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• 10 February 2020

Scrutinizing the effects of digital technology on mental health

• Jonathan Haidt &

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The topic in brief

• There is an ongoing debate about whether social media and the use of digital devices are detrimental to mental health.

• Adolescents tend to be heavy users of these devices, and especially of social media.

• Rates of teenage depression began to rise around 2012, when adolescent use of social media became common (Fig. 1).

• Some evidence indicates that frequent users of social media have higher rates of depression and anxiety than do light users.

• But perhaps digital devices could provide a way of gathering data about mental health in a systematic way, and make interventions more timely.

Figure 1 | Depression on the rise. Rates of depression among teenagers in the United States have increased steadily since 2012. Rates are higher and are increasing more rapidly for girls than for boys. Some researchers think that social media is the cause of this increase, whereas others see social media as a way of tackling it. (Data taken from the US National Survey on Drug Use and Health, Table 11.2b; go.nature.com/3ayjaww )

JONATHAN HAIDT: A guilty verdict

A sudden increase in the rates of depression, anxiety and self-harm was seen in adolescents — particularly girls — in the United States and the United Kingdom around 2012 or 2013 (see go.nature.com/2up38hw ). Only one suspect was in the right place at the right time to account for this sudden change: social media. Its use by teenagers increased most quickly between 2009 and 2011, by which point two-thirds of 15–17-year-olds were using it on a daily basis 1 . Some researchers defend social media, arguing that there is only circumstantial evidence for its role in mental-health problems 2 , 3 . And, indeed, several studies 2 , 3 show that there is only a small correlation between time spent on screens and bad mental-health outcomes. However, I present three arguments against this defence.

First, the papers that report small or null effects usually focus on ‘screen time’, but it is not films or video chats with friends that damage mental health. When research papers allow us to zoom in on social media, rather than looking at screen time as a whole, the correlations with depression are larger, and they are larger still when we look specifically at girls ( go.nature.com/2u74der ). The sex difference is robust, and there are several likely causes for it. Girls use social media much more than do boys (who, in turn, spend more of their time gaming). And, for girls more than boys, social life and status tend to revolve around intimacy and inclusion versus exclusion 4 , making them more vulnerable to both the ‘fear of missing out’ and the relational aggression that social media facilitates.

Second, although correlational studies can provide only circumstantial evidence, most of the experiments published in recent years have found evidence of causation ( go.nature.com/2u74der ). In these studies, people are randomly assigned to groups that are asked to continue using social media or to reduce their use substantially. After a few weeks, people who reduce their use generally report an improvement in mood or a reduction in loneliness or symptoms of depression.

The best way forward

Third, many researchers seem to be thinking about social media as if it were sugar: safe in small to moderate quantities, and harmful only if teenagers consume large quantities. But, unlike sugar, social media does not act just on those who consume it. It has radically transformed the nature of peer relationships, family relationships and daily activities 5 . When most of the 11-year-olds in a class are on Instagram (as was the case in my son’s school), there can be pervasive effects on everyone. Children who opt out can find themselves isolated. A simple dose–response model cannot capture the full effects of social media, yet nearly all of the debate among researchers so far has been over the size of the dose–response effect. To cite just one suggestive finding of what lies beyond that model: network effects for depression and anxiety are large, and bad mental health spreads more contagiously between women than between men 6 .

In conclusion, digital media in general undoubtedly has many beneficial uses, including the treatment of mental illness. But if you focus on social media, you’ll find stronger evidence of harm, and less exculpatory evidence, especially for its millions of under-age users.

What should we do while researchers hash out the meaning of these conflicting findings? I would urge a focus on middle schools (roughly 11–13-year-olds in the United States), both for researchers and policymakers. Any US state could quickly conduct an informative experiment beginning this September: randomly assign a portion of school districts to ban smartphone access for students in middle school, while strongly encouraging parents to prevent their children from opening social-media accounts until they begin high school (at around 14). Within 2 years, we would know whether the policy reversed the otherwise steady rise of mental-health problems among middle-school students, and whether it also improved classroom dynamics (as rated by teachers) and test scores. Such system-wide and cross-school interventions would be an excellent way to study the emergent effects of social media on the social lives and mental health of today’s adolescents.

NICK ALLEN: Use digital technology to our advantage

It is appealing to condemn social media out of hand on the basis of the — generally rather poor-quality and inconsistent — evidence suggesting that its use is associated with mental-health problems 7 . But focusing only on its potential harmful effects is comparable to proposing that the only question to ask about cars is whether people can die driving them. The harmful effects might be real, but they don’t tell the full story. The task of research should be to understand what patterns of digital-device and social-media use can lead to beneficial versus harmful effects 7 , and to inform evidence-based approaches to policy, education and regulation.

Long-standing problems have hampered our efforts to improve access to, and the quality of, mental-health services and support. Digital technology has the potential to address some of these challenges. For instance, consider the challenges associated with collecting data on human behaviour. Assessment in mental-health care and research relies almost exclusively on self-reporting, but the resulting data are subjective and burdensome to collect. As a result, assessments are conducted so infrequently that they do not provide insights into the temporal dynamics of symptoms, which can be crucial for both diagnosis and treatment planning.

By contrast, mobile phones and other Internet-connected devices provide an opportunity to continuously collect objective information on behaviour in the context of people’s real lives, generating a rich data set that can provide insight into the extent and timing of mental-health needs in individuals 8 , 9 . By building apps that can track our digital exhaust (the data generated by our everyday digital lives, including our social-media use), we can gain insights into aspects of behaviour that are well-established building blocks of mental health and illness, such as mood, social communication, sleep and physical activity.

Stress and the city

These data can, in turn, be used to empower individuals, by giving them actionable insights into patterns of behaviour that might otherwise have remained unseen. For example, subtle shifts in patterns of sleep or social communication can provide early warning signs of deteriorating mental health. Data on these patterns can be used to alert people to the need for self-management before the patterns — and the associated symptoms — become more severe. Individuals can also choose to share these data with health professionals or researchers. For instance, in the Our Data Helps initiative, individuals who have experienced a suicidal crisis, or the relatives of those who have died by suicide, can donate their digital data to research into suicide risk.

Because mobile devices are ever-present in people’s lives, they offer an opportunity to provide interventions that are timely, personalized and scalable. Currently, mental-health services are mainly provided through a century-old model in which they are made available at times chosen by the mental-health practitioner, rather than at the person’s time of greatest need. But Internet-connected devices are facilitating the development of a wave of ‘just-in-time’ interventions 10 for mental-health care and support.

A compelling example of these interventions involves short-term risk for suicide 9 , 11 — for which early detection could save many lives. Most of the effective approaches to suicide prevention work by interrupting suicidal actions and supporting alternative methods of coping at the moment of greatest risk. If these moments can be detected in an individual’s digital exhaust, a wide range of intervention options become available, from providing information about coping skills and social support, to the initiation of crisis responses. So far, just-in-time approaches have been applied mainly to behaviours such as eating or substance abuse 8 . But with the development of an appropriate research base, these approaches have the potential to provide a major advance in our ability to respond to, and prevent, mental-health crises.

These advantages are particularly relevant to teenagers. Because of their extensive use of digital devices, adolescents are especially vulnerable to the devices’ risks and burdens. And, given the increases in mental-health problems in this age group, teens would also benefit most from improvements in mental-health prevention and treatment. If we use the social and data-gathering functions of Internet-connected devices in the right ways, we might achieve breakthroughs in our ability to improve mental health and well-being.

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Competing Interests

N.A. has an equity interest in Ksana Health, a company he co-founded and which has the sole commercial licence for certain versions of the Effortless Assessment of Risk States (EARS) mobile-phone application and some related EARS tools. This intellectual property was developed as part of his research at the University of Oregon’s Center for Digital Mental Health (CDMH).

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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  

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.

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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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Study and Investigation on 5G Technology: A Systematic Review

Ramraj dangi.

1 School of Computing Science and Engineering, VIT University Bhopal, Bhopal 466114, India; [email protected] (R.D.); [email protected] (P.L.)

Praveen Lalwani

Gaurav choudhary.

2 Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark; moc.liamg@7777yrahduohcvaruag

3 Department of Information Security Engineering, Soonchunhyang University, Asan-si 31538, Korea

Giovanni Pau

4 Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy; [email protected]

Associated Data

Not applicable.

In wireless communication, Fifth Generation (5G) Technology is a recent generation of mobile networks. In this paper, evaluations in the field of mobile communication technology are presented. In each evolution, multiple challenges were faced that were captured with the help of next-generation mobile networks. Among all the previously existing mobile networks, 5G provides a high-speed internet facility, anytime, anywhere, for everyone. 5G is slightly different due to its novel features such as interconnecting people, controlling devices, objects, and machines. 5G mobile system will bring diverse levels of performance and capability, which will serve as new user experiences and connect new enterprises. Therefore, it is essential to know where the enterprise can utilize the benefits of 5G. In this research article, it was observed that extensive research and analysis unfolds different aspects, namely, millimeter wave (mmWave), massive multiple-input and multiple-output (Massive-MIMO), small cell, mobile edge computing (MEC), beamforming, different antenna technology, etc. This article’s main aim is to highlight some of the most recent enhancements made towards the 5G mobile system and discuss its future research objectives.

1. Introduction

Most recently, in three decades, rapid growth was marked in the field of wireless communication concerning the transition of 1G to 4G [ 1 , 2 ]. The main motto behind this research was the requirements of high bandwidth and very low latency. 5G provides a high data rate, improved quality of service (QoS), low-latency, high coverage, high reliability, and economically affordable services. 5G delivers services categorized into three categories: (1) Extreme mobile broadband (eMBB). It is a nonstandalone architecture that offers high-speed internet connectivity, greater bandwidth, moderate latency, UltraHD streaming videos, virtual reality and augmented reality (AR/VR) media, and many more. (2) Massive machine type communication (eMTC), 3GPP releases it in its 13th specification. It provides long-range and broadband machine-type communication at a very cost-effective price with less power consumption. eMTC brings a high data rate service, low power, extended coverage via less device complexity through mobile carriers for IoT applications. (3) ultra-reliable low latency communication (URLLC) offers low-latency and ultra-high reliability, rich quality of service (QoS), which is not possible with traditional mobile network architecture. URLLC is designed for on-demand real-time interaction such as remote surgery, vehicle to vehicle (V2V) communication, industry 4.0, smart grids, intelligent transport system, etc. [ 3 ].

