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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.

The Impact of Technology on Business and Society

Global Journal of Business Research, v. 12 (1) p. 23-39

17 Pages Posted: 10 Mar 2019

Kathleen M. Wilburn

St. Edward’s University

H. Ralph Wilburn

Date Written: 2018

Technology, specifically the interrelationships of Artificial intelligence (AI), big data, and the Internet of things (IoT), is accelerating its ability to help businesses do more with less and provide better results. Businesses can use technology to decrease time from product idea to product creation and product creation to customer delivery, while using fewer workers. Costs can be cut as automation and robots replace humans who need wages and benefits. Although this will create more products and services at lower prices, it may also decrease the number of consumers for those products and services. There has been significant research in those jobs and activities that can be automated now and in the near future. With jobs disappearing, a new economy is growing that turns employees into contract workers who work from gig to gig in solitude. While this new structure of work may allow some people the work/life balance to pursue their creative goals, for others it may mean a life with no stability or future. The result may be a two-tiered society where the rich can afford expensive products and services, and the poor require governmental assistance because although products can be produced more cheaply, they cannot afford them and so they are not produced.

Keywords: Technology Disruption, Business and Technology, Sharing/Gig Economy, Peer-To-Peer, Structure

JEL Classification: M0

Suggested Citation: Suggested Citation

Kathleen M. Wilburn (Contact Author)

St. edward’s university ( email ), do you have a job opening that you would like to promote on ssrn, paper statistics, related ejournals, io: productivity, innovation & technology ejournal.

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• Department for Science, Innovation & Technology
• Department for Education

Research on public attitudes towards the use of AI in education

Published 28 August 2024

© Crown copyright 2024

This publication is licensed under the terms of the Open Government Licence v3.0 except where otherwise stated. To view this licence, visit nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] .

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This publication is available at https://www.gov.uk/government/publications/research-on-parent-and-pupil-attitudes-towards-the-use-of-ai-in-education/research-on-public-attitudes-towards-the-use-of-ai-in-education

1. Executive Summary 

1.1 background .

The Responsible Technology Adoption Unit (RTA) within the Department for Science, Innovation and Technology (DSIT) commissioned this research in partnership with the Department for Education (DfE) to understand how parents and pupils feel about the use of AI tools in education. 

As AI tools such as large language models (LLMs) become more advanced, there are opportunities for such tools to support both teachers and pupils by creating tailored content and support, as well as streamlining processes. However, there are many questions that need to be answered before AI is implemented widely. 

1.2 Objectives 

The project sought to answer the following research questions: 

Under what circumstances, if any, are parents and pupils comfortable with AI tools being used in education? 

Under what circumstances, if any, are parents and pupils comfortable with pupils’ work being used to optimise the performance of AI tools? 

Through deliberative dialogue with parents and pupils, Thinks Insight and Strategy (Thinks) explored their concerns, hopes, and expectations, as well as the conditions for use of AI in this context. 

1.3 Methodology 

Thinks engaged a total of 108 parents and pupils across three locations in England in a mix of face-to-face and online activities. Each participant took part in four to six hours of engagement, following the below structure: 

Inform: Participants were provided with information about the purpose of the research, as well as key principles such as machine learning, data protection, intellectual property, and current and potential AI applications in education. 

Debate: Participants explored the potential social, ethical, legal, financial, and cultural issues associated with use of AI in education, and were provided with a range of views from experts and officials. 

Decide: At the end of each session, each group of participants articulated their preferred conditions for use and explored areas of consensus and difference. 

1.4 Summary of key findings 

1. Parents and pupils frequently share personal information online, often without considering the implications. The benefits and convenience of using online services, especially those that provide a tailored experience, tend to outweigh any privacy concerns. 

2. While awareness of AI among both parents and pupils was high, understanding did not run deep. AI is often associated with robots or machines, and fictional dystopian futures. Only some – those with more knowledge of or exposure to AI – thought of specific applications such as LLM-powered tools. 

3. As a result, views on the use of AI in education were initially sceptical – but there was openness to learning more. Initial concerns about AI in education were often based on a lack of understanding or imagination of how it could be used.  

4. Parents and pupils agreed that there are clear opportunities for teachers to use AI to support them in their jobs. They were largely comfortable with AI being used by teachers, though more hesitant about pupils interacting with it directly. 

5. By the end of the sessions, participants understood that pupil work and data is needed to optimise AI tools. They were more comfortable with this when data is anonymised or pseudonymised, and when there are clear rules for data sharing both with private companies and across government. 

6. The main concerns regarding AI use centred on overreliance – both by teachers and pupils. Participants were worried about the loss of key social and technical skills and reduced human contact-time leading to unintended adverse outcomes. 

7. The research showed that opinions on AI tools are not yet fixed. Parents’ and pupils’ views of and trust in AI tools fluctuated throughout the sessions, as they reacted to new information and diverging opinions. This suggests that it will be important to build trust and continue engagement with different audiences as the technology becomes more established. 

Participants agreed on some key conditions for the use of AI in education and the use of pupil work and data to optimise AI tools: 

Human oversight: Human involvement in AI use to ensure teacher roles are not displaced, to correct for error and unfair bias, and to provide safeguarding. 

Parent and pupil permissions: Providing parents and pupils with the necessary information to make informed decisions about the use of their data. 

Standardisation and regulation: Ensuring that tools introduced at schools are of a uniform standard to avoid exacerbation of inequalities, with strict oversight of tech companies providing the tools. 

Age and subject restrictions: Using AI tools only where appropriate and where they add value. Strict age restrictions on direct interaction with AI. 

Profit sharing: Ensuring that tech companies share some of their profits so that these can be reinvested into the education system and benefit schools and pupils – while recognising that private companies will need to be incentivised to develop better tools. 

While this report describes the views of the parents and pupils who participated in the research, the suggestions contained within would require further research, discussion and consultation (and use of other types of evidence) prior to translation into policy and practice. 

2. Background and methodology  

2.1 project background .

The use of AI in education has the potential to support pupils’ learning and help reduce teacher workload. But as with any new or emerging technology, there are a range of issues which need to be considered before this is implemented widely. 

The Department for Education (DfE) and the Responsible Technology Adoption Unit (RTA) within the Department for Science Innovation and Technology (DSIT) wanted to understand how parents and pupils feel about AI tools being used in education, as well as what they think about pupils’ work (e.g. schoolwork, homework, exam scripts) being used to improve AI tools.  

This research aimed to create a space for pupils and parents to learn about and discuss the issues, consider their preferences for the use of AI in education, and inform DfE’s approach to implementing AI within the education system. 

2.2 Project objectives 

The overall objectives of this project were to understand: 

In which circumstances, if any, are parents and pupils comfortable with AI tools being used in education?  

a. Which kinds of use cases are acceptable? 

b. How much human oversight do parents and pupils want to see? 

c. What concerns need to be addressed? 

d. What wider factors affect acceptability?  

