, 2D to 3D converter, 3D point cloud, 3D virtual world, avatar, and content delivery networks. The aforementioned terms signify that the cluster is associated with applying metaverse AI systems in medicine. Cluster 2 comprises terms such as ‘deep learning’, ‘virtual reality’, ‘machine learning’, ‘3D model’, ‘anomaly detection’, ‘big data’, and ‘body motion recognition’, suggesting that this cluster is associated with the various aspects of artificial intelligence. Similarly, additional clusters demonstrate connections with topics such as augmented reality, reading in AR, and asymmetric virtual environments. Determining a node’s centrality in a network is based on its closeness, which can be understood as a metric of its importance. Words that have higher closeness values demonstrate stronger connections to other nodes in the network, indicating their greater centrality about the topic of metaverse AI. The diagram succinctly depicts the interconnectedness between concepts and terminologies related to metaverse AI, as evidenced by the titles of scholarly articles in this field. The information provided has the potential to help understand the current state of research in this field and identify areas that require further investigation.

4 Results and analysis: a taxonomy

Metaverse was built using the conducted technique, and the final collection of articles met the considered inclusion and exclusion criteria. Furthermore, based on objective evidence from studies that met these criteria, the 54 publications were divided into seven basic categories (see Fig. 5 ). These categories are related to:

Metaverse in VR: including 17 of 54 papers.

Metaverse Integration with AI and Blockchain: including 4 of 54 papers.

Agent-Based Systems and Intelligent NPCs in the Metaverse: including 3 of 54 papers.

Metaverse’s Societal and Educational Impact: including 6 of 54 papers.

HCI and VR Applications in the Metaverse: including 16 of 54 papers.

Systems Design and Technical Aspects of the Metaverse: including 4 of 54 papers.

Metaverse Reviews: including 4 of 54 papers.

AI Integration in metaverse taxonomy

4.1 Metaverse in VR

Virtual Reality and the Metaverse are transforming the digital landscape by combining physical and digital worlds. Virtual reality immerses users in realistic simulated environments, while the Metaverse offers a shared virtual space for real-time interaction and transcends traditional limitations. These technologies promise to revolutionize industries and redefine digital experiences, with Virtual Reality as a fundamental component of the Metaverse. The literature explores this connection in 17 out of 64 articles discussing the potential of virtual reality within the Metaverse universe. Researchers have proposed various algorithms and models to enhance virtual environments and human reconstructions. (Su et al. 2022 ) presents a 3D human reconstruction algorithm, combining facial features and 2D image features to predict 3D human body parameters. The proposes a learning model in (Arroyo, Serradilla, and Calvo 2011) merging evolutionary computation and fuzzy controllers to optimize the movement of metabots, enhancing their autonomy and interaction in virtual worlds. (Fan, Chiu, and Chang 2022) introduces an algorithm for automatic depth information map generation using overlapping lines. Furthermore, (Park et al. 2022 ) proposes a grouping algorithm to secure topics for security and safety within the Metaverse, improving topic-based models’ accuracy. Researchers address privacy and identity concerns in the Metaverse by developing a superior finger vein recognition system using deep learning and anti-aliasing techniques (Tran et al. 2023 ). They also create an AI-based system to teach ArSL using avatars in AR and VR, benefitting people with hearing loss (Batnasan et al. 2022 ). Another study focuses on using AR to distinguish emotional states during book reading activities using EEG signals, demonstrating high classification performance (Dasdemir 2022 ).

The potential data-related issues and power inequities in VR technology are examined using Facebook’s Oculus VR as a case example. Three studies (Egliston and Carter 2021 ; Jian et al. 2022; Sun et al. 2023 ) discuss the impact of VR on society and human existence. They highlight concerns such as exacerbating wealth inequality, algorithmic bias, and digital exclusion, calling for regulatory intervention. Additionally, they explore the potential risks of addictive dependence and escapism while acknowledging the transformative possibilities of the "Metaverse." One study (Cho et al. 2022 ) focuses on designing a DAVE to optimize VR and AR experiences. Another aspect explored is the concept of hyperproduction, examining the implications of AI-generated media on cultural production. The study explores the convergence of simulation and reality in the context of rentier capitalism (Ferrari and McKelvey 2022). It presents four propositions for Metaverse tourism, emphasizing immersive experiences and multi-identification profiles (Koo et al. 2022 ). The research examines IoT, Blockchain, and AI use in medical healthcare within the Metaverse (Mozumder et al. 2022). Additionally, it discusses AI’s role in advancing Metaverse technologies (Cheng et al. 2022 ). The integration of Metaverse with extended reality technologies for healthcare improvement is also explored (Ahuja et al. 2023 ). Another paper focuses on speech interactions for aircraft maintenance in the Metaverse (Siyaev and Jo 2021).

4.2 Metaverse integration with AI and blockchain

Integrating AI, blockchain, and the Metaverse drives progress in the digital world. AI enhances immersive experiences with personalized content, while blockchain ensures transparency and ownership of digital assets. This synergy creates a dynamic ecosystem, fuelling gaming, finance, and commerce opportunities. Ultimately, this powerful amalgamation reshapes how people interact, transact, and navigate the digital world, forming a blockchain-based decentralized network of virtual worlds and 3D settings. Metaverse is a simple platform for anyone to develop their virtual world or 3D environment. In this context, this category includes 4 of 54 articles.

Authors in (Choi and Kim 2022 ) analyzed satisfaction with virtual object manipulation in the Metaverse based on MR, conducting experiments and assessing two properties: manipulation and virtual object. A study (Bouachir et al. 2022 ) explores AI’s potential in decision-making simplification, task automation, and Blockchain optimization in the Metaverse. It reviews Blockchain technology’s role and the impact of AI on intelligent Blockchain features, promising improved Metaverse ecosystem integration. In paper (Gupta et al. 2022 ) investigates AI’s role in the Metaverse’s establishment, exploring AI-based methods and potential applications and providing insights for researchers. Lastly, the study (Zhou 2022 ) reflects on a journal’s accomplishments, showcasing collaborative efforts between IEEE and the Chinese Association of Automation, serving as a model for future collaborations. It also talks about the possibilities of Metaverse and how it can be used in different fields.

4.3 Agent-based systems and intelligent NPCs in the metaverse

Agent-Based Systems and Intelligent NPCs play a crucial role in the Metaverse, enhancing immersive experiences. These systems use advanced AI algorithms to create lifelike virtual characters capable of interactive behaviour. NPCs in the Metaverse exhibit human-like behaviour, dynamically adapting and acquiring knowledge based on user interactions, resulting in engaging experiences. The integration of these technologies redefines virtual storytelling and interpersonal interactions, elevating the overall authenticity and engagement within the virtual environment. This category explains that virtual agents inhabiting the Metaverse will be self-contained, three-dimensional objects comparable to the non-realistic chatbots and speech bots currently in use and includes 3 of 54 papers.

The authors used DRL technology and the Metaverse to optimize emergency evacuations, offering a training system for evacuees to find the quickest route to exit buildings. They utilized sensor data for real-time building status monitoring (Gu et al. 2023 ). A multi-agent reinforcement learning framework was proposed to enhance intelligent non-player characters in the Metaverse, allowing personalized learning (Hare and Tang 2022 ). Additionally, a method was suggested to improve motion capture for CG avatars during interactions by supplementing a user’s motion with another person’s motion, resulting in more natural avatar movements (Suzuki, Mori, and Toyama 2022 ).

4.4 Metaverse’s societal and educational impact

The Metaverse is transforming society and education. This virtual environment changes how people interact, collaborate, and do business in a digitally connected world. Metaverse immersive learning experiences promote active engagement and knowledge retention in education. However, the effects of AI on privacy, digital identity, and social dynamics require careful regulation to maximize its potential for social and educational progress. Early technology applications show that metaverse education can be democratized. This category includes 6 of 54 articles.

The editorial will explain why interactive learning environment users may want to enter the Metaverse cautiously. To adequately outline these risks, the Metaverse must be defined, its technologies used, and how it can be used for learning and teaching (Rospigliosi 2022 ). They presented XIVA, an intelligent voice assistant for Chinese voice interaction in future educational metaverse systems. The open programming interfaces that allow third-party developers to create new voice commands and functions set XIVA apart. XIVA’s essential voice interaction and third-party extensions for smart classroom operation control are shown in the study (Lin et al. 2022 ). This study introduced a gesture recognition system using triboelectric smart wristbands and an AAL model. The wristbands’ anatomical design allows for highly sensitive and high-quality sensing, enabling accurate gesture recognition with low computational costs. The study shows that real-time somatosensory teleoperations can transform cyber-human interactions and provide immersive experiences (Fang et al. 2022 ).

Metaverse construction image classification can be improved with a CWCT transformer. CNN and transformers use an optimized Cross-Window Self-Attention mechanism to capture local and global features of high-resolution images, improving classification accuracy and model complexity (M. Li, Song, and Wang 2022 ). To examine how the metaverse age affects college students’ network behaviour and university ideological and political education. The study examines intelligent technology’s background, college students’ network behaviour, its multifaceted effects, and its nature to improve ideological and political education (Ge 2022 ). Implement a virtual world using GNU OpenSimulator and investigate metaverses in education. The researchers develop a metaverse-based expert systems course methodology that measures server performance and predicts server behaviour using time series analysis (Gonzalez Crespo et al. 2013 ).

4.5 HCI and VR applications in the metaverse

HCI and VR are merging to change how people use technology. HCI is the academic study of user interface design and implementation to improve human–computer interaction. VR technology simulates virtual environments to give users immersive experiences. HCI and VR convergence improves user experiences by enabling more genuine and immersive interactions. These technologies’ convergence could benefit gaming, education, and healthcare. HCI and VR advancements drive innovation and transform our engagements with digital content, improving our daily lives. The success of the Metaverse depends on HCI, mainly on how to feed user actions into the virtual environment. This category includes 16 of 54 articles.