1.1. Evolution from 1G to 5G

First generation (1G): 1G cell phone was launched between the 1970s and 80s, based on analog technology, which works just like a landline phone. It suffers in various ways, such as poor battery life, voice quality, and dropped calls. In 1G, the maximum achievable speed was 2.4 Kbps.

Second Generation (2G): In 2G, the first digital system was offered in 1991, providing improved mobile voice communication over 1G. In addition, Code-Division Multiple Access (CDMA) and Global System for Mobile (GSM) concepts were also discussed. In 2G, the maximum achievable speed was 1 Mpbs.

Third Generation (3G): When technology ventured from 2G GSM frameworks into 3G universal mobile telecommunication system (UMTS) framework, users encountered higher system speed and quicker download speed making constant video calls. 3G was the first mobile broadband system that was formed to provide the voice with some multimedia. The technology behind 3G was high-speed packet access (HSPA/HSPA+). 3G used MIMO for multiplying the power of the wireless network, and it also used packet switching for fast data transmission.

Fourth Generation (4G): It is purely mobile broadband standard. In digital mobile communication, it was observed information rate that upgraded from 20 to 60 Mbps in 4G [ 4 ]. It works on LTE and WiMAX technologies, as well as provides wider bandwidth up to 100 Mhz. It was launched in 2010.

Fourth Generation LTE-A (4.5G): It is an advanced version of standard 4G LTE. LTE-A uses MIMO technology to combine multiple antennas for both transmitters as well as a receiver. Using MIMO, multiple signals and multiple antennas can work simultaneously, making LTE-A three times faster than standard 4G. LTE-A offered an improved system limit, decreased deferral in the application server, access triple traffic (Data, Voice, and Video) wirelessly at any time anywhere in the world.LTE-A delivers speeds of over 42 Mbps and up to 90 Mbps.

Fifth Generation (5G): 5G is a pillar of digital transformation; it is a real improvement on all the previous mobile generation networks. 5G brings three different services for end user like Extreme mobile broadband (eMBB). It offers high-speed internet connectivity, greater bandwidth, moderate latency, UltraHD streaming videos, virtual reality and augmented reality (AR/VR) media, and many more. Massive machine type communication (eMTC), it provides long-range and broadband machine-type communication at a very cost-effective price with less power consumption. eMTC brings a high data rate service, low power, extended coverage via less device complexity through mobile carriers for IoT applications. Ultra-reliable low latency communication (URLLC) offers low-latency and ultra-high reliability, rich quality of service (QoS), which is not possible with traditional mobile network architecture. URLLC is designed for on-demand real-time interaction such as remote surgery, vehicle to vehicle (V2V) communication, industry 4.0, smart grids, intelligent transport system, etc. 5G faster than 4G and offers remote-controlled operation over a reliable network with zero delays. It provides down-link maximum throughput of up to 20 Gbps. In addition, 5G also supports 4G WWWW (4th Generation World Wide Wireless Web) [ 5 ] and is based on Internet protocol version 6 (IPv6) protocol. 5G provides unlimited internet connection at your convenience, anytime, anywhere with extremely high speed, high throughput, low-latency, higher reliability and scalability, and energy-efficient mobile communication technology [ 6 ]. 5G mainly divided in two parts 6 GHz 5G and Millimeter wave(mmWave) 5G.

6 GHz is a mid frequency band which works as a mid point between capacity and coverage to offer perfect environment for 5G connectivity. 6 GHz spectrum will provide high bandwidth with improved network performance. It offers continuous channels that will reduce the need for network densification when mid-band spectrum is not available and it makes 5G connectivity affordable at anytime, anywhere for everyone.

mmWave is an essential technology of 5G network which build high performance network. 5G mmWave offer diverse services that is why all network providers should add on this technology in their 5G deployment planning. There are lots of service providers who deployed 5G mmWave, and their simulation result shows that 5G mmwave is a far less used spectrum. It provides very high speed wireless communication and it also offers ultra-wide bandwidth for next generation mobile network.

The evolution of wireless mobile technologies are presented in Table 1 . The abbreviations used in this paper are mentioned in Table 2 .

Summary of Mobile Technology.

Table of Notations and Abbreviations.

1.2. Key Contributions

The objective of this survey is to provide a detailed guide of 5G key technologies, methods to researchers, and to help with understanding how the recent works addressed 5G problems and developed solutions to tackle the 5G challenges; i.e., what are new methods that must be applied and how can they solve problems? Highlights of the research article are as follows.

• This survey focused on the recent trends and development in the era of 5G and novel contributions by the researcher community and discussed technical details on essential aspects of the 5G advancement.
• In this paper, the evolution of the mobile network from 1G to 5G is presented. In addition, the growth of mobile communication under different attributes is also discussed.
• This paper covers the emerging applications and research groups working on 5G & different research areas in 5G wireless communication network with a descriptive taxonomy.
• This survey discusses the current vision of the 5G networks, advantages, applications, key technologies, and key features. Furthermore, machine learning prospects are also explored with the emerging requirements in the 5G era. The article also focused on technical aspects of 5G IoT Based approaches and optimization techniques for 5G.
• we provide an extensive overview and recent advancement of emerging technologies of 5G mobile network, namely, MIMO, Non-Orthogonal Multiple Access (NOMA), mmWave, Internet of Things (IoT), Machine Learning (ML), and optimization. Also, a technical summary is discussed by highlighting the context of current approaches and corresponding challenges.
• Security challenges and considerations while developing 5G technology are discussed.
• Finally, the paper concludes with the future directives.

The existing survey focused on architecture, key concepts, and implementation challenges and issues. In contrast, this survey covers the state-of-the-art techniques as well as corresponding recent novel developments by researchers. Various recent significant papers are discussed with the key technologies accelerating the development and production of 5G products.

2. Existing Surveys and Their Applicability

In this paper, a detailed survey on various technologies of 5G networks is presented. Various researchers have worked on different technologies of 5G networks. In this section, Table 3 gives a tabular representation of existing surveys of 5G networks. Massive MIMO, NOMA, small cell, mmWave, beamforming, and MEC are the six main pillars that helped to implement 5G networks in real life.

A comparative overview of existing surveys on different technologies of 5G networks.

2.1. Limitations of Existing Surveys

The existing survey focused on architecture, key concepts, and implementation challenges and issues. The numerous current surveys focused on various 5G technologies with different parameters, and the authors did not cover all the technologies of the 5G network in detail with challenges and recent advancements. Few authors worked on MIMO (Non-Orthogonal Multiple Access) NOMA, MEC, small cell technologies. In contrast, some others worked on beamforming, Millimeter-wave (mmWave). But the existing survey did not cover all the technologies of the 5G network from a research and advancement perspective. No detailed survey is available in the market covering all the 5G network technologies and currently published research trade-offs. So, our main aim is to give a detailed study of all the technologies working on the 5G network. In contrast, this survey covers the state-of-the-art techniques as well as corresponding recent novel developments by researchers. Various recent significant papers are discussed with the key technologies accelerating the development and production of 5G products. This survey article collected key information about 5G technology and recent advancements, and it can be a kind of a guide for the reader. This survey provides an umbrella approach to bring multiple solutions and recent improvements in a single place to accelerate the 5G research with the latest key enabling solutions and reviews. A systematic layout representation of the survey in Figure 1 . We provide a state-of-the-art comparative overview of the existing surveys on different technologies of 5G networks in Table 3 .

Systematic layout representation of survey.

2.2. Article Organization

This article is organized under the following sections. Section 2 presents existing surveys and their applicability. In Section 3 , the preliminaries of 5G technology are presented. In Section 4 , recent advances of 5G technology based on Massive MIMO, NOMA, Millimeter Wave, 5G with IoT, machine learning for 5G, and Optimization in 5G are provided. In Section 5 , a description of novel 5G features over 4G is provided. Section 6 covered all the security concerns of the 5G network. Section 7 , 5G technology based on above-stated challenges summarize in tabular form. Finally, Section 8 and Section 9 conclude the study, which paves the path for future research.

3. Preliminary Section

3.1. emerging 5g paradigms and its features.

5G provides very high speed, low latency, and highly salable connectivity between multiple devices and IoT worldwide. 5G will provide a very flexible model to develop a modern generation of applications and industry goals [ 26 , 27 ]. There are many services offered by 5G network architecture are stated below:

Massive machine to machine communications: 5G offers novel, massive machine-to-machine communications [ 28 ], also known as the IoT [ 29 ], that provide connectivity between lots of machines without any involvement of humans. This service enhances the applications of 5G and provides connectivity between agriculture, construction, and industries [ 30 ].

Ultra-reliable low latency communications (URLLC): This service offers real-time management of machines, high-speed vehicle-to-vehicle connectivity, industrial connectivity and security principles, and highly secure transport system, and multiple autonomous actions. Low latency communications also clear up a different area where remote medical care, procedures, and operation are all achievable [ 31 ].

Enhanced mobile broadband: Enhance mobile broadband is an important use case of 5G system, which uses massive MIMO antenna, mmWave, beamforming techniques to offer very high-speed connectivity across a wide range of areas [ 32 ].

For communities: 5G provides a very flexible internet connection between lots of machines to make smart homes, smart schools, smart laboratories, safer and smart automobiles, and good health care centers [ 33 ].

For businesses and industry: As 5G works on higher spectrum ranges from 24 to 100 GHz. This higher frequency range provides secure low latency communication and high-speed wireless connectivity between IoT devices and industry 4.0, which opens a market for end-users to enhance their business models [ 34 ].

New and Emerging technologies: As 5G came up with many new technologies like beamforming, massive MIMO, mmWave, small cell, NOMA, MEC, and network slicing, it introduced many new features to the market. Like virtual reality (VR), users can experience the physical presence of people who are millions of kilometers away from them. Many new technologies like smart homes, smart workplaces, smart schools, smart sports academy also came into the market with this 5G Mobile network model [ 35 ].