In which circumstances, if any, are parents and pupils comfortable with pupils’ work being used to optimise the performance of AI tools?  

a. Should parental agreement be required? If so, would parents give permission, and under which conditions?  

b. Who should control how the work produced by pupils is used and accessed?  

c. Who, if anyone, should profit from AI which is optimised with pupils’ work? 

2.3 Methodology and sample 

Thinks Insight & Strategy (Thinks) recruited six cohorts of parents across three locations in England. Three cohorts took part in an in-person workshop, while the other three took part in online workshops: 

Parents of children with special educational needs and/or disabilities (SEND) 

Parents of children of pre-school age  

Parents of primary school pupils 

Parents of pre-GCSE pupils  

Parents of GCSE pupils 

Parents of post-GCSE pupils (aged 17-18) 

We also recruited three cohorts of pupils across the three locations for face-to-face workshops, all attending state-funded schools: 

Pre-GCSE pupils 

GCSE pupils 

Post-GCSE pupils (aged 17-18) 

Table 1 below shows the breakdown of parent and pupil cohorts across the three fieldwork locations, by mode (in-person or online). A demographic sample breakdown can be found in the Appendix. 

Table 1: Breakdown of participant cohort by fieldwork location 

In-person fieldwork 

Online fieldwork 

Methodology 

In-person workshops  

We engaged a total of 36 parents/carers (referred to as “parents” throughout) and their children aged 11-18 (36 in total) in six-hour long workshops. Workshops took place in three locations across England on 24 February, 25 February and 2 March 2024. In these workshops, we used the following structure: 

Inform : First, we established the purpose of the dialogue and the reason for involving parents and pupils, providing contextual information about data, foundation models and potential applications. This included showing videos from those involved in the development of AI educational tools and a participant-led demo of some educational AI products.   

Debate : After a short break, we explored the potential social, ethical, legal, financial, and cultural issues associated with use of AI in education. This included watching videos from government ministers, officials and specialists in education who explained some of the potential benefits and risks of AI in education.   

Decide : After lunch we brought together participants in their groups to compare views and explore areas of consensus, conditions for use and preferences. This involved the groups discussing different AI use case suggestions and constructing their ideal future scenario. 

Online workshops  

We engaged a further 36 parents in two online workshops, on 21 February and 28 February 2024, each lasting two hours. We followed the same deliberative research process structure divided over the two sessions. 

Inform : In the first workshop, we focused primarily on informing the participants and providing contextual information. We showed videos from those involved in the development of AI educational tools and used voting tools to interact with participants. This workshop ended by asking participants to reflect on any concerns or needs for reassurance they might have. 

Debate and Decide : In the second workshop, participants were shown videos from government ministers, officials and specialists in education who explained the benefits and risks of AI in education. Following discussion on these topics, participants ranked potential uses of data and pupil work according to levels of comfort, before offering their thoughts and recommendations. 

3. Baseline views on AI and its uses 

3.1 summary.

While awareness of AI is relatively high, understanding does not run deep. Most participants had heard of and used AI-powered tools, although not necessarily on purpose. 

With increasing use of AI, many accept it as part of modern life, but remain uneasy about the perceived invasiveness of the technology.  

However, this generally did not stop participants from using and sharing their data with services that offer an improved experience based on machine learning, such as tailored recommendations or GPS. Expressed concerns about privacy were therefore at odds with actual behaviour. 

Most parents had not previously considered the application of AI tools in education beyond the risk of pupils using LLMs to plagiarise. However, for many children, the use of technology is already a big part of their everyday lives at school, meaning they viewed this as a natural extension, or a continuation, of what is already happening. 

3.2 Awareness and understanding of AI and its use 

At the start of each workshop, participants were asked to list their first associations with the terms “AI” or “Artificial Intelligence”. Although awareness of AI as a “hot topic” was high, understanding of the technology did not run deep. Both pupils and parents were likely to associate AI with robots or machines, but also with social media, streaming and shopping platforms, apps, and websites. In particular, they thought of chatbots, targeted advertising, and algorithms recommending products. Despite some awareness, only a handful of participants across the parent and pupil samples had purposely interacted with LLM-powered tools or proactively used them regularly. When prompted with some other less obvious examples (such as GPS, AutoCorrect and predictive text) however, most discovered that they had much more exposure to AI than they had originally thought. 

Parent of primary school pupil, Newcastle: 

[An online video streaming platform] has tracked who I view and what kinds of people I have viewed and followed and brings up related videos. 

3.3 Perceptions of AI 

Most participants accepted the use of AI in various settings, products, or services, as an inevitability of modern life. However, many expressed unease about the technology, due to its perceived invasiveness both in terms of its increasing ubiquity and its reliance on personal data-sharing. Generally, participants found it easier to think of the risks of AI than benefits, even where they acknowledged that it could improve efficiency or convenience. These concerns often centred on humans being replaced by machines resulting in job displacement, but also machines not being an adequate replacement for humans because they are perceived to lack more nuanced understanding – for example, in customer service settings. 

Younger children were generally the least worried about AI, often because they had not given much thought to it, were less concerned about data security, and more used to technology playing a role in their lives. Older children, and particularly those aged 17-18, were more likely to have used AI as well as to have a general awareness of its use. Some had used LLMs and found them useful, though only to an extent, as they had quickly found limitations of the technology. Even among children and young people, some aspects of AI were seen as “creepy” or going too far, particularly AI features used by social media platforms that mimicked human conversation too closely or felt overly friendly in tone to users. 

Post-GCSE Pupil, Birmingham: 

I use [LLM-powered tool] to help with my essays; it’s quicker. 

Post-GCSE Pupil, Birmingham : 

Sometimes it asks really random questions and you think do you need to know that? 

The use of personal data in relation to AI was also a concern for both parents and children. In particular, concerns involved the sale of data to third parties by companies developing AI tools and misuse of data by other humans (for example, in the creation of deepfakes). Despite these concerns, parents and pupils reported frequently sharing their personal data online. They noted that personal information is shared to create accounts, verify their identity, and receive relevant and tailored information or experiences. They also acknowledged that the benefit and convenience of sharing this data largely outweighed their concerns. Participants noted that they had little understanding of, or gave little consideration to, what happens to their data once it has been shared, beyond a general assumption that companies store and sell it to third parties to make a profit. In part, this may be because the benefits of sharing personal data were felt to be more immediate and tangible than the risks (such as a hypothetical data breach), which can feel more abstract and far-removed as a possibility. 

I’m not actually sure what [the app] does with my data, other than checking that I’m old enough to view the videos and the content is suitable. 

Post-GCSE pupil, Newcastle: 

[What does [a video streaming service] do with your information?] Stores it, recommends you shows, brings new things in, sells your information. 

Compared with their children, parents demonstrated higher awareness of the risks of data sharing, both in relation to their own data, and that of their children. They were concerned about the information that was being put “out there”, but also felt resigned to it. A handful of parents with higher levels of knowledge of technology were excited about the opportunities offered by AI, though still wary. 

Parent of a child with SEND, Bristol: 

Helping overcome barriers is good, but thinking about, for example, language, research, literature, it might take away from that. Create an overreliance on tech and developing social skills. What would data mining implications be? Would teachers lose jobs? 