A new study proposed an SDN IoT intrusion detection model under a 5G mobile network that combines traditional machine learning with deep learning to process traffic in linear sequence using DAE, GAN, and random forest for the future metaverse security problem. The DAE algorithm extracts and displays data features, the GAN algorithm optimizes and balances data, and the random forest algorithm classifies (Ding, Kou, and Wu 2022). Another research describes a skin-interactive electronic sticker in a hybrid cartridge (disposable bandages and non-disposable kits) that digitally decodes epidermal deformation. The gadget may be used in two ways: as a tiny electronic sticker with a thickness of 76 m and a node pitch of 7.45 mm for static body curvature measurement and as a wrist bandage for dynamic skin wave decoding into a colorful core-line map. This approach has a feedforward deep learning F1 score of 0.966 due to its high detection sensitivity in static mode and high accuracy of 0.986 in dynamic mode. By analyzing skin thicknesses and locations through picture segmentation, the gadget can decode 32 delicate finger folding actions, resulting in an optimum color map core line.

This method may help researchers better comprehend skin wave deflection and variations for wearable applications such as sensitive skin-related gesture control in the Metaverse, brain-degenerate rehabilitation programs, and digital medicine biophysical status detectors for body form and curvature (Hong, Lee, and Lee 2022 ). This research aims to create the AIOM touch sensor, a two-electrode multipoint touch sensor that can adapt to diverse setups effectively. This touch sensor can recognize, learn, and remember human–machine interactions, allowing for biometric authentication and interactive virtual object control (Wei et al. 2022 ). This study uses TTS and deep learning to create an intelligent non-contact gesture recognition system. The system accurately recognizes diverse, complex gestures without accessories or complex sensing platforms, making it suitable for touchless medical equipment, public facilities, smart robots, virtual reality, and metaverse (Zhou et al. 2022 ). This paper examines modern book layout design in colleges and universities in the educational Metaverse. The study emphasizes artistic value and creative language design strategies in book layout and proposes a layout analysis and text image preparation algorithm for university book layout design (Sun 2022 ). A simple, intelligent meta-learning system is being developed for the strengthened Metaverse during the COVID-19 pandemic. The study designs and creates a virtual learning environment using Open Simulator and Moodle to support students with different abilities, track their activities, and evaluate their performance, particularly in mathematics (Sghaier, Elfakki, and Alotaibi 2022). To investigate the possible uses of the Metaverse in healthcare and propose a metaverse of MeTAI to aid in the development, evaluation, and regulation of AI-based medical practices, notably in medical imaging. The research looks at use cases, challenges, and action items for developing the MeTAI metaverse to enhance healthcare quality, accessibility, cost-effectiveness, and patient happiness (Wang et al. 2022 ). The work presents a data-driven power coordination management strategy to increase PEMFC stability, performance, and efficiency.

To coordinate agents with distinct aims, the paper offers a metaverse-based multiagent double delay deep deterministic policy gradient (MET-MADDPG) method (J. Li, Yang, and Yu 2022 ): a DNN-based 3D-to-2D watermarking approach for Metaverse immersive material copyright protection. The research intends to assist authors of immersive material in protecting their copyrights and ownership (Park et al. 2023 ). To illustrate how the inherent anisotropy of tellurium nanowires influences electrical structure and piezoelectric polarization, allowing VR interaction and neuro-reflex. The researchers created a wearable glove with a bimodal Te-based sensor for improved somatosensory feedback in virtual reality, as well as successful stimulus recognition and neural reflex in a rabbit sciatic nerve model, demonstrating potential applications in the Metaverse, AI robotics, and electronic medicine (L. Li et al. 2022a , b , c ). To overcome electrode shifts in VR headsets that damage facial EMG based FER systems. The work suggests adopting covariate shift adaption approaches in the feature and classifier domains to increase system resilience and maintain high classification accuracy even when electrodes are removed and reattached, making fEMG-based FER more suitable for VR-based metaverse applications (Cha and Im 2022 ). This research aims to increase the training stability and speed of GANs in the Metaverse for human picture synthesis and modification. The study proposes novel methods for improving rendering and spectral normalization and using Residual Fast Fourier Transform Block and Wasserstein distance to improve GAN training stability and efficiency, demonstrating their effectiveness through experimental evaluations and achieving state-of-the-art image quality metrics (Wu et al. 2022 ).

This study aims to create a soft electronic glove compatible with skin and thermal transfer-printed. This glove will allow for seamless and natural interactions between people and XR equipment in the Metaverse. The glove identifies hand motions and operates VR applications in a customized shooting game utilizing low-cost, lightweight, and mass-producible materials (Xia et al. 2022 ). This research tackles the difficulties in identifying singers in the Metaverse induced by live effects. To enhance singer identification, the research employs MMD, gradient reversal (Revgrad), and CAN with CRNN. On the Artist20 dataset, CRNN-CAN produced cutting-edge F1 findings (X. Zhang et al. 2022a , b ). Additionally, VR, Metaverse, and AI technologies were employed to improve football education on mobile internet platforms. The paper presents a K-means-based optimal distribution approach for 360-degree panoramic VR football teaching videos. It assesses its efficiency using simulation experiments in order to enhance teaching and promote football teaching and smart learning (Li, Cui, and Jiang 2022). A hybrid system that combines AI-generated material with user-generated content generates interactive fiction and allows users to participate in narrative exchanges with the AI agent (Sun et al. 2022 ).

4.6 Systems design and technical aspects of the metaverse

Systems design and technology shape the Metaverse’s development and operation. The virtual domain needs resilient systems for network infrastructure, data storage, and security to handle its size and complexity. AI, blockchain, and cloud computing help create immersive experiences and instantaneous Metaverse interactions. To create a cohesive and sustainable ecosystem, scalability, interoperability, and standards must be analyzed. The Metaverse’s design and technical implementation affect its usability, stability, and growth. 3D design technologies can improve metaverse design and development, giving consumers more engaging and immersive experiences. This category includes 4 of 54 articles.

The VADER sentiment classifier improves online review sentiment analysis for mobile metaverse applications. The study compares machine learning classifiers using different embedding methods and finds that VADER-based classifiers outperform those that do not, with LightGBM and TF-IDF having the highest accuracy (Lee et al. 2022 ). This paper uses virtual and real methods to address transportation structure design, vision models, data security, and privacy in virtual transportation space. The paper examines how virtual transportation can manage these aspects to ensure functionality and safety (Zhang et al. 2022a , b ). This metaverse-based InfoMat study will create a digital-twin smart home. The InfoMat is used for smart home monitoring, position sensing, user identification, and virtual reality visualization to overcome unstable TENG output under environmental changes (Yang et al. 2023 ). Identify virtual 3D asset pricing factors and create a machine-learning model to predict them. The study highlights creators’ subjective assessments of virtual 3D assets as a significant factor in pricing behaviour (Korbel et al. 2022 ).

4.7 Metaverse reviews

Reviewing a topic emphasizes critical thinking. This study analyzes the topic’s pros, cons, and significance. Reviews help consumers, researchers, and audiences make informed decisions. Through feedback and constructive criticism, reviews help creators and providers improve products, services, and content. This category provides insight into previous works in the literature for a Metaverse review and includes 4 of 54 articles.

This study covers state-of-the-art head motion monitoring systems based on inertial sensors, including acquisition methods, prototype structures, pre-processing steps, computational methods, and validation methods. The study utilizes machine learning algorithms to monitor head motion and contextualizes inertial sensor technology for paralysis (Ionut-Cristian and Dan-Marius 2021 ). The study examines how AI has helped create and advance the Metaverse, a shared virtual world powered by emerging technologies. The study covers AI, including machine learning and deep learning, and explores AI-based methods in six metaverse-relevant technical areas and potential AI-aided applications in various fields. The survey concludes with critical contributions and metaverse AI research directions (Huynh-The et al. 2023 ) a metaverse survey based on Blockchain and AI. Digital currencies, virtual AI, and Blockchain technologies are integrated into the paper. These components are examined to understand how Blockchain and AI interact with the Metaverse (Yang et al. 2022 ). This study examines three components to create an immersive Metaverse like Ready Player One, Roblox, and Facebook. The authors emphasize films, games, and studies. They discuss how these platforms improve user experience and virtual social interaction. They also list social influences, constraints, and open challenges that must be overcome to implement an immersive Metaverse (Park and Kim 2022 ).

5 Discussion

This section examines three critical features of the Metaverse literature: motivations, challenges, and recommendations to reduce these issues to improve Metaverse quality.

5.1 Motivations

The convergence of the Metaverse and AI is fueled by various variables, each of which offers up new possibilities, see Fig. 6 . For starters, Horizons: The Synergy of AI and Advanced Technologies in the Metaverse is emerging. AI, paired with current technology, enhances user experiences by enabling personalized interactions, realistic NPCs, and content creation, propelling the Metaverse to new heights of immersion and engagement. The second theme is transforming Education and Medical Training Through the Metaverse. AI-powered adaptive learning and simulations revolutionize educational and medical training circumstances by giving personalized experiences that improve learning outcomes and medical skill development. Finally, in Metaverse and IoT: A synergistic integration of cutting-edge technologies, AI connects the Metaverse and the Internet of Things, creating an interconnected environment that combines virtual experiences with physical devices, resulting in intelligent automation, efficient management, and seamless integration of the Metaverse’s digital and physical worlds. Together, these motivations propel the Metaverse and AI forward, promising a future in which virtual experiences are more rich, transformative and interconnected.