3.2. Commercial Service Providers of 5G

5G provides high-speed internet browsing, streaming, and downloading with very high reliability and low latency. 5G network will change your working style, and it will increase new business opportunities and provide innovations that we cannot imagine. This section covers top service providers of 5G network [ 36 , 37 ].

Ericsson: Ericsson is a Swedish multinational networking and telecommunications company, investing around 25.62 billion USD in 5G network, which makes it the biggest telecommunication company. It claims that it is the only company working on all the continents to make the 5G network a global standard for the next generation wireless communication. Ericsson developed the first 5G radio prototype that enables the operators to set up the live field trials in their network, which helps operators understand how 5G reacts. It plays a vital role in the development of 5G hardware. It currently provides 5G services in over 27 countries with content providers like China Mobile, GCI, LGU+, AT&T, Rogers, and many more. It has 100 commercial agreements with different operators as of 2020.

Verizon: It is American multinational telecommunication which was founded in 1983. Verizon started offering 5G services in April 2020, and by December 2020, it has actively provided 5G services in 30 cities of the USA. They planned that by the end of 2021, they would deploy 5G in 30 more new cities. Verizon deployed a 5G network on mmWave, a very high band spectrum between 30 to 300 GHz. As it is a significantly less used spectrum, it provides very high-speed wireless communication. MmWave offers ultra-wide bandwidth for next-generation mobile networks. MmWave is a faster and high-band spectrum that has a limited range. Verizon planned to increase its number of 5G cells by 500% by 2020. Verizon also has an ultra wide-band flagship 5G service which is the best 5G service that increases the market price of Verizon.

Nokia: Nokia is a Finnish multinational telecommunications company which was founded in 1865. Nokia is one of the companies which adopted 5G technology very early. It is developing, researching, and building partnerships with various 5G renders to offer 5G communication as soon as possible. Nokia collaborated with Deutsche Telekom and Hamburg Port Authority and provided them 8000-hectare site for their 5G MoNArch project. Nokia is the only company that supplies 5G technology to all the operators of different countries like AT&T, Sprint, T-Mobile US and Verizon in the USA, Korea Telecom, LG U+ and SK Telecom in South Korea and NTT DOCOMO, KDDI, and SoftBank in Japan. Presently, Nokia has around 150+ agreements and 29 live networks all over the world. Nokia is continuously working hard on 5G technology to expand 5G networks all over the globe.

AT&T: AT&T is an American multinational company that was the first to deploy a 5G network in reality in 2018. They built a gigabit 5G network connection in Waco, TX, Kalamazoo, MI, and South Bend to achieve this. It is the first company that archives 1–2 gigabit per second speed in 2019. AT&T claims that it provides a 5G network connection among 225 million people worldwide by using a 6 GHz spectrum band.

T-Mobile: T-Mobile US (TMUS) is an American wireless network operator which was the first service provider that offers a real 5G nationwide network. The company knew that high-band 5G was not feasible nationwide, so they used a 600 MHz spectrum to build a significant portion of its 5G network. TMUS is planning that by 2024 they will double the total capacity and triple the full 5G capacity of T-Mobile and Sprint combined. The sprint buyout is helping T-Mobile move forward the company’s current market price to 129.98 USD.

Samsung: Samsung started their research in 5G technology in 2011. In 2013, Samsung successfully developed the world’s first adaptive array transceiver technology operating in the millimeter-wave Ka bands for cellular communications. Samsung provides several hundred times faster data transmission than standard 4G for core 5G mobile communication systems. The company achieved a lot of success in the next generation of technology, and it is considered one of the leading companies in the 5G domain.

Qualcomm: Qualcomm is an American multinational corporation in San Diego, California. It is also one of the leading company which is working on 5G chip. Qualcomm’s first 5G modem chip was announced in October 2016, and a prototype was demonstrated in October 2017. Qualcomm mainly focuses on building products while other companies talk about 5G; Qualcomm is building the technologies. According to one magazine, Qualcomm was working on three main areas of 5G networks. Firstly, radios that would use bandwidth from any network it has access to; secondly, creating more extensive ranges of spectrum by combining smaller pieces; and thirdly, a set of services for internet applications.

ZTE Corporation: ZTE Corporation was founded in 1985. It is a partially Chinese state-owned technology company that works in telecommunication. It was a leading company that worked on 4G LTE, and it is still maintaining its value and doing research and tests on 5G. It is the first company that proposed Pre5G technology with some series of solutions.

NEC Corporation: NEC Corporation is a Japanese multinational information technology and electronics corporation headquartered in Minato, Tokyo. ZTE also started their research on 5G, and they introduced a new business concept. NEC’s main aim is to develop 5G NR for the global mobile system and create secure and intelligent technologies to realize 5G services.

Cisco: Cisco is a USA networking hardware company that also sleeves up for 5G network. Cisco’s primary focus is to support 5G in three ways: Service—enable 5G services faster so all service providers can increase their business. Infrastructure—build 5G-oriented infrastructure to implement 5G more quickly. Automation—make a more scalable, flexible, and reliable 5G network. The companies know the importance of 5G, and they want to connect more than 30 billion devices in the next couple of years. Cisco intends to work on network hardening as it is a vital part of 5G network. Cisco used AI with deep learning to develop a 5G Security Architecture, enabling Secure Network Transformation.

3.3. 5G Research Groups

Many research groups from all over the world are working on a 5G wireless mobile network [ 38 ]. These groups are continuously working on various aspects of 5G. The list of those research groups are presented as follows: 5GNOW (5th Generation Non-Orthogonal Waveform for Asynchronous Signaling), NEWCOM (Network of Excellence in Wireless Communication), 5GIC (5G Innovation Center), NYU (New York University) Wireless, 5GPPP (5G Infrastructure Public-Private Partnership), EMPHATIC (Enhanced Multi-carrier Technology for Professional Adhoc and Cell-Based Communication), ETRI(Electronics and Telecommunication Research Institute), METIS (Mobile and wireless communication Enablers for the Twenty-twenty Information Society) [ 39 ]. The various research groups along with the research area are presented in Table 4 .

Research groups working on 5G mobile networks.

3.4. 5G Applications

5G is faster than 4G and offers remote-controlled operation over a reliable network with zero delays. It provides down-link maximum throughput of up to 20 Gbps. In addition, 5G also supports 4G WWWW (4th Generation World Wide Wireless Web) [ 5 ] and is based on Internet protocol version 6 (IPv6) protocol. 5G provides unlimited internet connection at your convenience, anytime, anywhere with extremely high speed, high throughput, low-latency, higher reliability, greater scalablility, and energy-efficient mobile communication technology [ 6 ].

There are lots of applications of 5G mobile network are as follows:

• High-speed mobile network: 5G is an advancement on all the previous mobile network technologies, which offers very high speed downloading speeds 0 of up to 10 to 20 Gbps. The 5G wireless network works as a fiber optic internet connection. 5G is different from all the conventional mobile transmission technologies, and it offers both voice and high-speed data connectivity efficiently. 5G offers very low latency communication of less than a millisecond, useful for autonomous driving and mission-critical applications. 5G will use millimeter waves for data transmission, providing higher bandwidth and a massive data rate than lower LTE bands. As 5 Gis a fast mobile network technology, it will enable virtual access to high processing power and secure and safe access to cloud services and enterprise applications. Small cell is one of the best features of 5G, which brings lots of advantages like high coverage, high-speed data transfer, power saving, easy and fast cloud access, etc. [ 40 ].
• Entertainment and multimedia: In one analysis in 2015, it was found that more than 50 percent of mobile internet traffic was used for video downloading. This trend will surely increase in the future, which will make video streaming more common. 5G will offer High-speed streaming of 4K videos with crystal clear audio, and it will make a high definition virtual world on your mobile. 5G will benefit the entertainment industry as it offers 120 frames per second with high resolution and higher dynamic range video streaming, and HD TV channels can also be accessed on mobile devices without any interruptions. 5G provides low latency high definition communication so augmented reality (AR), and virtual reality (VR) will be very easily implemented in the future. Virtual reality games are trendy these days, and many companies are investing in HD virtual reality games. The 5G network will offer high-speed internet connectivity with a better gaming experience [ 41 ].
• Smart homes : smart home appliances and products are in demand these days. The 5G network makes smart homes more real as it offers high-speed connectivity and monitoring of smart appliances. Smart home appliances are easily accessed and configured from remote locations using the 5G network as it offers very high-speed low latency communication.
• Smart cities: 5G wireless network also helps develop smart cities applications such as automatic traffic management, weather update, local area broadcasting, energy-saving, efficient power supply, smart lighting system, water resource management, crowd management, emergency control, etc.
• Industrial IoT: 5G wireless technology will provide lots of features for future industries such as safety, process tracking, smart packing, shipping, energy efficiency, automation of equipment, predictive maintenance, and logistics. 5G smart sensor technology also offers smarter, safer, cost-effective, and energy-saving industrial IoT operations.
• Smart Farming: 5G technology will play a crucial role in agriculture and smart farming. 5G sensors and GPS technology will help farmers track live attacks on crops and manage them quickly. These smart sensors can also be used for irrigation, pest, insect, and electricity control.
• Autonomous Driving: The 5G wireless network offers very low latency high-speed communication, significant for autonomous driving. It means self-driving cars will come to real life soon with 5G wireless networks. Using 5G autonomous cars can easily communicate with smart traffic signs, objects, and other vehicles running on the road. 5G’s low latency feature makes self-driving more real as every millisecond is essential for autonomous vehicles, decision-making is done in microseconds to avoid accidents.
• Healthcare and mission-critical applications: 5G technology will bring modernization in medicine where doctors and practitioners can perform advanced medical procedures. The 5G network will provide connectivity between all classrooms, so attending seminars and lectures will be easier. Through 5G technology, patients can connect with doctors and take their advice. Scientists are building smart medical devices which can help people with chronic medical conditions. The 5G network will boost the healthcare industry with smart devices, the internet of medical things, smart sensors, HD medical imaging technologies, and smart analytics systems. 5G will help access cloud storage, so accessing healthcare data will be very easy from any location worldwide. Doctors and medical practitioners can easily store and share large files like MRI reports within seconds using the 5G network.
• Satellite Internet: In many remote areas, ground base stations are not available, so 5G will play a crucial role in providing connectivity in such areas. The 5G network will provide connectivity using satellite systems, and the satellite system uses a constellation of multiple small satellites to provide connectivity in urban and rural areas across the world.