3.4 Initial views on AI in education 

Stimulus provided: .

Before exploring views of AI in an education context, participants were shown a video explaining what AI is and why it is important to understand and engage with it. 

In the context of limited understanding of AI, initial views of the use of AI in education were mostly sceptical. Most had not considered the use of AI in education before and found it difficult to imagine how it might be used within schools. Initially, participants were more likely to imagine pupils interacting directly with AI, rather than teachers using it to support them in their roles. Many participants immediately thought of the replacement of teachers with machines, in line with their initial concerns about human job displacement. This was rejected by participants, as they felt it was important for pupils to interact with human teachers. In addition, underlying assumptions about AI (and technology in general) making people lazy, particularly held by parents, also coloured spontaneous perceptions. 

Parent of pre-GCSE pupil, Newcastle: 

As long as the humans are not replaced, if it streamlines and allows for more personal time [with teachers], that’s got to be a benefit. 

As a result of this relatively limited prior knowledge and understanding of AI, it was initially unclear to both parents and children what the potential benefits of AI might be for teaching quality or pupil attainment. There was also uncertainty about what the use(s) of AI in various educational settings could be in practice. However, with scepticism largely grounded in a lack of experience or understanding, participants expressed an openness to hearing more. This was particularly true of pupils, many of whom felt more comfortable sharing their data and using technology relative to parents. Some pupils had already used AI in an educational context or knew that their teachers did. Even those who had not actively used AI in a school setting were familiar with the idea of existing software supporting pupils and teachers. As a result, most pupils felt that AI use in schools was already becoming the norm and further use would be a natural progression of technology application, even if they had not fully considered the implications. 

4. Using AI in education 

4.1 summary.

Both parents and pupils thought the main advantage of AI use in education was its potential to support teachers and, by extension, improve pupils’ learning experience. 

Parents, and to a lesser extent pupils, were much less certain about pupils interacting directly with AI, especially unsupervised – even though they could see benefits in AI providing highly tailored support. 

Both parents’ and pupils’ main concerns revolved around the quality of teaching, overreliance on AI resulting in loss of key social and technical skills, as well as the suitability of AI to address certain subjects and pupil needs. 

Across the board, participants were more comfortable with use cases where AI supports teachers (for example, preparing a lesson) or is used for “lower stakes” tasks (for example, marking a class test, rather than an exam).  

There was a sense that AI use should always be optional, both for teachers and pupils, and that parents should have a say in whether and how AI is used – though there was little acknowledgement of the practical issues that could arise in introducing AI on an opt-in/out basis. 

Before exploring more detailed uses of AI in education, participants were provided with stimulus in the form of demonstrations of AI tools currently available to support with learning or in development, and videos explaining: 

Machine learning and its potential uses in education 

Current and potential benefits of AI for teachers and pupils  

Current and potential risks of AI use, including data protection, privacy, intellectual property, and bias 

The strategic benefits and risks of AI use in education from a policy perspective, and how they can be managed 

4.2 Participants’ views regarding opportunities for AI use in education 

Supporting teachers .

The biggest perceived opportunity for AI use in education was to support teachers in generating classroom materials and managing feedback in more efficient ways. The perception was that this could reduce administration tasks and increase the attractiveness of teaching as a profession. 

Across the workshops, parents and pupils felt most comfortable with teachers using AI as a tool to support lesson delivery (for example, by helping to plan lessons). They were less comfortable with the idea of pupils engaging directly with AI tools, as they wanted to ensure some level of human oversight and pupil-teacher interaction. 

Pre-GCSE Pupil, Bristol: 

It can help teachers making slides, like information slides, and answer questions about stuff. 

Parent of post-GCSE Pupil, Birmingham: 

It’s less stressful for teachers to sort the homework, lesson plans… and [gives them] more time to be present and support the kids. 

Using AI as a support to teachers was felt to enable better learning experiences. 

There was a higher level of comfort with AI when it was seen as enabling teachers to redirect their time and energy into delivering high quality education. For parents in particular, the terms “helping” and “assisting” the teacher reassured them AI would play a supporting role, rather than taking over the teacher’s role, and alleviated parents’ concerns about the risks of potential overreliance on AI (see section 4.2.1 Lower quality of learning). Interestingly, some parents and pupils assumed that the introduction of AI tools would lead to more contact time between teachers and pupils – though they were not clear on whether they would expect pupils to spend more time in school. 

I think it’s great. I’m impressed by it. I think if teachers have got that kind of tool to help with the administrative side, they have more time in the classroom for actual teaching rather than having to go home and mark and make lesson plans. 

The potential for AI tools to support teachers to provide detailed, timely, high-quality feedback was considered to be a key benefit. Parents felt that better quality feedback would help them understand their child’s progress, and identify areas where they need more support. As a result, parents were supportive of the benefits of using AI tools to help teachers to provide more frequent and personalised feedback. 

Parent of pre-school pupil, Bristol: 

It would be more targeted to my child; it would collect so much information on my child that it would support and help their learning. To show [what their] focus area [is], what subjects, might show me what might be good extra learning. 

Participants’ views on the use of AI to enhance learning experiences 

Both parents and pupils recognised that some AI tools could make learning more fun and engaging for pupils by generating visually engaging and creative resources that teachers might not currently have the time to create. During the in-person workshops, participants were encouraged to explore an LLM-powered tool using tablets and some suggested prompts. Many were impressed by the ways in which the tool could help quickly bring topics to life in the classroom, such as when assuming the character of a historical or literary figure and answering questions asked by pupils from the perspective of that character.  

Some pupils saw an opportunity for LLM-powered tools to inspire them to be more creative in their work, either by expanding on pupils’ own ideas, or by providing a starting point on which pupils could then layer their own thinking and creativity, such as when writing an essay or story.  

Using AI in these ways was felt to be exciting and engaging, bringing topics to life and helping pupils develop their own ideas. Participants, particularly pupils, expressed a more positive sentiment about AI tools creating a more interactive learning environment where they could input ideas and get interesting new feedback generated by the AI. This use of AI in education was seen by some as more acceptable than auto-correcting pupils’ work, or providing the answers to copy and paste in response to an assignment question being asked of AI. Some pupils felt more positive about AI being used interactively to gain ideas and enrich understanding, rather than inputting questions and extracting answers. 

[Future vision of AI] To generate interactive lesson plans and deliver lessons that are more engaging. 

While there was some interest in the opportunities for AI to provide personalised learning, most parents – and pupils – had concerns about the quality AI could achieve as a personal tutor. 

Across the workshops, most participants emphasised the value of one-to-one support and feedback in education but acknowledged that it can be hard to attain for some, and is dependent on teachers’ availability. AI potentially providing the same support as a one-to-one personal tutor, immediately available and tailored to pupils’ needs, was seen as a clear opportunity to improve the quality of pupils’ education. We also heard from pupils that some felt personalised AI tools could “make learning more interactive” and be able to assess and identify areas pupils might need support in. 

Parent of post-GCSE pupil, Birmingham: 

It [AI tutor] might challenge them [the child] when the class isn’t ready to go on, but they could. 