Metaverse motivations

5.1.1 Emerging horizons: the synergy of AI and advanced technologies in the metaverse

In recent times, significant progress in AI has allowed the development of innovative approaches and substantial enhancements in the provision of established services and applications. The Metaverse has emerged as a beneficiary of technical advancements (Rospigliosi 2022). AI-based metaverse technology has significant potential to provide valuable assistance to those afflicted with various mental disorders. For instance, individuals diagnosed with BPD have a notably heightened propensity for engaging in suicidal behaviours. Individuals that engage in suicidal behaviour often do so due to experiencing symptoms of depression, anxiety, and impulsivity. An AI-driven conversational agent, functioning as a virtual assistant or companion, might serve as a reliable source of support for individuals, offering them a constant presence to address and ease the sometimes burdensome emotions they experience (Ahuja et al. 2023 ). The Metaverse leverages AI and blockchain technology to create a digital virtual environment that facilitates social and economic interactions beyond the limitations of the physical world. The integration of these advanced technologies is expected to expedite its development (Mozumder et al. 2022).

Due to the swift advancement of deep learning technology recently, individuals have started using deep learning methods to address 3D human reconstruction endeavours. There are two main classifications for approaches: parametric techniques and non-parametric methods. The parameterization technique utilizes a deep learning algorithm to estimate the parameters of the 3D human model. Subsequently, the contour is modelled based on these parameters to get the reconstruction result (Su et al. 2022 ). The Metaverse’s construction involves several technological components, including network communication technology, Internet of Things technology, artificial intelligence, extended reality, virtual reality technology, and blockchain technology (Ding et al. 2022 ). Machine vision, an amalgamation of computer vision and extended reality (XR), is seen as a vital technological component in establishing the foundational structure of the Metaverse. Collecting and processing raw data from the visual world allows for the inference of high-level information.

This information is presented to users through head-mounted devices, smart glasses, smartphones, and similar devices (Huynh-The et al. 2023 ). The data inside the Metaverse has distinctive characteristics that enable its identification and use within a blockchain-based system, hence facilitating the traceability of such data. The resource in question has been identified as a valuable asset in machine learning (Tran et al. 2023 ). The increasing prevalence of Metaverse apps, including AR and VR, has facilitated the remote instruction of sign language via an avatar replicating human gestures. This avatar is driven by an AI-driven framework, enhancing both the accessibility and enjoyment of the learning process (Batnasan et al. 2022 ). AI with other technological advancements such as AR/VR, blockchain, and networking can potentially establish a metaverse that offers safe, scalable, and immersive virtual environments on a reliable and continuously accessible platform. Based on the seven-layer metaverse design, the significance of AI in ensuring infrastructure dependability and enhancing performance is unquestionable (Huynh-The et al. 2023 ).

5.1.2 Transforming education and medical training through the metaverse

In order to address the disparities in learning within traditional educational settings, implementing an educational metaverse has the potential to provide a customized and distinctive learning environment and encounter. Additionally, it may give a personalized learning and development strategy that considers each student’s individual psychological attributes and cognitive processes. Integrating virtual and physical digital learning environments and using interactive learning methods developed by the educational Metaverse is expected to significantly enhance students’ desire to engage in the learning process (Lin et al. 2022 ). From an industrial perspective, the primary application scenario of the metauniverse is education. This statement emphasizes the conceptualization of education as a societal effort to promote individual growth throughout one’s lifespan and underscores the incorporation of technology into future educational systems. The emergence of the education metauniverse is poised to become a viable model for addressing the intersection of technological progress and educational obstacles (Sun 2022 ). The advent of the meta-universe era has considerably expanded the range of network activities college students show. The author posits that college students possess distinct characteristics in their engagement with network entertainment and network socialization, which align with their age and interests in online activities.

Consequently, the author proposes categorizing these behaviours into five primary dimensions: network learning behaviour, network social behaviour, network entertainment, network consumption behaviour, and network expression behaviour. These divisions are based on shared objectives and motivations (Ge 2022 ). Extended reality technology has significant promise in shaping the landscape of medical education in forthcoming years. The expanding range of applications for this technology may be used across all phases of the medical training process. The examination and discourse around these applications are of utmost importance in guaranteeing our medical education system’s future advancement and holistic proficiency (Ahuja et al. 2023 ). The use of augmented reality within the healthcare industry has a notable influence on prospective healthcare professionals’ competencies and knowledge foundations. Surgical aiding tools, such as the Microsoft HoloLens, are technological devices surgeons use to enhance and expedite surgical operations (Mozumder et al. 2022).

5.1.3 Metaverse and IoT: a synergistic integration of cutting-edge technologies

Integrating the Internet of Things (IoT), technology plays a pivotal role in the metaverse ecosystem. Internet of Things (IoT) devices facilitate the transmission of gathered data to higher-level applications, allowing instantaneous communication and fostering immersive experiences inside the Metaverse (Ding et al. 2022 ). The Metaverse is a novel application with significant gains due to technological advancements. The Metaverse relies heavily on data manipulation due to the integration of several advanced technologies, such as the IoT, DT, and big data (Rospigliosi 2022). The concept of the Metaverse and the IoT might be seen as digital twins, with the Metaverse being characterized by higher use of IoT devices inside its virtual office environment. The data in question has a distinct identifying tag and serves as traceable information inside the blockchain-based Metaverse (Mozumder et al. 2022). The global interest in the Metaverse has increased due to the emergence of IoT, virtual reality, cloud computing, and digital twin technologies. The Metaverse platform incorporates and utilizes several developing technologies in cloud education, smart health, digital governance, and disaster relief (Gu et al. 2023 ). The Metaverse provides a wide range of persons with extended network connectivity through wireless networks. In the last decade, several innovative technologies have been developed to enhance the overall efficiency of wireless communication and networking systems. AI has been extensively integrated into different layers of network design (Huynh-The et al. 2023 ).

5.2 Challenges

The ethical considerations of implementing various technologies and applications pose a significant challenge within the metaverse framework. Ensuring the safeguarding of user assets and maintaining the privacy and security of data is of paramount significance. Moreover, it is crucial to recognize and address the economic obstacles and technical complexities to fully realize the immersive metaverse experience’s extensive potential. Applying advanced technologies, such as DRL and electronic stickers for body measurement, poses challenges concerning reliability, accuracy, and user experience. Analyzing user satisfaction and resolving complex interactions between manipulation types, object properties, and user preferences present considerable challenges in advancing immersive and realistic metaverse applications (Fig. 7 ).

Metaverse challenges

5.2.1 3D modelling, rendering, and interaction

The development of the Metaverse and VR technology has posed significant challenges for 3D research, including 3D modelling, rendering, interaction, collaboration, and ethical considerations (Fan et al. 2022 ). Accurate 3D human body modelling in the Metaverse is challenging, especially for capturing detailed facial textures. A proposed solution combines a 3D reconstruction algorithm with facial features. Using a 3DMM, the algorithm predicts facial parameters and extracts features. These are then fused with 2D image features, enhancing accuracy. Challenges include body proportions, appearance variations, real-time performance, privacy, user customization, cross-platform compatibility, and ethical considerations. This approach improves 3D modelling for realistic virtual representations (Su et al. 2022 ).

5.2.2 Ethical considerations

One of the challenges in implementing the proposed metaverse network intrusion detection model is addressing the complexity and scalability issues that arise due to the integration of multiple technologies, such as GAN, IoT, DAE, and RF, while ensuring efficient and accurate detection of abnormal traffic in the Metaverse (Ding et al. 2022 ). Challenges in implementing the immersive Metaverse include ethical considerations, economic barriers, and technical hurdles (Park and Kim 2022 ). However, the implementation of DRL technology in emergency evacuation systems using the Metaverse also presents particular challenges, such as ensuring the reliability and accuracy of real-time data collection, addressing potential privacy concerns related to sensor data, and optimizing the DRL model to handle complex and dynamic evacuation scenarios effectively (Gu et al. 2023 ).

Developing a skin-interactive electronic sticker for measuring body curvature and skin wave fluctuations poses several challenges, including ensuring high detection sensitivity, accurate image segmentation, robust deep learning algorithms, compatibility with different body types, and establishing the reliability and usability of the device in various wearable applications (Hong et al. 2022 ). The challenges in studying satisfaction with virtual object manipulation in MR-based metaverse applications include understanding the complex interplay between manipulation types, object properties, and user preferences and ensuring seamless integration of MR technology to provide an immersive and realistic experience for users (Choi and Kim 2022 ). Integrating meta bots with motion capabilities into complex virtual 3D worlds and optimizing their behaviour through an evolutionary computation-based learning model presents challenges such as navigating intricate environments, achieving realistic human-like behaviours, managing the optimization process, balancing exploration and exploitation, ensuring generalization to diverse scenarios, integrating with social networks, and addressing ethical considerations. Overcoming these challenges is crucial for enhancing meta bots’ capabilities and seamless integration in virtual environments (Arroyo et al. 2011 ). The challenges associated with AI in the Metaverse for educators include navigating data privacy and security issues, addressing biases and discrimination in AI algorithms, preserving learner autonomy and agency, and promoting ethical AI design principles (Rospigliosi 2022).

5.2.3 Metaverse network and security

Blockchain implementation in the metaverse environment is complex. First, the Metaverse’s dynamic AI-based services need blockchain to be adaptive. Smart contracts and consensus methods may suffer in a fast-paced environment. The Metaverse creates and analyzes massive quantities of data, making scalability a challenge. Blockchain must effectively manage massive transaction volumes and data storage. Metaverse ecosystem security is essential. The Metaverse manipulates massive volumes of data, making data integrity, privacy, and secrecy challenging to maintain. Blockchain implementation should address these security concerns effectively to build trust and confidence within the Metaverse (Bouachir et al. 2022 ). Securing the Metaverse is challenging due to the need for seamless integration of real and virtual elements while ensuring robust security. Hacker attacks exploiting anonymous texts pose a significant risk, requiring safeguarding user assets. Leveraging NLP technologies like TF-IDF, word2vec, GRU, RNN, and LSTM aids content analysis and anomaly detection. Optimizing algorithms for metaverse security, including topic extraction and grouping, is crucial. Ongoing research focuses on validating algorithm performance and overcoming limitations for a practical approach (Park et al. 2022 ).