4. 5G Technologies

This section describes recent advances of 5G Massive MIMO, 5G NOMA, 5G millimeter wave, 5G IOT, 5G with machine learning, and 5G optimization-based approaches. In addition, the summary is also presented in each subsection that paves the researchers for the future research direction.

4.1. 5G Massive MIMO

Multiple-input-multiple-out (MIMO) is a very important technology for wireless systems. It is used for sending and receiving multiple signals simultaneously over the same radio channel. MIMO plays a very big role in WI-FI, 3G, 4G, and 4G LTE-A networks. MIMO is mainly used to achieve high spectral efficiency and energy efficiency but it was not up to the mark MIMO provides low throughput and very low reliable connectivity. To resolve this, lots of MIMO technology like single user MIMO (SU-MIMO), multiuser MIMO (MU-MIMO) and network MIMO were used. However, these new MIMO also did not still fulfill the demand of end users. Massive MIMO is an advancement of MIMO technology used in the 5G network in which hundreds and thousands of antennas are attached with base stations to increase throughput and spectral efficiency. Multiple transmit and receive antennas are used in massive MIMO to increase the transmission rate and spectral efficiency. When multiple UEs generate downlink traffic simultaneously, massive MIMO gains higher capacity. Massive MIMO uses extra antennas to move energy into smaller regions of space to increase spectral efficiency and throughput [ 43 ]. In traditional systems data collection from smart sensors is a complex task as it increases latency, reduced data rate and reduced reliability. While massive MIMO with beamforming and huge multiplexing techniques can sense data from different sensors with low latency, high data rate and higher reliability. Massive MIMO will help in transmitting the data in real-time collected from different sensors to central monitoring locations for smart sensor applications like self-driving cars, healthcare centers, smart grids, smart cities, smart highways, smart homes, and smart enterprises [ 44 ].

Highlights of 5G Massive MIMO technology are as follows:

• Data rate: Massive MIMO is advised as the one of the dominant technologies to provide wireless high speed and high data rate in the gigabits per seconds.
• The relationship between wave frequency and antenna size: Both are inversely proportional to each other. It means lower frequency signals need a bigger antenna and vise versa.

Pictorial representation of multi-input and multi-output (MIMO).

• MIMO role in 5G: Massive MIMO will play a crucial role in the deployment of future 5G mobile communication as greater spectral and energy efficiency could be enabled.

State-of-the-Art Approaches

Plenty of approaches were proposed to resolve the issues of conventional MIMO [ 7 ].

The MIMO multirate, feed-forward controller is suggested by Mae et al. [ 46 ]. In the simulation, the proposed model generates the smooth control input, unlike the conventional MIMO, which generates oscillated control inputs. It also outperformed concerning the error rate. However, a combination of multirate and single rate can be used for better results.

The performance of stand-alone MIMO, distributed MIMO with and without corporation MIMO, was investigated by Panzner et al. [ 47 ]. In addition, an idea about the integration of large scale in the 5G technology was also presented. In the experimental analysis, different MIMO configurations are considered. The variation in the ratio of overall transmit antennas to spatial is deemed step-wise from equality to ten.

The simulation of massive MIMO noncooperative and cooperative systems for down-link behavior was performed by He et al. [ 48 ]. It depends on present LTE systems, which deal with various antennas in the base station set-up. It was observed that collaboration in different BS improves the system behaviors, whereas throughput is reduced slightly in this approach. However, a new method can be developed which can enhance both system behavior and throughput.

In [ 8 ], different approaches that increased the energy efficiency benefits provided by massive MIMO were presented. They analyzed the massive MIMO technology and described the detailed design of the energy consumption model for massive MIMO systems. This article has explored several techniques to enhance massive MIMO systems’ energy efficiency (EE) gains. This paper reviews standard EE-maximization approaches for the conventional massive MIMO systems, namely, scaling number of antennas, real-time implementing low-complexity operations at the base station (BS), power amplifier losses minimization, and radio frequency (RF) chain minimization requirements. In addition, open research direction is also identified.

In [ 49 ], various existing approaches based on different antenna selection and scheduling, user selection and scheduling, and joint antenna and user scheduling methods adopted in massive MIMO systems are presented in this paper. The objective of this survey article was to make awareness about the current research and future research direction in MIMO for systems. They analyzed that complete utilization of resources and bandwidth was the most crucial factor which enhances the sum rate.

In [ 50 ], authors discussed the development of various techniques for pilot contamination. To calculate the impact of pilot contamination in time division duplex (TDD) massive MIMO system, TDD and frequency division duplexing FDD patterns in massive MIMO techniques are used. They discussed different issues in pilot contamination in TDD massive MIMO systems with all the possible future directions of research. They also classified various techniques to generate the channel information for both pilot-based and subspace-based approaches.

In [ 19 ], the authors defined the uplink and downlink services for a massive MIMO system. In addition, it maintains a performance matrix that measures the impact of pilot contamination on different performances. They also examined the various application of massive MIMO such as small cells, orthogonal frequency-division multiplexing (OFDM) schemes, massive MIMO IEEE 802, 3rd generation partnership project (3GPP) specifications, and higher frequency bands. They considered their research work crucial for cutting edge massive MIMO and covered many issues like system throughput performance and channel state acquisition at higher frequencies.

In [ 13 ], various approaches were suggested for MIMO future generation wireless communication. They made a comparative study based on performance indicators such as peak data rate, energy efficiency, latency, throughput, etc. The key findings of this survey are as follows: (1) spatial multiplexing improves the energy efficiency; (2) design of MIMO play a vital role in the enhancement of throughput; (3) enhancement of mMIMO focusing on energy & spectral performance; (4) discussed the future challenges to improve the system design.

In [ 51 ], the study of large-scale MIMO systems for an energy-efficient system sharing method was presented. For the resource allocation, circuit energy and transmit energy expenditures were taken into consideration. In addition, the optimization techniques were applied for an energy-efficient resource sharing system to enlarge the energy efficiency for individual QoS and energy constraints. The author also examined the BS configuration, which includes homogeneous and heterogeneous UEs. While simulating, they discussed that the total number of transmit antennas plays a vital role in boosting energy efficiency. They highlighted that the highest energy efficiency was obtained when the BS was set up with 100 antennas that serve 20 UEs.

This section includes various works done on 5G MIMO technology by different author’s. Table 5 shows how different author’s worked on improvement of various parameters such as throughput, latency, energy efficiency, and spectral efficiency with 5G MIMO technology.

Summary of massive MIMO-based approaches in 5G technology.

4.2. 5G Non-Orthogonal Multiple Access (NOMA)

NOMA is a very important radio access technology used in next generation wireless communication. Compared to previous orthogonal multiple access techniques, NOMA offers lots of benefits like high spectrum efficiency, low latency with high reliability and high speed massive connectivity. NOMA mainly works on a baseline to serve multiple users with the same resources in terms of time, space and frequency. NOMA is mainly divided into two main categories one is code domain NOMA and another is power domain NOMA. Code-domain NOMA can improve the spectral efficiency of mMIMO, which improves the connectivity in 5G wireless communication. Code-domain NOMA was divided into some more multiple access techniques like sparse code multiple access, lattice-partition multiple access, multi-user shared access and pattern-division multiple access [ 52 ]. Power-domain NOMA is widely used in 5G wireless networks as it performs well with various wireless communication techniques such as MIMO, beamforming, space-time coding, network coding, full-duplex and cooperative communication etc. [ 53 ]. The conventional orthogonal frequency-division multiple access (OFDMA) used by 3GPP in 4G LTE network provides very low spectral efficiency when bandwidth resources are allocated to users with low channel state information (CSI). NOMA resolved this issue as it enables users to access all the subcarrier channels so bandwidth resources allocated to the users with low CSI can still be accessed by the users with strong CSI which increases the spectral efficiency. The 5G network will support heterogeneous architecture in which small cell and macro base stations work for spectrum sharing. NOMA is a key technology of the 5G wireless system which is very helpful for heterogeneous networks as multiple users can share their data in a small cell using the NOMA principle.The NOMA is helpful in various applications like ultra-dense networks (UDN), machine to machine (M2M) communication and massive machine type communication (mMTC). As NOMA provides lots of features it has some challenges too such as NOMA needs huge computational power for a large number of users at high data rates to run the SIC algorithms. Second, when users are moving from the networks, to manage power allocation optimization is a challenging task for NOMA [ 54 ]. Hybrid NOMA (HNOMA) is a combination of power-domain and code-domain NOMA. HNOMA uses both power differences and orthogonal resources for transmission among multiple users. As HNOMA is using both power-domain NOMA and code-domain NOMA it can achieve higher spectral efficiency than Power-domain NOMA and code-domain NOMA. In HNOMA multiple groups can simultaneously transmit signals at the same time. It uses a message passing algorithm (MPA) and successive interference cancellation (SIC)-based detection at the base station for these groups [ 55 ].

Highlights of 5G NOMA technology as follows:

Pictorial representation of orthogonal and Non-Orthogonal Multiple Access (NOMA).

• NOMA provides higher data rates and resolves all the loop holes of OMA that makes 5G mobile network more scalable and reliable.
• As multiple users use same frequency band simultaneously it increases the performance of whole network.
• To setup intracell and intercell interference NOMA provides nonorthogonal transmission on the transmitter end.
• The primary fundamental of NOMA is to improve the spectrum efficiency by strengthening the ramification of receiver.

State-of-the-Art of Approaches

A plenty of approaches were developed to address the various issues in NOMA.