Participants recognised the potential for AI to offer more tailored and targeted support calibrated to the specific needs of individual pupils. Some pupils felt that personalised AI tools could help them improve by providing support with subjects they struggle with (such as via extension activities or summary sheets). Some parents of children with SEND saw an opportunity for AI tools to provide individualised support for their child, ranging from supporting speech or writing, tracking their progress, or even using AI as a tool for early identification of potential SEND.  

Upon closer consideration of AI providing personalised learning experiences, parents and pupils raised concerns regarding the amount of data the AI would need in order to provide personalised experiences. Parents were also concerned about pupils using AI unsupervised – which they perceived would be the case if AI was used in this way. One barrier to using AI in this way was the association that some made with unsophisticated customer service “chatbots”, which most had experienced to lack nuance and understanding for individual situations. Despite some perceived benefits, parents of children with SEND in particular were hesitant about their child using these tools unsupervised due to concerns about unfair bias, lack of sensitivity, or access to harmful content. 

As a result, whilst many saw an opportunity for AI to fill a gap in personalised learning, parents and pupils were unconvinced that the quality of the personalised learning that AI could deliver would be sufficient. 

Parent of GCSE pupil, Birmingham: 

The potential is phenomenal, it’s like the child would have its own teaching assistant, there has to be a big buy-in from the kids, parents and teachers themselves. Thinking about the implementation though, you’re looking at farm size data storage, how is that funded, and the upkeep of that as well, that’s a big cost. 

Parent of post-GCSE pupil, Newcastle: 

It would need a lot of data about your child to support your child in each area that they’re struggling in. 

4.3 Concerns about AI use in education 

Lower quality of learning .

Concerns about overreliance on AI were prevalent among participants, particularly the perception that AI could reduce quality of education and socialisation through decreased human contact hours.  

Human-to-human learning was seen as critical to providing children with a good education. We heard that there would need to be clear boundaries for the use of AI to ensure pupils benefit from social interaction with their teachers. This concern was particularly pronounced among parents of children with SEND.  

This worry also compounded an overall concern about the amount of time children spend on screens. Some parents associated AI use in education with yet another chunk of their child’s time being spent on a screen rather than having human contact. There was uncertainty about what the long-term effects prolonged screen time might be on a child’s physical and mental wellbeing. Some parents suggested placing a time limit on the use of AI in the classroom and at home. Without this, there was felt to be a risk that, when combined with use of personal devices during their leisure time, children would never have a break from screens. 

Following the experience of the pandemic, participating pupils were particularly keen to maximise face-to-face learning experiences and were consequently less positive regarding uses of AI which could result in increased screen time to the detriment of face-to-face activities. 

GCSE pupil, Birmingham: 

I missed the social interaction of being in school [during the lockdowns implemented in response to Covid-19]. 

I feel restricted [when learning] online. 

Parent of primary school pupil, Birmingham: 

Too much screen time isn’t good for their head, it affects their sleep. 

Impact on teachers 

Related to their spontaneous concerns about AI’s potential impact on the labour market, participants worried about job losses caused by the displacement of teachers by AI. 

In participants’ initial reactions prior to guided discussion, we heard concerns about AI being used to make up for teacher shortages, effectively making human teachers redundant in the process. Participants balance this concern against what they see as the key opportunity: AI freeing up teachers’ time to do what they do best. 

Parent of pre-GCSE pupil, Bristol: 

What will the teacher be doing with the saved time? And how do you know the tasks being given will be relevant? 

Loss of key skills 

Both parents and pupils were concerned that the use of AI in education could result in pupils failing to develop key skills.  

In the context of overreliance on AI, there was concern that pupils could use AI to complete tasks such as maths problems or creative writing with little of their own input. There was also unanimous concern about AI leading to plagiarism. This overreliance could lead pupils to become unable to perform key skills without AI. 

GCSE pupil, Bristol: 

It feels really detrimental to use a lot of AI, because in the long-term you won’t know anything. You wouldn’t want to go to the dentist and they’ve done their homework with [LLM-powered tool] and they know nothing. 

Pre-GCSE pupil, Bristol: 

You need to be able to do it yourself and then get the feedback. 

Older kids might use it to write assignments so they’re not actually learning. Instead of researching and learning about it, they just put it into [LLM-powered tool] to get the answer. 

Some parents of children with SEND were concerned that their child could become over reliant on AI tools, particularly AI that personalised learning to their specific needs. Whilst this was seen to support them to some degree (as mentioned in section 4.1.4), it was also felt to risk a loss of key skills. 

As a parent, my son has dyslexia, so he has to programme in text, and the computer processes it and helps him type it. So it’d be useful for that […] But you don’t want him to rely on that. 

AI accuracy and risk of unfair bias 

Data quality – specifically whether AI could misinform pupils – was a concern for many. Some felt that AI had the potential to reinforce unfair biases.  

Throughout the workshops, many participants expressed uncertainty over whether, at its current stage of development, AI was good enough to be used in an educational context. As participants became more informed about machine learning and how it works, more participants questioned the quality of the data being used to train AI and whether there would be sufficient human oversight to quality check AI outputs.  

Expectations of where and how interactive AI tools would use data, such as marking class tests or providing feedback, was not consistent among parents and pupils. Some were concerned about AI processing and learning from incorrect answers. This was seen to be potentially damaging to the educational process if it led to pupils receiving incorrect feedback from AI. Uncertainty about how AI learns and generates information for different uses was a driver of concern for AI being used in education, where it feels more important that data is accurate than in other settings. As a result, parents and pupils felt it was imperative that AI tools were carefully assured, and that appropriate training was provided, before AI is used widely in schools. 

Inaccurate information being fed through the software could be really concerning. 

After showing participants a video about machine learning and an animation about bias (see Appendix), some expressed concern about the potential for AI to reinforce harmful biases and reproduce inaccurate information. This raised questions about how quickly AI can “unlearn” biases and how these unfair biases would be picked up. Unfair bias in AI was perceived as a potential risk, however, many parents acknowledged that this risk already exists in humans. The majority of participants wanted reassurance that AI was going to be monitored by a human to ensure the information given to pupils was accurate. 

The fact that AI is always learning, and it learns from the data the kids are putting in, so if they aren’t getting it right, it could take it off course. 

Harmful content 

Lack of safeguarding and the risk of encountering harmful content when pupils interact directly with AI were concerns for parents.  

We heard concerns, particularly from parents of younger pupils, about children being exposed to harmful content at school when using AI, as it didn’t feel clear whether there would be robust safeguards in place. This built on an existing worry about how children interact with technology and what they are exposed to online. Some parents therefore suggested they would want to limit this risk where possible by reducing unsupervised technology use, rather than introducing a further opportunity for their child to encounter harmful content. At the same time, many parents felt that they already had little control over their child’s consumption of online content, and educational tools were likely to be safer than unregulated access to the internet.   

Like on [social media platform], and it learns from what you’re watching, if you’re watching suicidal content it’ll keep showing you suicidal content. 