5.2.4 Immersive metaverse implementation

The challenges in human–machine interactions using current interactive sensing interfaces include massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements (Wei et al. 2022 ). Introducing XIVA as an intelligent voice assistant for the educational Metaverse presents challenges in integration, infrastructure, language processing, privacy, testing, and ongoing support (Lin et al. 2022 ). Challenges in gesture recognition systems and wearable devices for human–machine interfaces include limited sensor data quality, high computational costs, difficulty in differentiating gestures, establishing correspondence between muscle/tendon groups and gestures, optimizing models with reduced computational resources, achieving low latency for real-time operations, and enhancing cyber-human interactions (Fang et al. 2022 ). The challenges in finger vein recognition for the Metaverse include low-quality images, variations in contrast, scale, translation, and rotation, as well as the need for robust security against unauthorized access and data compromise (Tran et al. 2023 ). While technology development to facilitate communication for people with hearing loss is promising, several challenges remain to address. These challenges include the limited availability of datasets and resources for Arabic sign language compared to other languages, the need for accurate and reliable gesture recognition algorithms, and ensuring widespread access to the developed systems and applications. Additionally, variations and regional differences in sign language may need to be accounted for in the development process. Overcoming these challenges will be crucial for creating compelling and inclusive solutions for the deaf and hard-of-hearing community (Batnasan et al. 2022 ).

5.2.5 AR/MR and VR

AR/MR audio implementation faces challenges in latency management, including fast head tracking, lightweight DL models, training data sets, strict latency in real sound control, acceptable latency in virtual sound rendering, hardware limitations, and efficient filtering methods (Gupta et al. 2022 ). The study faces challenges in accurately distinguishing and interpreting emotional states based on EEG signals in the context of AR-based reading. Additionally, ensuring the generalizability of the findings beyond the specific stimuli used and addressing potential confounding factors are important considerations. Furthermore, integrating AR systems into various Metaverse-based applications may require overcoming technical and practical hurdles for widespread adoption (Daşdemir 2022 ). VR’s challenges include wealth inequity due to high costs, algorithmic bias in data processing, digital exclusion for those without access, limited policy engagement, and the need for regulatory intervention (Egliston and Carter 2021 ). The challenges in metaverse construction include high model complexity, computational efficiency, local and global feature representation, and providing clear semantic information for objects. The CWCT transformer framework addresses these challenges by combining CNN and transformers, optimizing Cross-Window Self-Attention for local features and utilizing CNN for global features. It improves classification accuracy and operation speed and reduces model complexity compared to the original CMT network (Li et al. 2022a , b , c ). Designing a deep learning-based asymmetric virtual environment presents challenges in gesture recognition, hand tracking, text recognition, seamless integration, user satisfaction, system performance, and user adaptation. Overcoming variations in gestures and hand tracking accuracy, interpreting handwritten text, integrating VR and AR seamlessly, ensuring user satisfaction and immersive experiences, optimizing system performance, and aiding user adaptation are crucial considerations in this design process (Cho et al. 2022 ).

5.2.6 Metaverse AI gesture recognition challenges

Implementing intelligent non-contact gesture recognition systems faces several challenges. These include the need to ensure high accuracy across diverse user populations, accounting for variations in lighting and real-world conditions, accommodating a wide range of diverse and complex gestures, optimizing user experience and ergonomics, addressing scalability and integration with different platforms and devices, and addressing data privacy and security concerns associated with collecting and processing user data (Zhou H et al. 2022 ). The challenges in the proposed project include technical complexity, user adaptation, accessibility, content creation and management, connectivity and infrastructure, assessment and evaluation, cost and scalability, and privacy and security (Sghaier et al. 2022 ).

5.3 Recommendations

The recommendations in Fig. 8 focus on four key areas regarding which policy is required to achieve the general vision for Metaverse and AI, which are outlined next.

Metaverse recommendations

5.3.1 Sensor data

The acquisition of real-time data from diverse real-world components is a crucial resource for developing superior services in metaverse applications based on the IoT (Bouachir et al. 2022 ). Touchless HMIs have gained significant traction recently due to their notable benefits in superior hand dexterity, enhanced comfort, and improved hygiene. Consequently, they have great potential in several domains, such as intelligent robotics, virtual and augmented reality, and medical facilities (Zhou et al. 2022 ). The surface charge effect enhances the sensor array’s ability to detect muscle/tendon activity with superior reliability, sensitivity, and cost-effectiveness compared to the conventional surface electromyography (sEMG) technique. Significantly, the strong correlation between the activity of the dominant muscle/tendon groups and gestures plays a crucial role in distinguishing various components in sensor data, potentially enhancing the accuracy of gesture categorization. The analysis of the collected data from different hand gestures reveals the exceptional robustness and consistency of the sensor system. Additionally, a correlation is identified between the patterns of signal waveforms and the motions of significant muscles and tendons (Fang et al. 2022 ). The AIOM touch sensor exhibited a notable regional diversification in its mechanosensitive signal, enabling it to effectively react to both single-point touch sites and multipoint touch positions in the presence of spatiotemporally dynamic mechanical stimulations (Wei et al. 2022 ). Combining 3D virtual worlds with social networks provides software agents with similar attributes to avatars controlled by humans (Arroyo et al. 2011 ). Recent regulatory developments regarding data provide valuable insights into managing VR as a technology that generates substantial data. The FTC in the United States has lately directed its attention to the data used in face recognition algorithms. The FTC has issued a directive mandating the cessation of operations for algorithms developed using unlicensed data, specifically photographs obtained via unauthorized means from social media platforms. A comparable approach might be employed to mitigate the possibility for Facebook to exploit and re-identify VR data (Egliston and Carter 2021 ).

5.3.2 Data pre-processing

The efficiency of pre-processing and post-processing activities has been improved. The relevance of AR stimuli is seen in both the beta and gamma frequency bands in the categorization of 2D-VA groups. In the context of the 2D-VA group, it was shown that AR stimuli were more suitable for portraying emotional states. Previous research has shown the efficacy of including valence and arousal aspects (Daşdemir 2022 ). During our security strategy investigation aimed at mitigating hacker assaults, we saw that implementing filtering mechanisms proved very beneficial in several aspects, including subject classification, identification of risky groups, dimension management, and token classification methods (Park et al. 2022 ). Given that the frames extracted from the in-house video have not been included in any of the existing datasets, it would be justifiable to conduct a comparative analysis of two models in terms of their performance in classifying indicators that have been removed from the in-house video, using annotated frames as reference (Batnasan et al. 2022 ). The production of a stereoscopic effect necessitates the employment of display technology since a minimum of two images with distinct viewing angles is required for a comprehensive 3D stereoscopic image (Fan et al. 2022 ). To enhance the performance of the intrusion detection model, it is necessary to address the need for improvements in both model stability and timeliness (Ding et al. 2022 ).

5.3.3 Metaverse construction

Providing specific semantic information for each item is critical to improving interaction throughout the metaverse development process (M. Li et al. 2022a , b , c ). When an AR user is exploring and experiencing the general virtual world, he or she may use the text interface to interact with the virtual environment and other users and perform other mode transition activities (Cho et al. 2022 ). The interaction in the Metaverse is built on virtual three-dimensional space, which includes virtual landscapes, virtual characters, and so on. As a consequence, technological advances in three-dimensional human body reconstruction have had a profound impact on the Metaverse (Su et al. 2022 ). Natural and continuing interactions between people and XR devices are essential in the developing metaverse age. However, present rigid wearable devices are large, heavy, and costly (X. Zhang et al. 2022a , b ). Use the metaverse-based evacuation strategy in various situations, such as buildings with complex floor plans, evacuees in wheelchairs, or falls during the evacuation operation. All of this adds to the challenges of the evacuation (Gu et al. 2023 ).

Future educational metaverse development objectives include creating a new generation of talent with immersive "listening, speaking, reading, and writing" literacy skills. Simultaneously, the critical objective of constructing the future educational Metaverse is to provide virtual teachers or learning aides with the essential abilities of "listening, speaking, reading, and writing" (Lin et al. 2022 ). Computers and mobile phones are only two ways to connect to the Metaverse.

However, these gadgets do not give the same level of interaction as actual metaverse devices. Virtual reality equipment (Google, Samsung, HTC Vive, etc.) provides the most immersive experience (Tran et al. 2023 ). The small and portable device will provide a more user-friendly and intuitive UI solution than those now available in the Metaverse. Consequently, it will hasten the transition to a pandemic-induced touchless interface society (Hong et al. 2022 ). Disabled students may have easier access to more diversified tools to help them engage with the 3D virtual world. Indeed, instructors and students will no longer need to be physically present in the classroom or even in the same country for learning, assessments, and exams. Consequently, people may be able to access these experiences in the Metaverse as avatars (Sghaier et al. 2022 ). Despite much research into the Metaverse, the focus has been chiefly on social meaning, with little attention made to Metaverse technology. A rigorous approach to what concepts and technologies are needed to create an environment and material that consumers can appreciate, such as in Ready Player One (Park and Kim 2022 ), is required. The MRMA’s object manipulation significantly impacted user satisfaction (Choi and Kim 2022 ).

5.3.4 Metaverse developers

The comprehension of AI decision-making processes, such as the generation of predictions by AI models, is likely to need to be completed for metaverse developers, virtual world designers, and users, leading to a reliance on these processes without a comprehensive understanding of their inner workings. XAI refers to a comprehensive set of tools and methodologies utilized to define AI models, evaluate their anticipated outcomes, describe the transparency of the models, and scrutinize the results. These tools and methodologies facilitate human users in comprehending and placing trust in AI models by monitoring the entire process and ensuring accountability (Huynh-The et al. 2023 ). As the Metaverse continues to gain popularity, there is a corresponding increase in our dependence on artificial intelligence. As a result, teachers may encounter various formidable challenges. This editorial will elucidate many rationales for individuals using interactive learning settings to exercise caution while first navigating the Metaverse.