A novel approach to address the multiple receiving signals at the same frequency is proposed in [ 22 ]. In NOMA, multiple users use the same sub-carrier, which improves the fairness and throughput of the system. As a nonorthogonal method is used among multiple users, at the time of retrieving the user’s signal at the receiver’s end, joint processing is required. They proposed solutions to optimize the receiver and the radio resource allocation of uplink NOMA. Firstly, the authors proposed an iterative MUDD which utilizes the information produced by the channel decoder to improve the performance of the multiuser detector. After that, the author suggested a power allocation and novel subcarrier that enhances the users’ weighted sum rate for the NOMA scheme. Their proposed model showed that NOMA performed well as compared to OFDM in terms of fairness and efficiency.

In [ 53 ], the author’s reviewed a power-domain NOMA that uses superposition coding (SC) and successive interference cancellation (SIC) at the transmitter and the receiver end. Lots of analyses were held that described that NOMA effectively satisfies user data rate demands and network-level of 5G technologies. The paper presented a complete review of recent advances in the 5G NOMA system. It showed the comparative analysis regarding allocation procedures, user fairness, state-of-the-art efficiency evaluation, user pairing pattern, etc. The study also analyzes NOMA’s behavior when working with other wireless communication techniques, namely, beamforming, MIMO, cooperative connections, network, space-time coding, etc.

In [ 9 ], the authors proposed NOMA with MEC, which improves the QoS as well as reduces the latency of the 5G wireless network. This model increases the uplink NOMA by decreasing the user’s uplink energy consumption. They formulated an optimized NOMA framework that reduces the energy consumption of MEC by using computing and communication resource allocation, user clustering, and transmit powers.

In [ 10 ], the authors proposed a model which investigates outage probability under average channel state information CSI and data rate in full CSI to resolve the problem of optimal power allocation, which increase the NOMA downlink system among users. They developed simple low-complexity algorithms to provide the optimal solution. The obtained simulation results showed NOMA’s efficiency, achieving higher performance fairness compared to the TDMA configurations. It was observed from the results that NOMA, through the appropriate power amplifiers (PA), ensures the high-performance fairness requirement for the future 5G wireless communication networks.

In [ 56 ], researchers discussed that the NOMA technology and waveform modulation techniques had been used in the 5G mobile network. Therefore, this research gave a detailed survey of non-orthogonal waveform modulation techniques and NOMA schemes for next-generation mobile networks. By analyzing and comparing multiple access technologies, they considered the future evolution of these technologies for 5G mobile communication.

In [ 57 ], the authors surveyed non-orthogonal multiple access (NOMA) from the development phase to the recent developments. They have also compared NOMA techniques with traditional OMA techniques concerning information theory. The author discussed the NOMA schemes categorically as power and code domain, including the design principles, operating principles, and features. Comparison is based upon the system’s performance, spectral efficiency, and the receiver’s complexity. Also discussed are the future challenges, open issues, and their expectations of NOMA and how it will support the key requirements of 5G mobile communication systems with massive connectivity and low latency.

In [ 17 ], authors present the first review of an elementary NOMA model with two users, which clarify its central precepts. After that, a general design with multicarrier supports with a random number of users on each sub-carrier is analyzed. In performance evaluation with the existing approaches, resource sharing and multiple-input multiple-output NOMA are examined. Furthermore, they took the key elements of NOMA and its potential research demands. Finally, they reviewed the two-user SC-NOMA design and a multi-user MC-NOMA design to highlight NOMA’s basic approaches and conventions. They also present the research study about the performance examination, resource assignment, and MIMO in NOMA.

In this section, various works by different authors done on 5G NOMA technology is covered. Table 6 shows how other authors worked on the improvement of various parameters such as spectral efficiency, fairness, and computing capacity with 5G NOMA technology.

Summary of NOMA-based approaches in 5G technology.

4.3. 5G Millimeter Wave (mmWave)

Millimeter wave is an extremely high frequency band, which is very useful for 5G wireless networks. MmWave uses 30 GHz to 300 GHz spectrum band for transmission. The frequency band between 30 GHz to 300 GHz is known as mmWave because these waves have wavelengths between 1 to 10 mm. Till now radar systems and satellites are only using mmWave as these are very fast frequency bands which provide very high speed wireless communication. Many mobile network providers also started mmWave for transmitting data between base stations. Using two ways the speed of data transmission can be improved one is by increasing spectrum utilization and second is by increasing spectrum bandwidth. Out of these two approaches increasing bandwidth is quite easy and better. The frequency band below 5 GHz is very crowded as many technologies are using it so to boost up the data transmission rate 5G wireless network uses mmWave technology which instead of increasing spectrum utilization, increases the spectrum bandwidth [ 58 ]. To maximize the signal bandwidth in wireless communication the carrier frequency should also be increased by 5% because the signal bandwidth is directly proportional to carrier frequencies. The frequency band between 28 GHz to 60 GHz is very useful for 5G wireless communication as 28 GHz frequency band offers up to 1 GHz spectrum bandwidth and 60 GHz frequency band offers 2 GHz spectrum bandwidth. 4G LTE provides 2 GHz carrier frequency which offers only 100 MHz spectrum bandwidth. However, the use of mmWave increases the spectrum bandwidth 10 times, which leads to better transmission speeds [ 59 , 60 ].

Highlights of 5G mmWave are as follows:

Pictorial representation of millimeter wave.

• The 5G mmWave offer three advantages: (1) MmWave is very less used new Band, (2) MmWave signals carry more data than lower frequency wave, and (3) MmWave can be incorporated with MIMO antenna with the potential to offer a higher magnitude capacity compared to current communication systems.

In [ 11 ], the authors presented the survey of mmWave communications for 5G. The advantage of mmWave communications is adaptability, i.e., it supports the architectures and protocols up-gradation, which consists of integrated circuits, systems, etc. The authors over-viewed the present solutions and examined them concerning effectiveness, performance, and complexity. They also discussed the open research issues of mmWave communications in 5G concerning the software-defined network (SDN) architecture, network state information, efficient regulation techniques, and the heterogeneous system.

In [ 61 ], the authors present the recent work done by investigators in 5G; they discussed the design issues and demands of mmWave 5G antennas for cellular handsets. After that, they designed a small size and low-profile 60 GHz array of antenna units that contain 3D planer mesh-grid antenna elements. For the future prospect, a framework is designed in which antenna components are used to operate cellular handsets on mmWave 5G smartphones. In addition, they cross-checked the mesh-grid array of antennas with the polarized beam for upcoming hardware challenges.

In [ 12 ], the authors considered the suitability of the mmWave band for 5G cellular systems. They suggested a resource allocation system for concurrent D2D communications in mmWave 5G cellular systems, and it improves network efficiency and maintains network connectivity. This research article can serve as guidance for simulating D2D communications in mmWave 5G cellular systems. Massive mmWave BS may be set up to obtain a high delivery rate and aggregate efficiency. Therefore, many wireless users can hand off frequently between the mmWave base terminals, and it emerges the demand to search the neighbor having better network connectivity.

In [ 62 ], the authors provided a brief description of the cellular spectrum which ranges from 1 GHz to 3 GHz and is very crowed. In addition, they presented various noteworthy factors to set up mmWave communications in 5G, namely, channel characteristics regarding mmWave signal attenuation due to free space propagation, atmospheric gaseous, and rain. In addition, hybrid beamforming architecture in the mmWave technique is analyzed. They also suggested methods for the blockage effect in mmWave communications due to penetration damage. Finally, the authors have studied designing the mmWave transmission with small beams in nonorthogonal device-to-device communication.

This section covered various works done on 5G mmWave technology. The Table 7 shows how different author’s worked on the improvement of various parameters i.e., transmission rate, coverage, and cost, with 5G mmWave technology.

Summary of existing mmWave-based approaches in 5G technology.

4.4. 5G IoT Based Approaches

The 5G mobile network plays a big role in developing the Internet of Things (IoT). IoT will connect lots of things with the internet like appliances, sensors, devices, objects, and applications. These applications will collect lots of data from different devices and sensors. 5G will provide very high speed internet connectivity for data collection, transmission, control, and processing. 5G is a flexible network with unused spectrum availability and it offers very low cost deployment that is why it is the most efficient technology for IoT [ 63 ]. In many areas, 5G provides benefits to IoT, and below are some examples:

Smart homes: smart home appliances and products are in demand these days. The 5G network makes smart homes more real as it offers high speed connectivity and monitoring of smart appliances. Smart home appliances are easily accessed and configured from remote locations using the 5G network, as it offers very high speed low latency communication.

Smart cities: 5G wireless network also helps in developing smart cities applications such as automatic traffic management, weather update, local area broadcasting, energy saving, efficient power supply, smart lighting system, water resource management, crowd management, emergency control, etc.

Industrial IoT: 5G wireless technology will provide lots of features for future industries such as safety, process tracking, smart packing, shipping, energy efficiency, automation of equipment, predictive maintenance and logistics. 5G smart sensor technology also offers smarter, safer, cost effective, and energy-saving industrial operation for industrial IoT.

Smart Farming: 5G technology will play a crucial role for agriculture and smart farming. 5G sensors and GPS technology will help farmers to track live attacks on crops and manage them quickly. These smart sensors can also be used for irrigation control, pest control, insect control, and electricity control.

Autonomous Driving: 5G wireless network offers very low latency high speed communication which is very significant for autonomous driving. It means self-driving cars will come to real life soon with 5G wireless networks. Using 5G autonomous cars can easily communicate with smart traffic signs, objects and other vehicles running on the road. 5G’s low latency feature makes self-driving more real as every millisecond is important for autonomous vehicles, decision taking is performed in microseconds to avoid accidents [ 64 ].

Highlights of 5G IoT are as follows:

Pictorial representation of IoT with 5G.

• 5G with IoT is a new feature of next-generation mobile communication, which provides a high-speed internet connection between moderated devices. 5G IoT also offers smart homes, smart devices, sensors, smart transportation systems, smart industries, etc., for end-users to make them smarter.
• IoT deals with moderate devices which connect through the internet. The approach of the IoT has made the consideration of the research associated with the outcome of providing wearable, smart-phones, sensors, smart transportation systems, smart devices, washing machines, tablets, etc., and these diverse systems are associated to a common interface with the intelligence to connect.
• Significant IoT applications include private healthcare systems, traffic management, industrial management, and tactile internet, etc.

Plenty of approaches is devised to address the issues of IoT [ 14 , 65 , 66 ].