Parent of primary school pupil with SEND, Birmingham: 

She’s already talking to [voice assistant] all the time, it’s a different world for them. 

Clarification on whether pupils could be exposed to harmful content through their use of AI, and the steps to prevent this, was essential for all participants – but particularly parents. We consistently heard that parents would like a clear understanding of how AI will be used by their child and reassurance that steps are in place to protect their child from any harms. Additionally, both parents and pupils mentioned that they would expect there to be systems in place that would flag if a pupil was trying to access harmful content, or asked questions or mentioned real events in their personal or school lives that suggest a safeguarding issue. 

Overall, most parents felt more comfortable with their child using AI in schools with supervision from a teacher or member of staff. If it was to be used at home, some said they would want to oversee use. This was particularly important for parents of pre-school and primary school pupils, who were at times worried about whether there would be any security controls to prevent pupils accidently seeing harmful content. 

Unequal access to AI 

Parents and pupils were concerned that AI use would exacerbate existing inequalities in society. 

Almost all participants felt that if AI could indeed support children’s learning and potentially give them a head start, there should be equal access to it for all schools. Within the current education system, they assumed that the best AI tools would only be accessible to the schools that could afford it. They felt this would exacerbate existing inequalities, add to the unfair advantage of those who are better off, and lead to further stratification – of the education system, but also of the labour market and society as a whole. Parents of pupils who attend schools that are struggling or in disadvantaged areas felt resigned to inequality getting worse, with AI tools just another resource their child could miss out on. 

There was also some concern about variation in teachers’ abilities to use AI to its full potential, at least at first. Both parents and pupils worried that if training and support wasn’t provided to ensure all teachers meet a minimum level of proficiency with AI tools, some pupils would benefit less from AI use than others. 

As a result, many felt that the introduction of AI tools in schools should be centrally coordinated and funded, with tools standardised and quality assured, and profits from selling pupils’ work and data reinvested into the school system. 

What about schools that don’t have the facilities? It was hard enough before all this. 

It will just make the wealth divide worse. 

Poor and working class [areas] might not have access to computers, affluent areas will have the best access. 

Data assurances 

In order to give permission for their child’s data to be used, parents need more clarity and reassurance about how data will be collected, stored, used and shared.  

Concerns about privacy and data breaches were prevalent among parents, many of whom had questions about how and where their child’s data will be stored and shared. They were also concerned about the potential longevity of data, and the extent to which it could “follow their child through life” and affect their employment and further education opportunities. There were also concerns about potential data sharing between government departments. Parents of pupils with SEND in particular were concerned that the data could affect their child’s eligibility for state-funded benefits. 

Where does it go, where does it stop? Will it always be tagged to you? What about applying to university? 

Given these concerns, the majority of participants wanted to see data protection rules adhered to, and reassurances that data generated from pupils’ interactions with AI would not be used for wider, non-education related purposes. Alongside this, they needed clear information about why data is being collected, who will have access and how long it will be stored. For any use of AI in education, pupils’ personal data being accessed or hacked was a key concern which led to some participants feeling uncomfortable with pupil data being used to train educational AI tools. 

There is a sense of big brother about it all. Infant school, they’ve got your whole life in a data bank, how is that information going to be utilised. 

4.4 Acceptable and unacceptable use cases 

Table of ai use case acceptability , acceptable .

Acceptable uses of AI were felt to be those that help rather than replace teachers: 

Creating a lesson plan 

Generating class tests 

Generating class materials 

AI was also felt to be acceptable if being used by teachers as a tool to provide additional academic support: 

Generating feedback on pupils’ work 

Marking classwork 

Marking class tests 

Participants, especially parents, were hesitant to say AI was acceptable to personalise learning: 

Helping teachers decide what support a pupil needs 

Personal tutor chatbot for pupils 

There was some positive sentiment towards personalised learning and the potential benefits to the quality of education. When it was considered acceptable, specific conditions were required: 

The personalised AI tool is monitored and ‘signed off’ by a teacher 

Clear information is provided about what pupil data will be used and how it will be stored 

Parents’ permission is obtained before personalised AI tools are used 

Pseudonymised or anonymised data to be used, with robust data protection. 

Unacceptable 

Use cases felt less acceptable where AI error could negatively impact educational outcomes (and therefore the future prospects of children) by getting an exam mark wrong. 

• Marking exams 

5. Using pupil work and data to optimise AI tools 

5.1 summary .

Parents and pupils were generally comfortable with pupil work being used to optimise AI tools, with very few concerns about intellectual property. 

However, there was much more uncertainty about work being personally identifiable and personal data being shared outside of schools and DfE.  

Both parents and pupils needed reassurance about the de-identification or anonymisation of data, especially concerning special category data, which was seen as requiring more protection, or the links to other information, such as patient records (such as for children with SEND). 

Although neither parents nor pupils thought that they should be directly compensated for providing their work or data to tech companies, they strongly felt that private companies should be required to share at least some of the profit with schools (via DfE). 

After receiving an explanation of machine learning, participants were provided with examples of different forms of pupil work (such as homework, class work, mock exams, exams) and data (such as name, age, SEND status) that could be used to optimise AI tools. 

5.2 Pupil work 

Pupil work that can be used to optimise ai tools .

Parents and pupils were comfortable with pupil work being used for AI tool development in the vast majority of cases.  

Most participants understood that greater breadth and volume of data provided to optimise AI tools results in AI tools having a greater understanding of what constitutes ‘good’ and ‘bad’ work, and being able to provide constructive feedback. Most grasped the need for AI tools to be optimised with work spanning higher to lower grades, and some specifically pointed out that without examples of ‘bad’ work and the ability to identify what makes work stronger or poorer, AI tools would not be able to assess work as needed.  

In particular, participants felt that AI tools would need to be optimised with as many different styles of work as possible, in order to fairly and accurately assess and support pupils with differing abilities and needs, especially children with SEND. They noted the particular importance of this in more subjective cases, such as in creative writing. 

For me it would be that what is put into the system is enough to get a positive outcome for the children. 

Although there was confusion about how exactly AI tools would learn from pupils’ work, parents and pupils still felt pupil work was fine to share. By the end of the engagement, both parents and pupils understood that providing a wide range and quality of work would improve AI outcomes in the long run. As a result, they accepted data sharing as a necessity. 

Concerns about the use of pupil work to optimise AI tools 

While most types of work are fine to be used, usage needs to be clearly communicated to avoid concerns about plagiarism or penalisation. 

The topmost concern about sharing work with AI tools was of more substantial pieces of work (such as coursework) being plagiarised by other pupils. Parents and particularly pupils’ first assumption was that AI tools could be used by other pupils to generate work that draws heavily from their own work, leading to their efforts being co-opted. Some understood AI ‘learning’ from pupils’ work to mean that AI would then use it to create new pieces of work for other pupils. 

Post-GCSE pupil, Birmingham: 

Not okay to share [Homework] – because your schoolwork is your intellectual property, it’s you and you did that. If the AI takes that then you can’t copyright it. 

It can’t use everyone’s homework so it can be copied and plagiarised. 

