10–50 m
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.
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.
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.
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 ].
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).
Approach | R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | R9 | R10 | R11 | R12 | R13 | R14 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Panzner et al. [ ] | Good | Low | Good | - | Avg | - | - | - | - | - | - | - | - | - |
Qiao et al. [ ] | - | - | - | - | - | - | - | Avg | Good | Avg | - | - | - | - |
He et al. [ ] | Avg | Low | Avg | - | - | - | - | - | - | - | - | - | - | - |
Abrol and jha [ ] | - | - | Good | - | - | - | - | - | - | - | - | - | - | Good |
Al-Imari et al. [ ] | - | - | - | - | Good | Good | Avg | - | - | - | - | - | - | - |
Papadopoulos et al. [ ] | Good | Low | Avg | - | Avg | - | - | - | - | - | - | - | - | - |
Kiani and Nsari [ ] | - | - | - | - | Avg | Good | Good | - | - | - | - | - | - | - |
Beck [ ] | - | Low | - | - | - | - | - | Avg | - | - | - | Good | - | Avg |
Ni et al. [ ] | - | - | - | Good | - | - | - | - | - | - | Avg | Avg | - | - |
Elijah [ ] | Avg | Low | Avg | - | - | - | - | - | - | - | - | - | - | - |
Alawe et al. [ ] | - | Low | Good | - | - | - | - | - | - | - | - | - | Avg | - |
Zhou et al. [ ] | Avg | - | Good | - | Avg | - | - | - | - | - | - | - | - | - |
Islam et al. [ ] | - | - | - | - | Good | Avg | Avg | - | - | - | - | - | - | - |
Bega et al. [ ] | - | Avg | - | - | - | - | - | - | - | - | - | - | Good | - |
Akpakwu et al. [ ] | - | - | - | Good | - | - | - | - | - | - | Avg | Good | - | - |
Wei et al. [ ] | - | - | - | - | - | - | - | Good | Avg | Low | - | - | - | - |
Khurpade et al. [ ] | - | - | - | Avg | - | - | - | - | - | - | - | Avg | - | - |
Timotheou and Krikidis [ ] | - | - | - | - | Good | Good | Avg | - | - | - | - | - | - | - |
Wang [ ] | Avg | Low | Avg | Avg | - | - | - | - | - | - | - | - | - | - |
Akhil Gupta & R. K. Jha [ ] | - | - | Good | Avg | Good | - | - | - | - | - | - | Good | Good | - |
Pérez-Romero et al. [ ] | - | - | Avg | - | - | - | - | - | - | - | - | - | - | Avg |
Pi [ ] | - | - | - | - | - | - | - | Good | Good | Avg | - | - | - | - |
Zi et al. [ ] | - | Avg | Good | - | - | - | - | - | - | - | - | - | - | - |
Chin [ ] | - | - | Good | Avg | - | - | - | - | - | Avg | - | Good | - | - |
Mamta Agiwal [ ] | - | Avg | - | Good | - | - | - | - | - | - | Good | Avg | - | - |
Ramesh et al. [ ] | Good | Avg | Good | - | Good | - | - | - | - | - | - | - | - | - |
Niu [ ] | - | - | - | - | - | - | - | Good | Avg | Avg | - | - | - | |
Fang et al. [ ] | - | Avg | Good | - | - | - | - | - | - | - | - | - | Good | - |
Hoydis [ ] | - | - | Good | - | Good | - | - | - | - | Avg | - | Good | - | - |
Wei et al. [ ] | - | - | - | - | Good | Avg | Good | - | - | - | - | - | - | - |
Hong et al. [ ] | - | - | - | - | - | - | - | - | Avg | Avg | Low | - | - | - |
Rashid [ ] | - | - | - | Good | - | - | - | Good | - | - | - | Avg | - | Good |
Prasad et al. [ ] | Good | - | Good | - | Avg | - | - | - | - | - | - | - | - | - |
Lähetkangas et al. [ ] | - | Low | Av | - | - | - | - | - | - | - | - | - | - | - |
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.