In [ 65 ], the paper focuses on 5G mobile systems due to the emerging trends and developing technologies, which results in the exponential traffic growth in IoT. The author surveyed the challenges and demands during deployment of the massive IoT applications with the main focus on mobile networking. The author reviewed the features of standard IoT infrastructure, along with the cellular-based, low-power wide-area technologies (LPWA) such as eMTC, extended coverage (EC)-GSM-IoT, as well as noncellular, low-power wide-area (LPWA) technologies such as SigFox, LoRa etc.

In [ 14 ], the authors presented how 5G technology copes with the various issues of IoT today. It provides a brief review of existing and forming 5G architectures. The survey indicates the role of 5G in the foundation of the IoT ecosystem. IoT and 5G can easily combine with improved wireless technologies to set up the same ecosystem that can fulfill the current requirement for IoT devices. 5G can alter nature and will help to expand the development of IoT devices. As the process of 5G unfolds, global associations will find essentials for setting up a cross-industry engagement in determining and enlarging the 5G system.

In [ 66 ], the author introduced an IoT authentication scheme in a 5G network, with more excellent reliability and dynamic. The scheme proposed a privacy-protected procedure for selecting slices; it provided an additional fog node for proper data transmission and service types of the subscribers, along with service-oriented authentication and key understanding to maintain the secrecy, precision of users, and confidentiality of service factors. Users anonymously identify the IoT servers and develop a vital channel for service accessibility and data cached on local fog nodes and remote IoT servers. The author performed a simulation to manifest the security and privacy preservation of the user over the network.

This section covered various works done on 5G IoT by multiple authors. Table 8 shows how different author’s worked on the improvement of numerous parameters, i.e., data rate, security requirement, and performance with 5G IoT.

Summary of IoT-based approaches in 5G technology.

4.5. Machine Learning Techniques for 5G

Various machine learning (ML) techniques were applied in 5G networks and mobile communication. It provides a solution to multiple complex problems, which requires a lot of hand-tuning. ML techniques can be broadly classified as supervised, unsupervised, and reinforcement learning. Let’s discuss each learning technique separately and where it impacts the 5G network.

Supervised Learning, where user works with labeled data; some 5G network problems can be further categorized as classification and regression problems. Some regression problems such as scheduling nodes in 5G and energy availability can be predicted using Linear Regression (LR) algorithm. To accurately predict the bandwidth and frequency allocation Statistical Logistic Regression (SLR) is applied. Some supervised classifiers are applied to predict the network demand and allocate network resources based on the connectivity performance; it signifies the topology setup and bit rates. Support Vector Machine (SVM) and NN-based approximation algorithms are used for channel learning based on observable channel state information. Deep Neural Network (DNN) is also employed to extract solutions for predicting beamforming vectors at the BS’s by taking mapping functions and uplink pilot signals into considerations.

In unsupervised Learning, where the user works with unlabeled data, various clustering techniques are applied to enhance network performance and connectivity without interruptions. K-means clustering reduces the data travel by storing data centers content into clusters. It optimizes the handover estimation based on mobility pattern and selection of relay nodes in the V2V network. Hierarchical clustering reduces network failure by detecting the intrusion in the mobile wireless network; unsupervised soft clustering helps in reducing latency by clustering fog nodes. The nonparametric Bayesian unsupervised learning technique reduces traffic in the network by actively serving the user’s requests and demands. Other unsupervised learning techniques such as Adversarial Auto Encoders (AAE) and Affinity Propagation Clustering techniques detect irregular behavior in the wireless spectrum and manage resources for ultradense small cells, respectively.

In case of an uncertain environment in the 5G wireless network, reinforcement learning (RL) techniques are employed to solve some problems. Actor-critic reinforcement learning is used for user scheduling and resource allocation in the network. Markov decision process (MDP) and Partially Observable MDP (POMDP) is used for Quality of Experience (QoE)-based handover decision-making for Hetnets. Controls packet call admission in HetNets and channel access process for secondary users in a Cognitive Radio Network (CRN). Deep RL is applied to decide the communication channel and mobility and speeds up the secondary user’s learning rate using an antijamming strategy. Deep RL is employed in various 5G network application parameters such as resource allocation and security [ 67 ]. Table 9 shows the state-of-the-art ML-based solution for 5G network.

The state-of-the-art ML-based solution for 5G network.

Highlights of machine learning techniques for 5G are as follows:

Pictorial representation of machine learning (ML) in 5G.

• In ML, a model will be defined which fulfills the desired requirements through which desired results are obtained. In the later stage, it examines accuracy from obtained results.
• ML plays a vital role in 5G network analysis for threat detection, network load prediction, final arrangement, and network formation. Searching for a better balance between power, length of antennas, area, and network thickness crossed with the spontaneous use of services in the universe of individual users and types of devices.

In [ 79 ], author’s firstly describes the demands for the traditional authentication procedures and benefits of intelligent authentication. The intelligent authentication method was established to improve security practice in 5G-and-beyond wireless communication systems. Thereafter, the machine learning paradigms for intelligent authentication were organized into parametric and non-parametric research methods, as well as supervised, unsupervised, and reinforcement learning approaches. As a outcome, machine learning techniques provide a new paradigm into authentication under diverse network conditions and unstable dynamics. In addition, prompt intelligence to the security management to obtain cost-effective, better reliable, model-free, continuous, and situation-aware authentication.

In [ 68 ], the authors proposed a machine learning-based model to predict the traffic load at a particular location. They used a mobile network traffic dataset to train a model that can calculate the total number of user requests at a time. To launch access and mobility management function (AMF) instances according to the requirement as there were no predictions of user request the performance automatically degrade as AMF does not handle these requests at a time. Earlier threshold-based techniques were used to predict the traffic load, but that approach took too much time; therefore, the authors proposed RNN algorithm-based ML to predict the traffic load, which gives efficient results.

In [ 15 ], authors discussed the issue of network slice admission, resource allocation among subscribers, and how to maximize the profit of infrastructure providers. The author proposed a network slice admission control algorithm based on SMDP (decision-making process) that guarantees the subscribers’ best acceptance policies and satisfiability (tenants). They also suggested novel N3AC, a neural network-based algorithm that optimizes performance under various configurations, significantly outperforms practical and straightforward approaches.

This section includes various works done on 5G ML by different authors. Table 10 shows the state-of-the-art work on the improvement of various parameters such as energy efficiency, Quality of Services (QoS), and latency with 5G ML.

The state-of-the-art ML-based approaches in 5G technology.

4.6. Optimization Techniques for 5G

Optimization techniques may be applied to capture NP-Complete or NP-Hard problems in 5G technology. This section briefly describes various research works suggested for 5G technology based on optimization techniques.

In [ 80 ], Massive MIMO technology is used in 5G mobile network to make it more flexible and scalable. The MIMO implementation in 5G needs a significant number of radio frequencies is required in the RF circuit that increases the cost and energy consumption of the 5G network. This paper provides a solution that increases the cost efficiency and energy efficiency with many radio frequency chains for a 5G wireless communication network. They give an optimized energy efficient technique for MIMO antenna and mmWave technologies based 5G mobile communication network. The proposed Energy Efficient Hybrid Precoding (EEHP) algorithm to increase the energy efficiency for the 5G wireless network. This algorithm minimizes the cost of an RF circuit with a large number of RF chains.

In [ 16 ], authors have discussed the growing demand for energy efficiency in the next-generation networks. In the last decade, they have figured out the things in wireless transmissions, which proved a change towards pursuing green communication for the next generation system. The importance of adopting the correct EE metric was also reviewed. Further, they worked through the different approaches that can be applied in the future for increasing the network’s energy and posed a summary of the work that was completed previously to enhance the energy productivity of the network using these capabilities. A system design for EE development using relay selection was also characterized, along with an observation of distinct algorithms applied for EE in relay-based ecosystems.

In [ 81 ], authors presented how AI-based approach is used to the setup of Self Organizing Network (SON) functionalities for radio access network (RAN) design and optimization. They used a machine learning approach to predict the results for 5G SON functionalities. Firstly, the input was taken from various sources; then, prediction and clustering-based machine learning models were applied to produce the results. Multiple AI-based devices were used to extract the knowledge analysis to execute SON functionalities smoothly. Based on results, they tested how self-optimization, self-testing, and self-designing are done for SON. The author also describes how the proposed mechanism classifies in different orders.

In [ 82 ], investigators examined the working of OFDM in various channel environments. They also figured out the changes in frame duration of the 5G TDD frame design. Subcarrier spacing is beneficial to obtain a small frame length with control overhead. They provided various techniques to reduce the growing guard period (GP) and cyclic prefix (CP) like complete utilization of multiple subcarrier spacing, management and data parts of frame at receiver end, various uses of timing advance (TA) or total control of flexible CP size.

This section includes various works that were done on 5G optimization by different authors. Table 11 shows how other authors worked on the improvement of multiple parameters such as energy efficiency, power optimization, and latency with 5G optimization.

Summary of Optimization Based Approaches in 5G Technology.

5. Description of Novel 5G Features over 4G

This section presents descriptions of various novel features of 5G, namely, the concept of small cell, beamforming, and MEC.

5.1. Small Cell

Small cells are low-powered cellular radio access nodes which work in the range of 10 meters to a few kilometers. Small cells play a very important role in implementation of the 5G wireless network. Small cells are low power base stations which cover small areas. Small cells are quite similar with all the previous cells used in various wireless networks. However, these cells have some advantages like they can work with low power and they are also capable of working with high data rates. Small cells help in rollout of 5G network with ultra high speed and low latency communication. Small cells in the 5G network use some new technologies like MIMO, beamforming, and mmWave for high speed data transmission. The design of small cells hardware is very simple so its implementation is quite easier and faster. There are three types of small cell tower available in the market. Femtocells, picocells, and microcells [ 83 ]. As shown in the Table 12 .

Types of Small cells.