Despite this assumption, this concern was only notable for larger pieces of work that pupils spent considerable time on, with little concern about other more routine work produced by pupils (such as class test answers).  

There was also concern from some about pupil work being shared more widely by AI tools, with pupils in particular worrying that this would mean that examples of ‘bad’ work they produced would be circulated among or accessible to other people and cause embarrassment or judgement.  

Further explanation of how work would be used to optimise AI tools, rather than being regurgitated or circulated, provided reassurance to uncertain pupils and parents. Emphasis on the volume of data required to optimise AI tools, and reiterating that an individual piece of work would be one among millions of pieces of pupil work, also reassured some parents and pupils.  

Additionally, some parents noted that examples of high-scoring essays or exam answers were already shared more widely, and did not feel sharing work with AI tools would be cause for more concern.  

However, pupils and parents maintained some doubts about the limitations of AI optimisation, especially in relation to more creative or subjective pieces of work. 

Some parents and pupils were unconvinced by the ability of AI tools to assess work for subjective subjects requiring more nuanced interpretation such as PSHE, or creative subjects like Art and English. They did not feel that pupil work would optimise tools to the extent needed for them to achieve a human level of expertise and understanding, making the use of pupil work feel futile. 

I think it makes sense with the factual subjects, because with science and maths most of the time there is a definitive answer. But like English there is a main answer but there are other right answers too. 

Concerns about plagiarism were also heightened for creative work such as artwork or longer essays, which pupils felt was more obviously valuable intellectual property and could hold more personal significance than written work. As above, they struggled to understand how AI tools could be optimised using this work or to believe that a sufficient level of optimisation could be achieved. 

It wasn’t very clear about the copyright situation, I think that’s a huge thing to know, for all children, some children have been designing logos and stuff from like 13/14. 

Acceptability of the use of different types of pupil work 

Acceptable pieces of work were those felt to be less ‘valuable’, with fewer concerns about them being plagiarised or misinterpreted by the AI: 

Participants were less sure about the use of work that more effort had gone into or that felt more subjective or creative: 

Coursework 

There was more reluctance about the use of more ‘serious’ pieces of work with higher stakes, and more reassurance needed for their use: 

Mock exams 

Exam answers 

5.3 Types of data 

Types of data .

Parents and pupils were most comfortable with anonymised demographic data being used and shared. 

In almost all cases, participants were comfortable with anonymous demographic data being used to optimise AI tools. They particularly recognised the importance of providing AI tools with information on pupils’ ages or year groups, in order to accurately gauge the progress and performance of pupils at this level. 

While there was some confusion about the need for data like gender, most participants were nevertheless fine with it being provided as it was not a threat to pupils’ anonymity. A few parents expressed concern that this data could contribute to unfair bias or discrimination, and some parents and pupils stressed the need for data about gender in particular to be inclusive, reflecting pupils’ own gender identities rather than erasing them. 

You’ve got bias in AI but its already there, probably easier to correct than it is in a person. 

More conditions were attached to the use of pseudonymised and special category data which was seen as requiring more protection, despite recognition of its necessity and openness to its use. 

[On including gender] It depends what it’s being used to train it for. It doesn’t really bother me but bias can happen. 

Parents and pupils understood that in order for AI tools to provide personalised, lifelong support for pupils that is tailored to their educational needs and learning styles, data linkage is necessary via pupil identifiers. There was openness to this due to the potential benefits for pupils and the perception that this tailored support would lead to better outcomes than generic AI use.  

However, participants were deeply concerned about the security of this data, especially special category data, fearing that any breaches would result in comprehensive datasets about individual pupils’ demographics, abilities, and weaknesses being shared more widely and exploited. This was a particular worry for parents of children with SEND, for whom concerns centred around their children’s future opportunities. They were particularly concerned that their child’s SEND status could be shared between government departments which could impact the benefits their child might be entitled to, or about future employers accessing their child’s data via the companies developing AI, impacting their child’s future.  

Both parents and pupils strongly felt if data is pseudonymised, identifiers should be held at a school level and ought not to be shared with tech companies or the government. There should also be stringent restrictions and safeguards in place to ensure the security of this data, with assurances communicated to parents and pupils of how the data is stored, who has access to it, and when and where it will be used. 

Data should only be shared with schools, parents and education department. 

Parents and pupils felt strongly that personally identifiable data should not be used in any circumstance. 

Participants emphasised that data that allows individual pupils to be identified, such as name or date of birth, should not be used. This data was seen as unnecessary for AI optimisation in an educational context, and was deemed to carry too many risks for pupils when linked with the other data being collected, particularly special category data. While parents were more resistant to the use of this data, citing the concerns about future opportunities covered above, pupils also strongly preferred the use of their data in an anonymised or pseudonymised form. 

Acceptability of the use of different types of pupil data 

Use of data that could easily be anonymised and was felt to be relevant to AI understanding of pupils’ work was widely accepted. 

Assurances were needed about data perceived as more sensitive or pseudonymised, particularly to address concerns about data security and storage: 

Pupil identifier 

Information about SEND (or any health conditions) 

Data identifying pupils was unacceptable and felt to be unnecessary: 

Date of birth 

5.4 Control of pupil work and data 

Parents and pupils .

All participants expected to be involved in decisions made around the use of pupil’s work and data, with parents and pupils having final say. 

While parents and pupils didn’t expect to make specific decisions about AI optimisation, they did expect to be consulted on whether and by whom pupil work and data can be used. There was widespread consensus that work and data should not be used without parents’ and/or pupils’ explicit agreement. Parents, in particular, stressed the need for clear and comprehensive information about pupil work and data use and any potential risks relating to data security and privacy breaches.  

Pupils also felt that knowing how their work and data would be used would be important, and that they should have a say alongside their parents, especially if they were old enough (see 7.2 Parent and Pupil permission for further discussion of age at which pupils should have a say). However, they were less likely to require extensive detail about its intended use, reflecting their higher level of comfort with data sharing and acceptance of its necessity in order to benefit from the tools using it. With the understanding that pupil work is their intellectual property, some pupils were more concerned about the use of their work than their data (see 6.1.2 for concerns about work use). 

If child’s work is going to be used/processed in AI the parents should be advised. 

Schools were most trusted to make decisions about the use of pupils’ work and data, as well as to hold data that was seen as more sensitive (such as SEND data or pupil identifiers). Where concerns about school involvement existed, they were centred around unequitable AI use and access. 

Parents and pupils felt that schools could be relied on to make decisions in the best interest of pupils and to prioritise educational outcomes and safety over other considerations like AI development and profit. Central to this trust was the widely held perception that schools are not primarily profit-motivated and are already trusted to safeguard pupils, which led to the assumption that schools can be relied upon to continue doing this when it comes to AI. As a result, participants wanted schools to have the final say in how pupil work and data is used, with the ability to approve or reject uses suggested by the government or tech companies if they are felt to harm pupils or jeopardise their privacy and safety.  

Schools were also trusted to hold pupil data, with many who were uncertain about special category data being shared and used feeling reassured about this data being collected if schools could control its use and guarantee that it would not be shared beyond the school. 