In order to adequately highlight these issues, it is necessary to provide a clear definition of the Metaverse, including the technology it employs and its potential applications in an educational environment (Rospigliosi 2022). The use of extended reality technology in healthcare delivery has raised concerns over the depersonalization of medicine. Providing substantial evidence for the comparable efficacy of digital and physical modes of human connection is challenging now, mainly owing to the nascent stage of extended reality and metaverse advancements. Currently, the predominant area of research pertaining to extended reality technology is its use in medical education. Nevertheless, the specific implications of using this technology in interpersonal interactions between physicians and patients remain uncertain and require more investigation (Ahuja et al. 2023 ).

6 Analysis of characteristics and research gaps

This section will look at various critical factors that will help academics by addressing gaps in future research. Each component draws attention to a gap in the research literature and examines the missing ingredient or elements. Because the Metaverse has not been adequately researched, it must be explored. The subsections that follow, on the other hand, include several essential numbers and research that explain the current state-of-the-art Metaverse.

6.1 The reality of applying trustworthy AI requirements in metaverse studies

Several recent discussions have focused on the transparency of AI-based solutions. This is understandable, given that some of these algorithms are black boxes, demonstrating how difficult it is to articulate their inner workings. Furthermore, since this technology is based only on historical and often human-generated data, it may sometimes exhibit various types of bias, creating ethical difficulties. Now that AI-based solutions are widely used in medical devices, fairness, ethics, transparency, and reliability are indispensable. The seven elements of trustworthy AI have been discovered.

These components include human agency and supervision, technology robustness and safety, privacy and data governance, transparency, diversity, non-discrimination and fairness, social and environmental well-being, and accountability. It should be noted that the word "trustworthy AI" implies that the authors are concerned with ensuring that the AI used in Metaverse applications of taxonomy literature is reliable, safe, and ethical. Given the potential impact of AI on Metaverse system outcomes, this is an important subject. As a consequence, Table (1) depicts the presence of reliable AI criteria in Metaverse literature. The authors may have researched several publications in various fields of literature and identified the most often stated conditions for trustworthy AI. According to EU legislation, there are seven key criteria for trustworthy AI (see Fig. 9 ). The seven critical aspects of AI governance can be summarized as follows: First , human agency and oversight involve safeguarding fundamental rights, involving human decision-making, and ensuring human supervision. Second , technical robustness and safety encompass protection against security threats, backup plans for system failures, and maintaining accuracy and reliability. Third , privacy and data governance focus on upholding data quality, privacy, and access. Fourth , transparency entails tracing and clarifying decision-making processes and communicating outcomes transparently to stakeholders. Fifth , diversity, non-discrimination, and fairness encompass preventing biased outcomes, ensuring inclusivity and accessibility, and promoting stakeholder involvement. Sixth , societal and environmental well-being considers sustainability, social and environmental impact, and democratic values. Lastly , accountability involves audibility, mitigating and reporting negative consequences, acknowledging trade-offs, and providing necessary remedies.

The seven essential elements of trustworthy AI

The significance of all seven demands is noteworthy as they mutually enhance each other, and it is imperative to implement and assess them over the whole lifespan of the AI system (Wei and Liu 2024 ; Alzubaidi et al 2024a , b , c , d ). When evaluating criteria in various domains and sectors, it is essential to consider the contextual factors and any conflicts that may arise. These criteria have to be used throughout the whole life cycle of an AI system and should be subject to variation depending on the specific application.

The bulk of the criteria apply to all AI approaches, with particular emphasis placed on those that have a discernible influence, whether direct or indirect, on individuals. Consequently, specific applications, particularly those in industrial contexts, may see a decrease in relevance. Under some conditions, the existing statute already encompasses the abovementioned requirements. In accordance with the first element of reliable AI, AI professionals are responsible for adhering to their legal obligations, including universally applicable norms and regulations unique to their respective fields. Moreover, the term ‘trustworthy AI’ indicates the authors’ focus on ensuring the reliability, safety, and ethicality of the AI used in the Metaverse. The significance of the subject matter lies in the prospective consequences of AI on the results of Metaverse applications. Table 1 presents the prevalence of reliable AI requirements in the existing literature on the Metaverse. The researchers extensively reviewed the current body of literature on the Metaverse in order to find the prevailing conditions often cited for ensuring the trustworthiness of artificial intelligence.

Based on an examination of several sources, Table 1 illustrates that the prevalence of trustworthy AI needs to be provided in Metaverse literature. For each reference, the Table shows the frequency of each need as very low (VL), low (L), medium (M), high (H), or very high (VH). The following sections provide the debate and analysis for each requirement:

In the Metaverse literature, the percentages of Human agency and oversight required are VH (0%), H (0%), M (0%), L (0%), and VL (100%). According to the literature reviewed, all research or papers on Metaverse need 100% human oversight and agency. This suggests that no autonomous Metaverse systems in the literature run without human input or supervision and that all systems need some degree of human control. This might be owing to the fact that the Metaverse is still in its early stages and completely autonomous systems do not yet exist, or it could be due to ethical issues, technological limits, or safety concerns. It should be noted that this assertion is particular to the material reviewed and may not reflect the whole area of Metaverse. The degree of human agency and oversight necessary in these systems may alter as Metaverse technology progresses and new research is undertaken.

In terms of technological robustness and safety, the investigations meet this need in the following percentages: VH (10%), H (18%), M (32%), L (10%), and VL (30%). According to the percentages, the majority of research addresses this need as M, with VH and H being the least represented. Although Metaverse is meant to be technically robust and safe, only some researchers deem this need important or high-risk.

Based on the percentages, it seems that the Metaverse system under consideration puts a low value on the "Privacy and data governance" criteria. Specifically, all studies thought this need was insignificant, and just 8% thought it was crucial. Furthermore, 22% of the studies thought it was of medium relevance, while 16% thought it was of low value. The majority of research (54%) rated it as extremely low relevance. This shows that the Metaverse system may need more comprehensive privacy and data governance controls and that it may prioritize user data protection and compliance with applicable data privacy laws and regulations. This might be a problem for businesses or people using the Metaverse system, mainly if dealing with sensitive or secret information. Exploring new privacy and data governance procedures may be necessary to address these concerns to enhance the Metaverse system’s capabilities.

VH criteria are not reflected in the "Transparency" criterion, whereas the bulk of the requirements (78%) fall into the VL group. Furthermore, 14% of the studies regarded it to be of considerable relevance, compared to 2% and 6% for the H and L needs, respectively. This implies that the Metaverse system emphasizes openness for low-risk needs, but high-risk requirements may get less information. However, it is crucial to emphasize that more details of the precise standards and their execution are necessary to properly assess the efficiency of the existing transparency measures.

Based on the percentages, it seems that the Metaverse system under consideration puts a low value on the "Diversity, non-discrimination, and fairness" criteria. In particular, none of the research deemed it to be of very high or high relevance. Furthermore, 8% of the studies thought it was of medium relevance, while 18% thought it was of low value. The majority of research (74%) rated it as extremely low relevance. This shows that the Metaverse system may not prioritize encouraging diversity, equality, and inclusion in its outputs and ensuring that its models are not prejudiced against certain groups of people. This may be a source of worry for organizations or people using the Metaverse system, mainly if they operate in industries where fairness and non-discrimination are essential. Additional methods may be necessary to examine and eliminate biases in the Metaverse system’s outputs or enhance its capabilities with tools or procedures that promote fairness and non-discrimination.

The percentages supplied for the "Societal and environmental well-being" criteria indicate that the Metaverse system under consideration puts a very low value on this need. In particular, none of the studies judged this condition to be extremely important, and just 2% of the studies considered it to be essential. Furthermore, 24% of the studies thought it was of medium relevance, while 14% thought it was of low value. The majority of research (60%) rated it as extremely low relevance. This implies that the Metaverse system may not prioritize the possible consequences of its outputs on society and the environment. This may be a source of worry for organizations or people using the Metaverse system, mainly if they operate in domains where societal and environmental well-being are essential, such as sustainable development or social justice. Additional actions may be required to examine and mitigate the negative implications of the Metaverse system’s outputs on these sectors.

The "Accountability" criteria percentages indicate that the Metaverse system under consideration puts a relatively low weight on this need. None of the studies thought it was of very high, high, or medium relevance, and just 2% thought it was of low value. The bulk of research (98%) rated it as extremely low relevance. This implies that the Metaverse system may not prioritize guaranteeing openness and accountability in its processes and outputs. This might worry companies or people that use the Metaverse system, mainly if they operate in highly regulated sectors or disciplines. Additional actions may be necessary to promote openness and accountability, such as developing auditing or documentation procedures to monitor the system’s inputs and outputs.

This shows that the Metaverse literature still needs to incorporate these characteristics of trustworthy AI completely. Diversity, non-discrimination, and justice are similarly relatively low on the list, with only a few sources rating them as high or extremely high. This is especially important in light of the rising awareness of prejudice and discrimination in AI systems and the need for more inclusive and fair AI development. Overall, the Table demonstrates that the most often cited characteristics of trustworthy AI in the Metaverse literature are technological robustness and safety, human agency and supervision, and privacy and data governance, with very high or high scores in many studies. On the other hand, diversity, non-discrimination and justice, social and environmental well-being, and accountability are less commonly cited, with primarily medium or poor scores. It should be noted that the findings in the Table represent the emphasis of the examined research, not the overall value or usefulness of each feature of trustworthy AI in the creation of Metaverse systems.