MmWave is a very high band spectrum between 30 to 300 GHz. As it is a significantly less used spectrum, it provides very high-speed wireless communication. MmWave offers ultra-wide bandwidth for next-generation mobile networks. MmWave has lots of advantages, but it has some disadvantages, too, such as mmWave signals are very high-frequency signals, so they have more collision with obstacles in the air which cause the signals loses energy quickly. Buildings and trees also block MmWave signals, so these signals cover a shorter distance. To resolve these issues, multiple small cell stations are installed to cover the gap between end-user and base station [ 18 ]. Small cell covers a very shorter range, so the installation of a small cell depends on the population of a particular area. Generally, in a populated place, the distance between each small cell varies from 10 to 90 meters. In the survey [ 20 ], various authors implemented small cells with massive MIMO simultaneously. They also reviewed multiple technologies used in 5G like beamforming, small cell, massive MIMO, NOMA, device to device (D2D) communication. Various problems like interference management, spectral efficiency, resource management, energy efficiency, and backhauling are discussed. The author also gave a detailed presentation of all the issues occurring while implementing small cells with various 5G technologies. As shown in the Figure 7 , mmWave has a higher range, so it can be easily blocked by the obstacles as shown in Figure 7 a. This is one of the key concerns of millimeter-wave signal transmission. To solve this issue, the small cell can be placed at a short distance to transmit the signals easily, as shown in Figure 7 b.

Pictorial representation of communication with and without small cells.

5.2. Beamforming

Beamforming is a key technology of wireless networks which transmits the signals in a directional manner. 5G beamforming making a strong wireless connection toward a receiving end. In conventional systems when small cells are not using beamforming, moving signals to particular areas is quite difficult. Beamforming counter this issue using beamforming small cells are able to transmit the signals in particular direction towards a device like mobile phone, laptops, autonomous vehicle and IoT devices. Beamforming is improving the efficiency and saves the energy of the 5G network. Beamforming is broadly divided into three categories: Digital beamforming, analog beamforming and hybrid beamforming. Digital beamforming: multiuser MIMO is equal to digital beamforming which is mainly used in LTE Advanced Pro and in 5G NR. In digital beamforming the same frequency or time resources can be used to transmit the data to multiple users at the same time which improves the cell capacity of wireless networks. Analog Beamforming: In mmWave frequency range 5G NR analog beamforming is a very important approach which improves the coverage. In digital beamforming there are chances of high pathloss in mmWave as only one beam per set of antenna is formed. While the analog beamforming saves high pathloss in mmWave. Hybrid beamforming: hybrid beamforming is a combination of both analog beamforming and digital beamforming. In the implementation of MmWave in 5G network hybrid beamforming will be used [ 84 ].

Wireless signals in the 4G network are spreading in large areas, and nature is not Omnidirectional. Thus, energy depletes rapidly, and users who are accessing these signals also face interference problems. The beamforming technique is used in the 5G network to resolve this issue. In beamforming signals are directional. They move like a laser beam from the base station to the user, so signals seem to be traveling in an invisible cable. Beamforming helps achieve a faster data rate; as the signals are directional, it leads to less energy consumption and less interference. In [ 21 ], investigators evolve some techniques which reduce interference and increase system efficiency of the 5G mobile network. In this survey article, the authors covered various challenges faced while designing an optimized beamforming algorithm. Mainly focused on different design parameters such as performance evaluation and power consumption. In addition, they also described various issues related to beamforming like CSI, computation complexity, and antenna correlation. They also covered various research to cover how beamforming helps implement MIMO in next-generation mobile networks [ 85 ]. Figure 8 shows the pictorial representation of communication with and without using beamforming.

Pictorial Representation of communication with and without using beamforming.

5.3. Mobile Edge Computing

Mobile Edge Computing (MEC) [ 24 ]: MEC is an extended version of cloud computing that brings cloud resources closer to the end-user. When we talk about computing, the very first thing that comes to our mind is cloud computing. Cloud computing is a very famous technology that offers many services to end-user. Still, cloud computing has many drawbacks. The services available in the cloud are too far from end-users that create latency, and cloud user needs to download the complete application before use, which also increases the burden to the device [ 86 ]. MEC creates an edge between the end-user and cloud server, bringing cloud computing closer to the end-user. Now, all the services, namely, video conferencing, virtual software, etc., are offered by this edge that improves cloud computing performance. Another essential feature of MEC is that the application is split into two parts, which, first one is available at cloud server, and the second is at the user’s device. Therefore, the user need not download the complete application on his device that increases the performance of the end user’s device. Furthermore, MEC provides cloud services at very low latency and less bandwidth. In [ 23 , 87 ], the author’s investigation proved that successful deployment of MEC in 5G network increases the overall performance of 5G architecture. Graphical differentiation between cloud computing and mobile edge computing is presented in Figure 9 .

Pictorial representation of cloud computing vs. mobile edge computing.

6. 5G Security

Security is the key feature in the telecommunication network industry, which is necessary at various layers, to handle 5G network security in applications such as IoT, Digital forensics, IDS and many more [ 88 , 89 ]. The authors [ 90 ], discussed the background of 5G and its security concerns, challenges and future directions. The author also introduced the blockchain technology that can be incorporated with the IoT to overcome the challenges in IoT. The paper aims to create a security framework which can be incorporated with the LTE advanced network, and effective in terms of cost, deployment and QoS. In [ 91 ], author surveyed various form of attacks, the security challenges, security solutions with respect to the affected technology such as SDN, Network function virtualization (NFV), Mobile Clouds and MEC, and security standardizations of 5G, i.e., 3GPP, 5GPPP, Internet Engineering Task Force (IETF), Next Generation Mobile Networks (NGMN), European Telecommunications Standards Institute (ETSI). In [ 92 ], author elaborated various technological aspects, security issues and their existing solutions and also mentioned the new emerging technological paradigms for 5G security such as blockchain, quantum cryptography, AI, SDN, CPS, MEC, D2D. The author aims to create new security frameworks for 5G for further use of this technology in development of smart cities, transportation and healthcare. In [ 93 ], author analyzed the threats and dark threat, security aspects concerned with SDN and NFV, also their Commercial & Industrial Security Corporation (CISCO) 5G vision and new security innovations with respect to the new evolving architectures of 5G [ 94 ].

AuthenticationThe identification of the user in any network is made with the help of authentication. The different mobile network generations from 1G to 5G have used multiple techniques for user authentication. 5G utilizes the 5G Authentication and Key Agreement (AKA) authentication method, which shares a cryptographic key between user equipment (UE) and its home network and establishes a mutual authentication process between the both [ 95 ].

Access Control To restrict the accessibility in the network, 5G supports access control mechanisms to provide a secure and safe environment to the users and is controlled by network providers. 5G uses simple public key infrastructure (PKI) certificates for authenticating access in the 5G network. PKI put forward a secure and dynamic environment for the 5G network. The simple PKI technique provides flexibility to the 5G network; it can scale up and scale down as per the user traffic in the network [ 96 , 97 ].

Communication Security 5G deals to provide high data bandwidth, low latency, and better signal coverage. Therefore secure communication is the key concern in the 5G network. UE, mobile operators, core network, and access networks are the main focal point for the attackers in 5G communication. Some of the common attacks in communication at various segments are Botnet, message insertion, micro-cell, distributed denial of service (DDoS), and transport layer security (TLS)/secure sockets layer (SSL) attacks [ 98 , 99 ].

Encryption The confidentiality of the user and the network is done using encryption techniques. As 5G offers multiple services, end-to-end (E2E) encryption is the most suitable technique applied over various segments in the 5G network. Encryption forbids unauthorized access to the network and maintains the data privacy of the user. To encrypt the radio traffic at Packet Data Convergence Protocol (PDCP) layer, three 128-bits keys are applied at the user plane, nonaccess stratum (NAS), and access stratum (AS) [ 100 ].

7. Summary of 5G Technology Based on Above-Stated Challenges

In this section, various issues addressed by investigators in 5G technologies are presented in Table 13 . In addition, different parameters are considered, such as throughput, latency, energy efficiency, data rate, spectral efficiency, fairness & computing capacity, transmission rate, coverage, cost, security requirement, performance, QoS, power optimization, etc., indexed from R1 to R14.

Summary of 5G Technology above stated challenges (R1:Throughput, R2:Latency, R3:Energy Efficiency, R4:Data Rate, R5:Spectral efficiency, R6:Fairness & Computing Capacity, R7:Transmission Rate, R8:Coverage, R9:Cost, R10:Security requirement, R11:Performance, R12:Quality of Services (QoS), R13:Power Optimization).

8. Conclusions

This survey article illustrates the emergence of 5G, its evolution from 1G to 5G mobile network, applications, different research groups, their work, and the key features of 5G. It is not just a mobile broadband network, different from all the previous mobile network generations; it offers services like IoT, V2X, and Industry 4.0. This paper covers a detailed survey from multiple authors on different technologies in 5G, such as massive MIMO, Non-Orthogonal Multiple Access (NOMA), millimeter wave, small cell, MEC (Mobile Edge Computing), beamforming, optimization, and machine learning in 5G. After each section, a tabular comparison covers all the state-of-the-research held in these technologies. This survey also shows the importance of these newly added technologies and building a flexible, scalable, and reliable 5G network.

9. Future Findings

This article covers a detailed survey on the 5G mobile network and its features. These features make 5G more reliable, scalable, efficient at affordable rates. As discussed in the above sections, numerous technical challenges originate while implementing those features or providing services over a 5G mobile network. So, for future research directions, the research community can overcome these challenges while implementing these technologies (MIMO, NOMA, small cell, mmWave, beam-forming, MEC) over a 5G network. 5G communication will bring new improvements over the existing systems. Still, the current solutions cannot fulfill the autonomous system and future intelligence engineering requirements after a decade. There is no matter of discussion that 5G will provide better QoS and new features than 4G. But there is always room for improvement as the considerable growth of centralized data and autonomous industry 5G wireless networks will not be capable of fulfilling their demands in the future. So, we need to move on new wireless network technology that is named 6G. 6G wireless network will bring new heights in mobile generations, as it includes (i) massive human-to-machine communication, (ii) ubiquitous connectivity between the local device and cloud server, (iii) creation of data fusion technology for various mixed reality experiences and multiverps maps. (iv) Focus on sensing and actuation to control the network of the entire world. The 6G mobile network will offer new services with some other technologies; these services are 3D mapping, reality devices, smart homes, smart wearable, autonomous vehicles, artificial intelligence, and sense. It is expected that 6G will provide ultra-long-range communication with a very low latency of 1 ms. The per-user bit rate in a 6G wireless network will be approximately 1 Tbps, and it will also provide wireless communication, which is 1000 times faster than 5G networks.