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.
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.
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.
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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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
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
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
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
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IMAGES
COMMENTS
Understanding technology and how we can make better decisions about designing, deploying, and refining it requires capturing that nuance and complexity through in-depth analysis of the impacts of different technological advancements and the ways they have played out in all their complicated and controversial messiness across the world.
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The findings suggest that ICT integration in schools impacts more than just students' performance; it affects several other school-related aspects and stakeholders, too. Furthermore, various factors affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the digital transformation process.
From the environmental impact of using less paper for handouts and books to the time savings and convenience of research, digital learning is a wonderful way to cut costs, better utilise resources, promote sustainability and expand both reach and impact for students and teachers. [ 16, 17 ]. Technology is pervasive and intertwined in many aspects of modern life and society. The digital ...
ABSTRACT While education technology has been widely used in classrooms, and considerable investments have been made to support its use in the UK, the evidence base for many such rapidly changing technologies is weak, and their efficacy is unclear. The aim of this paper is to systematically review and synthesise empirical research on the use of technology in formative assessment, to identify ...
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In a synopsis of 10 articles we present ample evidence that the use of digital technology may influence human brains and behavior in both negative and positive ways. For instance, brain imaging techniques show concrete morphological alterations in early childhood and during adolescence that are associated with intensive digital media use.
Highlights • We evaluate impact of a large-scale intervention using technology-aided teaching in 1823 rural government schools in India. • We use a resource light design that requires one computer and projector per school and minimal teacher retraining. • We observe positive impact on student learning outcomes in the subjects for which the intervention was conducted. • Being a resource ...
Approached this way, the systematic literature review displays major research avenues of digital transformation that consider technology as the main driver of these changes. This paper qualitatively classifies the literature on digital business transformation into three different clusters based on technological, business, and societal impacts.
Scrutinizing the effects of digital technology on mental health Does time spent using digital technology and social media have an adverse effect on mental health, especially that of adolescents?
The importance of technology in schools cannot be ignored. In fact, with the onset of computers in education, it has become easier for teachers to impart knowledge and for students to acquire it ...
The analysis of the articles showed positive and negative factors associated with the use of technologies by children. The main losses caused by technology use in childhood are excessive time connected to the internet, worsening of mental health, and changes in the circadian rhythm.
From elementary schooling to doctoral-level education, technology has become an integral part of the learning process in and out of the classroom. With the implementation of the Common Core Learning Standards, the skills required for research are more valuable than ever, for they are required to succeed in a college setting, as well as in the ...
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. ... It reviews Blockchain technology's role and the impact ...
Technological Innovation New research on technological innovation from Harvard Business School faculty on issues including using data mining to improve productivity, why business IT innovation is so difficult, and the business implications of the technology revolution.
The present review study draws upon a similar line of inquiry, investigating the state of research on the impact of using advanced technologies in marketing between 1999 and 2019. The findings of this review study will give a clear answer on the main research question: What is the impact of technological innovation on marketing sector?
This research article delves into the multifaceted impact of technology, exploring its influence on communication, economy, education, healthcare, and social dynamics.
Danovitch (2019) believed the exposure of technology could impact the cognitive. development of children in regard to memory, concentration, seeking information, and thinking. The team's recent research provided insight into how technology could affect cognitive. development in early childhood.
Abstract : This research deals with the impacts of misusing modern technologies on members of the society and their negative influences on economic, religious and social aspect, as well as their impact on people's behavior and the responsibility of the society in monitoring the children who are in need of attention by everyone through the guidance of appropriate educational ways. Moreover ...
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 ...
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.
The impact of entrepreneurship and business education on entrepreneurial abilities was 11%. Digital education research (7%) investigates digital technologies' classroom applications, focusing on online learning and digital literacy.
This collection of essays explores the implications of AI and large language models on retirement planning, providing insights and discussions on the topic.