6.2 Metaverse datasets for AI applications

In general, AI techniques and datasets are critical. The main focus of the study is the availability of reliable Metaverse datasets for use in AI applications. The employment of AI in the Metaverse has great potential for improving user experience, speeding data processing, and strengthening security measures. Here’s a more in-depth look at how AI may directly help in these areas (Otoum et al. 2024 ; Soliman et al. 2024).

Improved User Experience:

Personalisation: AI algorithms analyse user behaviour, preferences, and interactions in the Metaverse to deliver personalised experiences. For example, AI may adjust virtual surroundings, avatars, and content suggestions to individual interests, increasing user engagement.

Natural Language Processing (NLP): AI-powered chatbots and virtual assistants can let people communicate seamlessly with the Metaverse. NLP allows these assistants to interpret and reply to natural language questions, assisting users with navigation, information retrieval, and task completion.

Immersive Interactions: AI may improve immersion by creating realistic simulations, dynamic surroundings, and lifelike NPCs (non-player characters) in the Metaverse. This results in more engaging and dynamic experiences for users, encouraging deeper relationships and longer engagement.

Optimising Data Processing:

Big Data Analytics: The Metaverse creates large volumes of data via user interactions, transactions, and virtual environments. AI-powered analytics can effectively handle large amounts of data to provide important insights such as user behaviour patterns, market trends, and performance indicators. These insights may help with decision-making, content optimisation, and personalised suggestions.

Predictive Modelling: AI algorithms may utilise previous data to forecast future trends, user preferences, and possibilities in the Metaverse. This allows proactive decision-making, resource allocation, and content curation to accommodate changing user needs and market realities.

Real-time Processing: Artificial intelligence enables real-time data processing and analysis, allowing for dynamic changes and optimisations inside the Metaverse. This assures reactivity, scalability, and adaptation to changing situations, resulting in better user experiences and increased operational efficiency.

Improved Security:

AI-powered security systems identify and mitigate cyber risks including malware, phishing attacks, and unauthorised access in the Metaverse. Machine learning algorithms can proactively detect possible security breaches by analysing trends and abnormalities in user behaviour, network traffic, and system activity.

Fraud Prevention: Artificial intelligence can improve fraud detection and prevention by analysing transactional data, user profiles, and behavioural patterns for signals of suspect or fraudulent conduct. This helps to protect financial transactions, virtual assets, and sensitive data inside the Metaverse.

Content Moderation: Artificial intelligence algorithms may automate content moderation procedures by recognising and filtering out improper or harmful information, such as hate speech, harassment, or explicit material. This provides a safer and more inclusive virtual environment for users, hence reducing possible risks and liabilities.

To summarise, the integration of AI technology has enormous potential to convert the Metaverse into a more immersive, efficient, and secure digital environment, providing improved user experiences while tackling difficult data processing and cybersecurity concerns. Furthermore, most authors do not share and keep their information secret for various reasons, exacerbating availability difficulties. Journals regularly require dataset disclosures to be included in published research. This means the datasets presented inside the publications must meet all legal criteria. In contrast, more specific datasets generated for risk bias concerns and solutions are becoming a significant concern. Consequently, further research should be conducted to improve the trustworthiness of AI systems by employing relevant datasets produced for risk bias concerns and solutions. Table 2 includes crucial information on dataset availability, a description, size, or sample of the data, and how large data has been handled in the AI trustworthiness study.

The paragraph describes a diverse set of datasets used across various domains. It includes datasets for human scans showcasing clothing, body shapes, and poses for computer vision applications. There are datasets for network traffic (normal and attack) used in network security and intrusion detection research. Other datasets include sensor technology, email classification, keystroke data, Chinese voice, hand motion, finger biometrics, multimodal biometrics, sign language recognition, EEG data for emotion recognition, and image classification benchmark (MNIST). There are also datasets for gesture recognition and disabled learners in a 3D virtual environment for inclusive education and virtual reality research. The case studies in the dataset description column received minimal attention in the study. The dataset settings used in the literature were mainly focused on images, signals, words, and packets. When the third and seventh columns in Table 2 are compared, it is feasible to conclude that despite having a small sample size, some studies considered their datasets significant. It should be noted that datasets including just 22 people’s EEG signals, as in (Dasdemir 2022 ), or 1000 photos, as in (Li et al. 2022a , b , c ), were considered big data. One potential source of worry is that some researchers may use the term ‘big data’ as a buzzword to make their work sound more impressive, even if it still needs to meet the established qualifications for big data. This might be owing to the excitement around big data, which has led to misunderstandings about what it comprises. be a consequence, detailed reasons for why a dataset is referred to be big data are required, taking into consideration not just the collection’s size but also its complexity, variety, and velocity. This may help ensure that research initiatives are credible and adequately labelled and provide clarity among academics and policymakers about what constitutes big data. Furthermore, although all of the authors in the relevant study declared that the datasets used satisfied the legal standards, numerous publications omitted a link to data availability. Researchers must adhere to regulatory obligations while collecting, processing, and exploiting data, particularly given the rising focus on data privacy and security. This includes following data protection norms and regulations such as the EU’s GDPR, the Accountability Act in the US, and analogous legislation worldwide (Fig. 10 ).

Topic modelling of AI in metaverse

The diagram depicts a thorough topic modelling for classifying research papers on the Metaverse and its uses. The taxonomy is divided into six main groups, each of which covers a particular subject topic as follows:

Human Engagement and Interaction in the Metaverse

Measuring People Engagement (Park and Kim 2022 ).

Using Speech Interactions with Virtual Objects in Mixed Reality (Siyaev and Jo 2021).

User Satisfaction with Virtual Object Manipulation in Mixed Reality (Choi and Kim 2022 ).

Deep Learning-based Interaction Recognition and Sensor Applications (Wei et al. 2022 ).

Development of an Intelligent Voice Assistant (Lin et al. 2022 ).

Gesture Recognition System using Smart Wristbands (Fang et al. 2023 ).

Deep Learning-based Gesture and Text Interfaces in an Asymmetric Virtual Environment (Cho et al. 2022 ).

Intelligent Noncontact Gesture-Recognition System for Medical Applications (Zhou H et al. 2023 ).

Optimization and Control Algorithms for Metaverse Applications

Evolutionary Computation for Optimizing Fuzzy Controllers (Arroyo et al. 2011 ).

Hierarchical Multiagent Reinforcement Learning Approach with Experience (Hare and Tang 2022 ).

Hybrid Intrusion Detection Model using GAN, DAE, and RF (Ding et al. 2022 ).

Deep Reinforcement Learning for Emergency Evacuation in the Metaverse (Gu et al. 2023 ).

Technological Innovations and Applications

Fusing Blockchain in Metaverse Applications (Yang et al. 2022 ).

Hybrid Cartridge Format Electronic Sticker for Body Analysis and Control (Hong et al. 2022 ).

Finger Vein Recognition for VR Human–Robot Equipment (Tran et al. 2023 ).

Arabic Sign Language Gesture Recognition for Enhanced Accessibility (Batnasan et al. 2022 ).

Improved Image Classification Framework for Metaverse Construction (M. Li et al. 2022a , b , c ).

Ethical and Societal Implications of Metaverse and Virtual Reality

Ethical Issues in Metaverse and Deep Learning-based Interactive Experiences (Rospigliosi 2022).

Data-Borne Harms and Power Inequities in Virtual Reality (Egliston and Carter 2021 ).

Education and Learning in the Metaverse

Propositions of Metaverse Tourism (Koo et al. 2022 ).

AI-based Methods for the Metaverse: NLP, Machine Vision, Blockchain, Networking, Digital Twin, Neural Interface (Huynh-The et al. 2023 ).

Signal Processing Techniques for AR/MR Audio in Educational Contexts (Gupta et al. 2022 ).

Emotion Recognition in AR Systems for Educational Activities (Daşdemir 2022 ).

Integration of Virtual Reality and Educational Technology

Virtual Learning Environment Integration with Educational Technology (Sghaier et al. 2022 ).

6.3 Evaluation and benchmarking process of the metaverse ecosystem

This section presents a proposed grouping algorithm to ensure the evaluation and benchmarking of topics pertaining to the Metaverse. The application of MCDM can be utilized to assess the efficacy of the Metaverse under consideration. This evaluation involves the consideration of various criteria, including but not limited to technical performance, user experience, content quality, ethical considerations, societal impact, performance metrics, security and safety, and accessibility. The proposed decision matrix for this process is shown in Table 3 .