Acknowledgments

Author contributions.

Conceptualization: R.D., I.Y., G.C., P.L. data gathering: R.D., G.C., P.L, I.Y. funding acquisition: I.Y. investigation: I.Y., G.C., G.P. methodology: R.D., I.Y., G.C., P.L., G.P., survey: I.Y., G.C., P.L, G.P., R.D. supervision: G.C., I.Y., G.P. validation: I.Y., G.P. visualization: R.D., I.Y., G.C., P.L. writing, original draft: R.D., I.Y., G.C., P.L., G.P. writing, review, and editing: I.Y., G.C., G.P. All authors have read and agreed to the published version of the manuscript.

This paper was supported by Soonchunhyang University.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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How AI tools help students—and their professors—in academic research

New systems can help surface relevant research papers and quickly understand what they have to say..

For students and professional scholars alike, starting a new research project typically means digging through academic literature to understand what others have already written.

That can take a considerable amount of time, with researchers tracking down and combing through journal articles to begin their research and contextualize their own findings. But a growing collection of AI-powered tools aims to make that process easier. These new tools can help researchers more quickly find relevant papers, pull out relevant information from them, or both.

“It can be a really helpful way to get started with research, especially for students who aren’t familiar with the research process,” says Breanne Kirsch, director of the library at Illinois College. “As long as they’re taught how to use it in an ethical way, and that they can then expand beyond what it does.”

A tool called  Elicit  can help researchers conduct what are called  systematic reviews , which involve going through copious amounts of published research to find an answer to a question, like how a particular drug affects a medical condition. “It’s all very, very manual,” says James Brady, head of engineering at Elicit. “It takes teams of people many months, and you know, costs hundreds of thousands or millions of dollars to do these things.”

Elicit can make that process much faster, and also help researchers by quickly finding and summarizing published papers related to a particular question. It can also generate tables describing a whole set of relevant papers, with columns for data points like algorithms and statistical techniques used, variables examined, and the number of participants in experiments.

The company recommends researchers still look at the original papers, and Brady emphasizes that the tool doesn’t replace the human judgment and analysis necessary to scientific research. “It’s not like you take the final step of Elicit and hit the publish button and then it ends up in  Nature  or something,” he says, but it can still greatly speed the process of sifting through and understanding prior work.

Understanding how AI can help academic research is part of a larger industry question of how and when the technology can  replace or supplement  traditional web search tools. And  since the 1990s , computer scientists have realized that the academic publishing landscape—where scholars cite each other’s papers and publish in journals with a particular reputation in a particular field—isn’t that different from the  internet ecosystem . That means techniques for finding relevant materials, minimizing AI errors and hallucinations, and presenting useful and verifiable results to the user may transfer from academia to the broader web.

Indeed, not everyone searching for scientific answers is a professional scientist. And the organizations behind these tools say they can be especially helpful for people looking to understand new fields of interest, whether they’re students, professionals doing interdisciplinary work, or interested members of the public.

Eric Olson, cofounder and CEO at AI research search engine  Consensus , says about 50% of the tool’s research is at academic institutions, where it’s often used by graduate students. “We typically do quite well with folks who need that easy, quick access to research but maybe aren’t a full-blown expert yet,” he says.

Consensus lets users type in natural language queries to get answers summarized from across published work. It surfaces summaries of particular papers, metadata like publication year and citation count, and an indication of how much scientific consensus there is about a particular question. Another popular audience for the tool is healthcare workers, including doctors, who use the tool to get insights more quickly than traditional scholarly search engines or Google can provide. Everyday users also use Consensus to research health topics, parenting practices, and policy issues in the news, Olson says.

Like other companies in the field, Consensus doesn’t simply rely on a single GPT-style large language model to generate answers to user questions. The company deploys a custom search engine to find papers addressing a query, and a variety of expert-trained language models to extract relevant information and—equally important—verify the paper is actually on topic, cutting the chance that an overzealous AI model will try to point out facts that aren’t actually there.

“I’m only gonna let this go to the model if we think that it actually has a relevant insight in it,” Olson says. “It’s a really great trick to reduce the risk of misinterpreting the paper.”

Academic publishing giant Elsevier has similarly developed a  tool called Scopus AI  to search through research collected in its  Scopus database , which includes article abstracts and metadata from tens of thousands of journals (including those published by rival publishers). Scopus AI can generate summary responses based on particular queries, suggest additional questions to help users expand their knowledge of the field, and highlight “foundational papers” and “topic expert” authors who have especial influence in an area of expertise.

“We’ve actually found this is quite a shared need across a number of different people who are at this precipice of trying to understand another domain,” says Maxim Khan, SVP of analytics products and data platform at Elsevier.

Khan says users have confirmed it helps them understand new fields faster and come across papers they might not otherwise have discovered. Thanks in part to licensing terms, the tool doesn’t include full text, meaning users can’t directly query about material in articles beyond the abstracts and citations.

Other software can help users dive deep into specific research. An  AI tool from JStor , still in limited beta, lets users see article summaries customized to their particular queries and can answer questions based on document contents, pointing to particular passages that contain the answer. That can help users figure out which papers are relevant enough for a close read, and the tool can also point to other topics or particular papers for a user to investigate based on particular passages.

The organization, with its focus on helping students with research, deliberately doesn’t generate aggregate answers to particular questions from multiple articles. Beth LaPensee, senior product manager at Ithaka, says the software can help students learning research skills and specialized vocabulary understand material they might otherwise struggle with. In a June blog post, Guthrie and LaPensee compared the process to learning the basic plot of a Shakespeare play before diving into the antiquated text, and say it can be especially helpful with humanities and social science papers that customarily don’t include abstracts.

The software has also proven helpful to professors. “One faculty member we were talking to said that they could do in one day what used to take them four or five days,” LaPensee says.

And the organization has found participants in the AI beta, which is slated to expand in the fall, spend “significantly more time on JStor” than other users.

Measuring results—and even knowing what to measure—is naturally an important part of testing new AI resources. Since 2015, a  project called Semantic Scholar  has focused on using AI to analyze scientific papers. It’s part of  Ai2 , the AI research institute founded by late Microsoft cofounder Paul Allen, and today it includes features to help users understand papers, like surfacing definitions of technical terms from within a paper or other research it cites, answering general questions about specific papers, and generating “tl; dr” summaries of papers based on the types of descriptions authors post on social media.

How to test whether those summaries were helpful wasn’t immediately obvious, recalls Dan Weld, chief scientist and general manager of Semantic Scholar. If users were benefiting from them, they might either click more articles from search results—if the summaries indicated they were interesting—or fewer, if the summaries helped them weed out extraneous results. But when the summaries were later added to email alerts, the results seemed positive—users clicked fewer emailed articles overall, but were more likely to save articles they clicked, suggesting the summaries steered them to interesting work.

Evaluating a feature Semantic Scholar is currently testing to answer questions from across multiple papers is even more challenging, according to Weld, who says, “It’s really quite difficult to compare different systems. There are some other systems out there that do question answering—we think ours is better than theirs, but we can’t prove it yet.”

And since different AI research tools have access to different sets of papers as well as different features, researchers may still find they need to use multiple AI platforms—often along with traditional database tools—to find everything they need. It’s important to note, Illinois College’s Kirsch says, that reading AI summaries can’t substitute for working through actual papers and verifying that they say what the tools claim, tempting though it can be.

“While the generative AI tools may help as a starting point, just like Wikipedia would, you still want to go to some of those actual sources,” she says. “You can’t just rely solely on the GenAI tools. You also need to look at the sources themselves and make sure it really does make sense for what you’re trying to do and what you’re trying to research.”

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Impact of AI on Retirement Professionals and Retirees - Essay Collection

August 2024

The Society of Actuaries Aging and Retirement Strategic Research Program Steering Committee issued a call for essays to explore the impact of artificial intelligence (AI) and large language models (LLM) on retirement professionals and retirees. The objective was to gather a variety of perspectives and experiences with AI and LLM in different retirement settings—both now and in the future. It is the goal of this collection to spur thoughts for future research and set the stage for upcoming efforts.

The seven essays that form this collection are included below and are also compiled here: Impact of AI on Retirement Professionals and Retirees - Essay Collection

Prize winners

Three essays were chosen for creativity, originality and the extent to which an idea might help promote further thought in this area, are noted here:

The Retirement Reckoning – When Family Ties Clash with Financial Realities Stefano Orfanos, FSA, CERA

Can Artificial Intelligence Help Me with Retirement Planning: An Individual Perspective Anna M. Rappaport, FSA, MAAA

A Retiree’s Guide to Artificial Intelligence risks and Mitigating Those Risks Gregory Whittaker, FSA, FASSA

Remaining essays

Artificial Intelligence and Retirement Planning John Cutler, J.D.

Artificial Intelligence as a Partner for Retirement Professionals: What Are the Issues? Anna M. Rappaport, FSA, MAAA

The Impact of Artificial Intelligence on Financial Decisions for Retirees Mark Dennis, DBA, CFP®

Pick a Payout Using AI John Blocher, FSA, MAAA

Acknowledgments

The SOA Research Institute Aging and Retirement Strategic Research Program thanks the Project Oversight Group (POG) for their careful review and judging of the submitted essays. Any views and ideas expressed in the essays are the authors’ alone and may not reflect the POG’s views and ideas nor those of their employers, the authors’ employers, the Society of Actuaries, the Society of Actuaries Research Institute, nor Society of Actuaries members.

Gavin Benjamin, FSA, FCIA Bonnie Birns, FSA, MAAA Ruth Schau, FSA, MAAA, FCA, EA Andrea Sellars, FSA, MAAA Matthew Smith, FSA, MAAA Cavan Stackpool, FSA, CERA

Questions or comments?

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If you have comments or questions, please send an email to [email protected]