The ID number has to stay within the school and be really safe. 

I would want to feel the school (teachers especially) have all the info and are confident the AI is safe. 

A few parents noted that schools may not all choose to use AI, or that there could be disparities within schools if it were left up to teachers’ discretion and some refused to integrate AI into their teaching. Some worried that schools with fewer resources would be left behind as other schools (such as private schools) adopted AI use to their advantage. There was also a minor question about the impact schools’ teaching philosophies might have on the decision to use AI or not, for example whether religious schools might choose not to use a standardised AI tool in order to have control over what exactly students learn.  

However, there was little real concern about schools’ oversight of AI tools and pupil work or data, with most participants feeling the more control schools have, the better. 

Parent of GCSE pupil, Bristol: 

What about schools that don’t have the facilities? It was hard enough before all this.  

Access is a concern, ensure there’s a level playing field across the board.  

Government 

Parents and pupils saw a role for DfE in setting rules around AI use and (to a lesser extent) pupil work and data, recognising the need for a central authority. However, many participants were worried about potential negative impacts from the use of AI tools and pupil work and data by government.  

Most felt there was a role for DfE having a say in how AI is used in schools, feeling that central guidelines would make AI use more consistent. DfE was also generally trusted to make decisions in the best interest of pupils and with education rather than profit in mind. This was seen to necessitate its involvement in any decisions made by tech companies. 

However, trust in DfE to set rules was predicated on school involvement in decisions made, particularly those around the use of pupil work and data to optimise AI tools. While there was a need for DfE to provide central oversight, parents and pupils were still hesitant to hand over complete control of pupil data. In this, participants’ preferences reflected pre-established views that schools, being closer to pupils and in close communication with them and their parents, were more familiar with pupils’ needs and parents’ concerns, and were therefore more likely to make decisions accordingly. 

Pupil-centric at every stage, profits should be distributed to the schools and [for] development not just led by tech companies, with the education [sector] as well. 

There was a notable tension between the desire and perceived need for robust government oversight, and concern around government involvement. Many parents and pupils worried that other government departments might not make decisions in the best interest of pupils, or might not have the ability to direct efficient, effective, and beneficial use of AI. 

My initial thought is an independent regulatory body so they’re a step away from it but I don’t know what that looks like. 

Parents also worried about how pupils’ performance and special category data (such as SEND status) could be used by government if held in a central database accessible beyond DfE. There were also concerns around how particular agendas might determine the content used to optimise AI and therefore how and what AI tools teach pupils.  

This was a particular concern for parents of children with SEND, who worried that their children’s future could be affected if pseudonymised or personally identifiable data is held and accessed by government beyond their time at school. They required reassurance that data showing their children’s level of ability and any SEND would not be used in future, for example to affect their entitlement to government assistance.  

Many parents also generally worried about increased surveillance if provided with data on children throughout their formative years, particularly if AI use becomes standard and most or all of the population’s data in this context is held and used by a limited number of central organisations. 

Thinking about the work…How long will it be kept there - who will it be shared with and how much of my child’s personal info is attached to it? 

Participants feared that particular viewpoints or biases, including those within the curriculum, could become more entrenched in AI and harder to correct. For these participants, involvement of independent experts within the field of AI and education could mitigate some of this risk by providing a check for decisions and ensuring a balance of views. 

I feel like they’re trying to push the kids in a certain direction, and then the government gets to know everything [decision] they make. 

Tech companies 

Trust in tech companies was extremely limited and there was little to no support for them to be granted control over AI and pupil work and data use. 

Profit was almost universally assumed to be the primary or sole motivation of tech companies, rather than the desire to improve education and pupil outcomes. Reflecting starting views of tech companies as non-transparent and assumptions that data is sold on to third parties, participants did not trust them to protect or use data responsibly. Parents and pupils assumed that given free rein and with no oversight, tech companies would choose to sell data on to other companies with little concern for pupil privacy or wellbeing. 

I think yes, the company is going to benefit, that’s economics, but I think it would be good to give it back to schools. 

Yeah, you kind of want to know what type of people are developing [it], if the people running it are doing it for the wrong reasons, it could get out of hand, you want to know they’re doing it for the right reasons. 

Participants did note that tech companies working in close partnership with schools or DfE, with clear oversight and regulation, would provide some assurances that they would be more likely to use pupil work and data responsibly and to benefit pupils. 

6. Conditions for use 

6.1 summary .

Participants’ identified the following conditions for the use of AI in education and the use of pupils’ work and data to optimise AI tools: 

Human oversight: Human involvement in AI use to correct for error and unfair bias, as well as providing safeguarding. 

Parent and pupil permissions: Providing parents and pupils with the necessary information and the opportunity to make informed decisions about the use of their data. 

Standardisation and regulation: Ensuring that AI tools used within schools are of a uniform standard to avoid exacerbation of inequalities, with strict oversight of any tech companies providing the tools. 

Age and subject restrictions: Using AI tools only where appropriate and where they add value. Strict age restrictions on direct interaction with AI.  

Profit sharing: Ensuring that tech companies that benefit from accessing data share some of their profits so that this can be reinvested into the education system and benefit schools and pupils – while recognising that private companies will need to be incentivised to develop better tools. 

6.2 Human oversight 

Participants stressed the importance of human involvement in AI use at every step of the process. 

Given the recent developments in AI, and the need to continue to optimise it, the use of any tools in the classroom or at home was seen as risky if not overseen by humans, at least to begin with. This concern was particularly pronounced after participants heard about the risks of bias and about AI only being as good as the data it learns from. Many noted that AI can make mistakes or ‘hallucinate’ inaccurate responses, and would need humans to ensure nothing was being taught or assessed incorrectly. There was also an assumption that errors made by AI would be harder to correct than those made by a teacher, which can often be addressed directly by parents or pupils in conversation. This means AI tools should always be checked, with any resources created looked over by teachers, any marking or feedback generated by AI tools reviewed by teachers, and any tests or exams marked by AI being assessed by teachers or external markers.  

Parents were particularly keen that pupils’ AI use is supervised or at least controlled, and that AI tools are never used as a substitute for a teacher. Pupils similarly stressed that learning should not be solely delivered by an AI tool operating independently, as teacher-pupil interaction is highly valued and most felt some level of human subjectivity is always needed. Pupils also worried that AI use without human oversight might mean errors made by AI are overlooked, leading to them not learning the skills they need or being taught incorrectly. Any potential errors should and could be picked up by earlier human assessment of AI outputs. 

Parent of Pre-GCSE pupil, Newcastle: 

The [AI] tool should supplement the teacher, not replace or undermine [the teacher]. A pupil-teacher relationship is still very important for [the pupil’s] development. 

6.3 Parent and pupil agreement for use of work and data 

Both parents and pupils felt they should be enabled to make free and informed decisions about how pupil work and data is used. 

This means having an understanding of when AI tools will be used and why, and how pupil work and data will be used to optimise them and why. Almost all participants felt that agreement should be a pre-condition of AI use. 