The MCDM model can facilitate evaluating Metaverse performance concerning other established methodologies, thereby enabling the selection of optimal Metaverse strategies based on the identified criteria. Additionally, MCDM algorithms can provide an appropriate weight for the eight Metaverse criteria through the analytic hierarchy process (AHP) (Al-Qaysi et al. 2023 ) and best–worst method (BWM) (Rezaei 2015 ) that have shown promising results. However, the inconsistency in their weighing techniques needs to be addressed (Al-Humairi et al. 2022 ; Al-Samarraay et al. 2022b , a ; Albahri et al. 2022 ; Alsalem et al. 2022 ; Alzubaidi et al. 2024; Salih et al. 2021 ). To tackle this, FWZIC method has been introduced (Alsalem et al. 2021 ; Alamoodi et al. 2022 ). FWZIC method assigns weights to evaluation Metaverse criteria while ensuring zero inconsistency. It computes and calculates weight coefficients for each criterion separately, allowing for consistent and accurate assessment. By utilizing the FWZIC method or similar approaches, the evaluation and benchmarking of Metaverse can be performed more effectively. This method ensures that the weighting process is reliable and free from errors or inconsistencies that could impact the overall evaluation results. Furthermore, in the context of benchmarking issues and ranking, MCDM can be employed to address these challenges using the FDOSM (Alsalem et al. 2021 ; Al-Samarraay et al. 2022b , a ). FDOSM is a method used to determine the best rank for Metaverse alternatives, overcoming the issues associated with Metaverse criteria. FDOSM incorporates the concept of an ideal or optimal solution, eliminates inconsistency and two preferences, reduces the number of comparisons required, provides fair and implicit comparisons, and requires fewer mathematical operations (Alamoodi et al. 2022 ; Albahri et al. 2023 ). It also addresses concerns about normalization and weights, which are common in MCDM techniques. One of the critical features of FDOSM is its ability to handle ambiguous and fuzzy data. By employing these processes, FDOSM can effectively deal with imprecision and uncertainty in decision-making (Mahmoud et al. 2022 ). The advantage of utilizing this new combination lies in its ability to select the optimal or best Metaverse based on eight criteria. This method considers the overall performance of the Metaverse alternatives and incorporates a comprehensive evaluation based on multiple criteria, enabling the selection of the most suitable Metaverse. The FDOSM provides a systematic and robust approach to selecting the optimal Metaverse alternative by considering eight criteria and addressing the challenges associated with benchmarking and ranking in MCDM. Moreover, the utilization of MCDM can be advantageous in identifying potential trade-offs and conflicts that may arise among various dimensions within the Metaverse. One potential trade-off that can arise is balancing the degree of security and the level of user-friendliness or between the preservation of privacy and the extent of functionality. By thoroughly examining these trade-offs, individuals in positions of authority can arrive at well-informed decisions and achieve a suitable equilibrium among various criteria, considering the distinctive demands and limitations of the Metaverse setting. In conclusion, using MCDM to evaluate the proposed grouping algorithm for safeguarding Metaverse topics offers a methodical and unbiased appraisal of its efficacy. This aids decision-makers in making well-informed choices and improving the overall Metaverse ecosystem.

6.4 AI methods and techniques used in metaverse

As the Metaverse continues to evolve, AI plays an essential role in shaping and enhancing the immersive experiences within this virtual world. Thus, AI in the Metaverse has gained much attention recently as researchers employ AI solutions to create interactive virtual worlds. Table 4 provides a comprehensive overview of various research studies in AI regarding the Metaverse. It includes AI directions, methods used, metrics employed, applications, and technologies such as AR, VR, IoT, NLP, Computer Vision, Machine Learning, GANs, Reinforcement Learning, Virtual Assistant Technology, Semantic Web, Multi-agent Systems, NLG, Predictive Analytics, and Computer Graphics.

Table 4 highlights different research areas or directions of AI in selected papers and their respective AI applications. For example, face detection and landmark identification using MTCNN are explored in the context of 3D human reconstruction. Network intrusion detection systems are investigated using CNN, LSTM, and CNN + LSTM models; Evacuation training systems employ the rainbow-deep Q-network to simulate dynamic evacuation scenarios. Gesture recognition is addressed through multilayer perceptron and feedforward deep neural network models, enabling delicate skin-related gesture control. Another important aspect of AI in the Metaverse is the personalized content recommendation and user assistance. AI algorithms analyze user preferences, behaviours, and interactions within the virtual environment to provide tailored recommendations, guiding users towards relevant content and experiences. AI-powered virtual assistants can offer real-time support and guidance, enhancing user engagement and facilitating seamless navigation in the Metaverse. AI is also crucial in ensuring a safe and secure metaverse experience. AI-based systems can detect and prevent malicious activities such as fraud, hacking, or unauthorized content distribution. Additionally, the Table showcases the utilization of AI techniques in various domains, such as computational linguistics for recommendation systems, convolutional neural networks for finger vein recognition, and YOLO (You Only Look Once) for gesture recognition in education. Other areas of investigation include image classification, control and management using the MDP, digital content protection, natural language processing for prediction of user satisfaction, 3D point cloud classification in Metaverse applications, human image synthesis, singer identification through audio processing, and smart home technologies. All this can happen through different AI directions such as knowledge-based systems, computational linguistics, identification and authentication, emotion classification, image classification, control and management, digital content protection, natural language processing, 3D point cloud classification, human image synthesis, audio processing and music information retrieval, smart home technology, and more. AI methods enable the generation of lifelike and responsive virtual beings capable of interacting with users and simulating human-like behaviours. Through NLP and computer vision, these virtual characters can understand and respond to user commands, engage in meaningful conversations, and exhibit emotions, enhancing the sense of presence and social interaction in the Metaverse. Various AI techniques and models are employed across the studies, such as MTCNN, CNN, RNN, LSTM, YOLO, and GANs. In addition, RF, Naïve Bayes, KNN, LR, LightGBM, and Catboost. These methods highlight the utilization of both traditional and advanced machine learning and deep learning approaches. The "Metrics Used" criteria in the Table presented the evaluation metrics or measures used to assess the performance of the proposed methods. These metrics may include accuracy, precision, recall, F1 score, RMSE, and EER. The choice of metrics depends on the nature of the problem being addressed in each study. The "Application" column highlights the practical application or domain to which each research or project is targeted. This includes areas such as 3D human reconstruction, intrusion detection systems, evacuation training, gesture control, metaverse robots, recommendation systems, singer identification, digital content protection, prediction of user satisfaction, aircraft maintenance education, and more. The Table also indicates the presence or absence of specific technologies and approaches. AR, VR, IoT, NLP, computer vision, machine learning, GANs, reinforcement learning, virtual assistant technology, semantic web, multi-agent systems, NLG, predictive analytics, and computer graphics are among the technologies and approaches employed in the studies. Finally, the Table comprehensively overviews different AI research directions, methodologies, applications, and associated technologies. It showcases the diversity and breadth of AI research and highlights the areas in which different techniques and technologies are applied to solve various problems and challenges. Future AI in the Metaverse has a lot of potential, as we can see. The capabilities of virtual characters, environment generation, and user interactions will continue to be improved by developments in AI technologies like deep learning, reinforcement learning, and neuro-symbolic AI. Experiences in the Metaverse will become increasingly more immersive and connected as AI is combined with cutting-edge technologies like VR, AR, and the IoT.

6.5 Mixed reality & hologram

Mixed reality (MR) and holograms play crucial roles in the Metaverse, offering immersive and interactive experiences. MR combines the real and virtual worlds, enabling users to interact with virtual objects while maintaining awareness of their physical surroundings. Within the Metaverse, MR facilitates virtual collaboration, immersive commerce experiences, and augmented entertainment, breaking down distance barriers and enhancing user engagement. Holograms, projecting three-dimensional images into space, bring a sense of physical presence and depth to the Metaverse. They find applications in virtual conferencing, performances and events, education and training, and spatial computing, providing realistic interactions and enhancing user experiences. Overall, MR and holograms enrich the Metaverse by blurring the boundaries between reality and virtuality, creating dynamic and engaging environments for users to explore and interact with.

6.5.1 The integration of physical and digital worlds through mixed reality technology.

Mixed reality and the Metaverse are popular in technology and virtual experiences nowadays. Before assessing their association, each issue must be examined separately. Mixed reality combines VR and AR technology so that the physical and digital worlds may interact in real-time. A holistic setting is created by overlaying virtual components over the actual environment or integrating digital entities with their physical surroundings. Mixed reality experiences often involve headgear or gadgets to help people see and interact with virtual material (Liberatore and Wagner 2021 ). The Metaverse is a dynamic, all-encompassing virtual world that includes virtual reality, augmented reality, and the internet. The phenomenon is a lasting, all-encompassing, and linked domain of computer-generated environments where people may interact synchronously with other people and digital things. A shared, permanent, and diversified infrastructure that allows social interactions, business transactions, leisure pursuits, intellectual pursuits, and other undertakings is the Metaverse: mixed reality and the metaverse attempt to provide immersive, interactive digital experiences. Mixed reality may help you enter and explore the Metaverse. They integrate virtual and physical content to make metaverse interaction more fluid. The Metaverse provides a vast infrastructure for mixed-reality experiences. This is done by providing a platform for virtual content production and sharing, user connection, and social interactions (Siyaev and Jo 2021). The Metaverse may influence many areas of human existence. Technology might change social relations by allowing people from different regions to collaborate and communicate in virtual settings. Immersive experiences and interactive story frameworks may transform entertainment and gaming using virtual reality technology. Virtual learning environments, markets, and innovative business models in the Metaverse may alter education, commerce, and other industries. Companies and developers are working to create the Metaverse, which is still under development. Technology leaders and entrepreneurs invest in virtual reality, augmented reality, and related technologies to build a metaverse. Open standards, interoperability, and safe technologies are essential for a decentralized, inclusive metaverse that benefits all stakeholders. It is crucial to explore mixed reality and metaverse challenges and concerns. To create a sustainable and inclusive metaverse, privacy, security, ethical considerations, digital ownership, and the digital divide must be addressed to protect individual rights and promote positive experiences (Liu et al. 2023 ). Mixed reality and the Metaverse are interconnected and may change how we use digital media and communicate. The Metaverse provides a more extensive framework for collaborative, immersive, and interrelated virtual interactions. The prospective effects of these principles on human connection, communication, and digital experiences as technology and the metaverse advance are intriguing.

6.5.2 The integration of immersive realities through the utilization of holograms and the Metaverse.

Hologram optics and photonics research is extensive. Science and engineering researchers have investigated many methods to build high-quality holographic displays and enhance hologram recording and reconstruction. Academic research has focused on holographic materials, recording mediums, and display technologies (He et al. 2023 ). Metaverse research spans computer science, human–computer interaction, virtual reality, and social sciences. Scholars have studied immersive and interactive virtual worlds, genuine personalities and digital representations, and metaverse communication and interaction systems. The metaverse and holographic technologies provide new academic opportunities. Academic scholars may study the technological challenges of holographic displays in virtual reality. This involves improving real-time holographic rendering and developing new projection methods to improve immersive experiences. Scholars may also study how holographic portrayals affect metaverse user engagement, immersion, and communication. Analyzing user perceptions and experiences with holographic objects or avatars is one possibility. Researchers may also examine how holograms improve virtual communication and cooperation (Upadhyay and Khandelwal 2022 ). Academic institutions may build specialized research labs or institutes to study holography and the Metaverse, boosting cooperation between optics, computer graphics, and human factors experts. The collaborations might advance holographic technology and provide more immersive and genuine metaverse experiences. Furthermore, researchers might analyze the ethical and social effects of holographic technology and the Metaverse. One possibility is to study the impact of broad holographic representations in education, entertainment, and communication (Dwivedi et al. 2022 ). To conclude, holograms and the Metaverse are studied across many academic fields. Technology scholars are developing holographic technologies, creating and executing immersive virtual worlds, and studying their social and ethical effects. The authors’ study might lead to new holography and metaverse applications and experiences.

7 Future research implications of metaverse-AI convergence

The metaverse and AI convergence opens new virtual experiences and interactions. This symbiotic relationship’s potential advantages and drawbacks needs more examination.

Academics & Researchers: Researchers benefit from a comprehensive metaverse and AI research study. This attempt helps identify knowledge gaps, builds on previous work, and stimulates the exploration of unexplored territory in this subject.

Impact on Technology Companies and Developers: Systematic assessments can forecast metaverse-AI developments. These insights help technology organizations and developers enhance current technologies, uncover unexplored opportunities, and make educated product and investment choices.

Policymakers/Regulators: As the Metaverse and AI advance, policymakers and regulators must understand their potential impacts, possibilities, and difficulties. Evidence-based policies, regulatory frameworks, and ethical norms may be developed and used from a systematic review.

Implications for Entrepreneurs and Startups: A detailed assessment may aid entrepreneurs and startups entering the Metaverse and AI. This endeavour helps analyze the marketplace, find profitable possibilities, and provide insights to create new goods and services.

Investors and Venture Capitalists: Systematic assessments may help investors evaluate research progress, economic prospects, and technology improvements in the metaverse and AI initiatives. These analyses help investors choose potential enterprises.

Value to Educators and Students: Systematic reviews provide complete Metaverse and AI insights. These evaluations help educators build courses, direct student research, and improve knowledge of this diverse topic.

Healthcare and Medicine: The Metaverse and AI can alter healthcare and medicine. Virtual simulations and AI-powered medical models may transform medical education and patient care by delivering realistic training situations and individualized treatment plans based on patient data.

Implications for Agriculture and Environment Conservation: The Metaverse and AI provide great prospects for sustainable agriculture and environmental preservation. AI-powered metaverse apps improve resource use and reduce environmental effects.

A rigorous investigation of the metaverse-AI nexus finds many possible ramifications across many areas. This study emphasizes the significance of educated decision-making, ethical concerns, and extensive research to harness this strong integration’s revolutionary potential properly. The Metaverse and AI have promising futures, requiring ongoing research and multidisciplinary cooperation.

8 Conclusion

In conclusion, a notable research gap persists in exploring different aspects of the Metaverse and resolving prevailing issues and obstacles in this domain. The systematic analysis conducted in this study has underscored the necessity for additional investigation into AI methods to enhance trustworthiness. Furthermore, a more comprehensive assessment of the performance of deep learning and machine learning approaches is required to cultivate dependable and precise models. Enhancing comprehensive datasets, thorough scrutiny, and evaluations of proposed methodologies and applications also emerge as improvement areas. The immersive digital world of the Metaverse, driven by virtual reality, presents the potential for limitless encounters and is closely intertwined with the advancement of AI, sparking considerable academic intrigue. Prospective implications encompass Entrepreneurs and Startups, Investors and Venture Capitalists, Educators and Students, and Healthcare and Medicine, where AI-driven virtual simulations and medical models have the potential to revolutionize medical training and patient care through realistic scenarios and tailored treatment plans based on patient-specific data. Policymakers must prioritize funding for interdisciplinary research, establish regulatory frameworks for responsible AI deployment, and promote collaboration to address ethical considerations in the Metaverse. Moreover, a Metaverse enriched by AI introduces dynamic settings, intelligent avatars, and personalized experiences, enriching realism and engagement. However, this also introduces ethical deliberations, necessitating a harmonious balance between innovation and the responsible utilization of technology to ensure a constructive and secure digital cosmos. Sustained exploration and progress in AI methodologies, data accessibility, and evaluation approaches are imperative to tackle the gaps and challenges in the Metaverse arena and augment our capacity to mitigate their consequences effectively.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Artificial intelligence

Science direct

IEEE Xplore

Web of science

Intelligent non-player characters

Human–computer Interaction

Internet of things

Preferred Reporting Items for Systematic Reviews and Meta-Analysis

Convolutional neural networks

Augmented reality

Arabic sign language

Virtual reality

Deep learning-based asymmetric virtual environment

Adaptive accelerated learning

Circular waveguide to coaxial transformer

Deep autoencoder

Generative adversarial network

Medical technology and AI

Proton-exchange membrane fuel cells

Deep neural network

Electromyogram

Facial expression recognition

Maximum mean discrepancy

Contrastive adaptation network

Convolutional recurrent neural network

Valence aware dictionary and sentiment reasoner

Light gradient-boosting machine

Term frequency–inverse document frequency

Triboelectric nanogenerator

Borderline personality disorder

Extended reality

Digital twin

Deep reinforcement learning

3D morphable model

Mixed reality

Gated recurrent unit

Recurrent neural network

Long short-term memory

Human–machine interfaces

All-in-one multifunctional

Three dimensional

Federal trade commission

2D visual attention

User interface

Explainable AI

Electroencephalogram

General data protection regulation

Multi-criteria decision making

Fuzzy Weighted with zero Inconsistency

Fuzzy decision-by-opinion score method

Natural language processing

Natural language generation

Multi-task cascaded convolutional networks

You only look once

Markov decision process

Random forest

K-nearest neighbor

Logistic regression

Root mean square error

Equal error rate

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Mohammed A. Fadhel: Methodology, Writing—Original Draft, Writing—Review & Editing, Visualization Ali M. Duhaim: Methodology, Writing—Original Draft, Writing—Review & Editing, Visualization A. S. Albahri: Supervision, Methodology, Writing—Original Draft, Writing—Review & Editing, Visualization Z.T.Al-Qaysi: Conceptualization, Methodology, Software, Validation, Formal analysis, Review & Editing, Visualization M. A. Chyad: Software, Validation, Formal analysis, Review & Editing, Visualization Wael Abd-Alaziz: Software, Formal analysis, Review & Editing Laith Alzubaidi: Conceptualization, Methodology, Supervision, Validation, Writing -Original Draft, Writing—Review & Editing, Formal analysis, Funding O.S. Albahri: Supervision, Validation, Formal analysis, Investigation, Writing -Original Draft, Writing—Review and editing, Visualization A.H. Alamoodi: Supervision, Data Curation, Validation, Writing—Review & Editing Ashish Gupta: Software, Formal analysis, Review & Editing, Visualization, Funding Yuantong Gu: Supervision, Validation, Writing -Original Draft, Writing—Review & Editing, Funding All authors reviewed the manuscript.

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Fadhel, M.A., Duhaim, A.M., Albahri, A.S. et al. Navigating the metaverse: unraveling the impact of artificial intelligence—a comprehensive review and gap analysis. Artif Intell Rev 57 , 264 (2024). https://doi.org/10.1007/s10462-024-10881-5

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The Roles of Computer as a tool in Educational Research. By. T olorunleke, Emmanuel Adebayo (Ph.D.) Department of Educational Foundations, Faculty of Education, Kogi State University, Anyigba ...
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The 21st century has witnessed a paradigm shift in the education sector, largely propelled by the pervasive impact of computer technology. This essay delves into the multifaceted advantages of integrating computers into education, exploring key areas such as information storage, quick data processing, audio-visual aids, internet access, and communication among students, teachers, and parents.
Role Of Computer In Education
The role of computer in education is vital, revolutionizing learning methods, enhancing interactivity, and enabling access to vast knowledge resources.
Essay on Computer Education
High-quality essay on the topic of "Computer Education" for students in schools and colleges.
Using Computers in Education
Computers may be used to create and print question papers, mark lists, letters, posters, and banners as well as to keep track of things like student fees and attendance information. Computers are also employed in the field of education to develop syllabi and timetables, verify test papers, and create results.
Use Of Computers In Education Education Essay
The usage of technology in a school plays an important role. It is said to be one of the factors that cause a digital divide amongst schools. Reasons are as followed; putting computers in the classroom does not automatically decrease the divide. The outcome of investing in computers should be for the benefit of the students.
Role of Computers in Education
Role of Computers in Education The computer technology has a deep impact on education. Computer education forms a part of the school and college curricula, asit is important for every individual …show more content…
Computers in Education Essay
Computers have become ubiquitous in modern society and have revolutionized traditional education by becoming valuable resources in classrooms. Computers provide students with a wealth of information to develop their research and communication skills while preparing them for computer-reliant careers. Educational software personalizes instruction to target each student's unique academic needs ...
The Role of Computers in Education
The advantages of computers in education include an efficient storage and performance of information, quick information processing and very importantly the saving of paper. Know more about the importance of computer education. Computer teaching plays a key role in the modern systems of education.
Study shows how students and teachers are using AI for college essays
A new study from Seattle-based education research organization foundry10 found that 30% of students and teachers surveyed are using AI to help with the college essay and letters of recommendation ...
Navigating the metaverse: unraveling the impact of artificial
In response to the burgeoning interest in the Metaverse—a virtual reality-driven immersive digital world—this study delves into the pivotal role of AI in shaping its functionalities and elevating user engagement. Focused on recent advancements, prevailing challenges, and potential future developments, our research draws from a comprehensive analysis grounded in meticulous methodology. The ...