Despite consensus that agreement should be required, views around the details of agreement differed: 

Parents emphasised their responsibility to make informed decisions for their children’s wellbeing. They therefore felt their permission ought to be required, particularly for younger pupils (generally those aged under 16). Many were resistant to the idea that their children could make these decisions for themselves, wanting to have a say in all aspects of their children’s education.  

Pupils tended to attach more importance to their own comfort with AI and work and data use, particularly with the understanding that the work they create is their intellectual property. Most pupils we spoke to had experience of permitting data sharing for themselves when signing up to and/or using apps and websites, and most did not view agreeing to work and data use for AI optimisation purposes any differently. While many were happy for their parents to also have a say, some felt this should not supersede their own wishes, and that pupils should have final say over the use of their work and data above a certain age (13 or 16). 

Parent of GCSE pupil, Birmingham:  

Up to 16, it’s definitely a parental choice, but as they start to make their own choices this would be included. 

Might be good to trial with older kids, because we can already consent ourselves and then you could show the parents the positive data. 

Expectations for how permission would be provided varied, but most parents described an “opt-in” model and expected to be given the chance to understand and agree to all potential uses of their child’s data and work. Parents suggested that this agreement could be “staggered” as understanding of AI tools and comfort with its use grows, and that schools and DfE could make decisions about AI use within the parameters of permission provided. Generally, the expectation was that even completely anonymised data and work would require some level of permission to be shared and/or used, though most participants indicated they would agree to its use. However, there was little consideration of how this would work in practice, especially alongside equitable access to AI for all pupils and schools, which was seen as an important condition for its use.   

Generally, pupils expressed higher levels of comfort with sharing their data than parents, many of whom had serious concerns about data privacy, security and storage. A few pupils assumed their parents would lack understanding and would be reluctant to allow them to share their data as a result, in contrast to their own willingness to share it. Many parents noted that widespread AI use and normalisation of data-sharing would make them feel more positively about it and more likely to easily provide permission, assuming that once AI use becomes “tried and tested”, concerns are likely to be alleviated. 

6.4 Standardisation and regulation 

AI use in schools should only be through standardised and strictly regulated tools to ensure quality control and equity of access. 

Parents and pupils stressed that all schools should have access to the same, quality assured, AI tools.  Many suggested this could be provided by certification processes sanctioned by schools and the government, with only AI tools that are officially tested and meet a minimum performance standard being approved for use in education. For many, this would alleviate concerns about some pupils or schools benefitting over others by accessing more developed AI tools than others. 

Concerns about the quality of AI tools also led to worries that pupils could be penalised for, or disadvantaged by, poor teaching or support provided by low-performing AI tools. Pupils worried that they would be held accountable for any errors committed as a result of incorrect AI teaching or support. Parents also wanted guarantees that, in cases where low-performing AI tools led to poor pupil performance, the pupil would not be penalised, and emphasised a need for regulations ensuring clear accountability in case of AI error or misuse. In particular, parents of primary and pre-school children wanted guarantees of accountability in the case of malicious or inappropriate content being propagated by AI tools, along with strong and appropriate content safeguards to ensure they are safe for children to use. 

Parent of Post-GCSE pupil, Newcastle: 

If used in marking exams, make sure its accurate so pupils are not disadvantaged. 

While there was no overall consensus on who ultimately could be held accountable for any issues that arise, many suggested DfE and schools both have a responsibility to ensure AI tools are fit for use, and to minimise and rectify any errors or misuse. Others felt that this responsibility should lie with tech companies, and that as the developers of these tools, they should be made to answer if their use harms pupils. 

Regulation was also felt to be crucial for ensuring stringent data collection, privacy, and security. 

DfE and the wider government were generally seen as responsible for setting, communicating, and maintaining these standards. Parents in particular expected clear rules to be established for:  

How pupil data can be collected; 

For what purpose it can be collected; 

How it will be stored; 

How long it will be stored for; and  

Who can access it.  

Parents emphasised the importance of these regulations being put in place and communicated as a pre-condition for widespread AI use in education. 

6.5 Age and subject restrictions 

Parents and pupils were in agreement that the use of AI tools should be restricted, with the most accepted uses involving older pupils and subjects seen as “objective”. 

There was a general consensus that AI tools would be best used directly by pupils in secondary education, at which point both parents and pupils felt that pupils would be able to confidently and safely interact with the technology. There was less concern about pupils not developing necessary social skills at this point (due to interacting with AI tools alongside teachers), and less concern about the use of pupils’ data and work. Overall, both parents and pupils felt most comfortable with AI tools being directly used by pupils old enough to understand the tools and agree to their use. Parents’ estimation of this age tended to be higher than pupils, as pupils were more likely to set the minimum age at 11 or 13, while many parents felt that pupils would only be able to meaningfully agree at age 16. 

GCSE pupil, Birmingham:  

Maybe it’s not appropriate for young kids, you should have restricted access, and it might not simplify it enough. 

Parents of primary and pre-school pupils were least comfortable with the potential use of AI tools, citing concerns around unintentional exposure to harmful content and children not picking up the skills they need to develop. At this age, the importance of play and socialisation was emphasised, and parents worried these elements of young children’s day-to-day education would be lost or minimised through reliance on AI. 

Both parents and pupils were most comfortable with AI being used to support learning (and particularly to mark work and/or provide feedback) in subjects seen to have more concrete, and therefore more easily assessed answers, such as Science or Maths. These subjects, which contain simple answers (for example, multiple choice), were seen as less likely to confuse AI tools or to be incorrectly assessed due to bias or a lack of understanding. Participants broadly felt reassured that AI tools could be sufficiently optimised to correctly assess these forms of work and would trust their use when overseen by a teacher. 

There was considerably less openness to AI being used to support marking or to assess more creative or subjective subjects like Art, English, Religious Studies or Social Studies. Participants deeply doubted that AI could engage with pupils’ schoolwork on these subjects in the same way as a human, or to grasp their nuances as a teacher would. They also broadly felt that these forms of schoolwork are more personal to pupils, or involve more effort to create, making the stakes of any AI error feel higher. 

Parent of primary school pupil, Bristol: 

You lose being creative, the students being creative, relying on an AI to educate them, and then using AI to do their homework, they’re going to lose that creativity. 

6.6 Profit sharing 

There was widespread consensus that, if profit were to be generated through the use of pupil’s work to enhance AI in education, schools would be the preferred beneficiaries, and resistance to the idea of tech companies being the sole profiteers. 

Generally, parents and pupils acknowledged that pupils profiting individually from the use of their work and data would not be feasible, but almost all strongly believed that any profits derived from this data use should be distributed among schools to enable pupils to benefit. This belief was intensified by the understanding of intellectual property and pupils’ ownership of their work and data. Participants suggested a minimum share of the profits being handed back to schools, but views on how this should be done varied, with many feeling this should be done to maximise equality of access to AI (with profits being used to fund AI tools and resources for schools who are not able to do this themselves), while others felt profits should be equally shared. Few participants thought profits should correspond to each school’s level of data sharing and AI use, and participants were especially positive about profits being used to level the playing field for schools. 

While participants did want schools to profit from AI use, some felt this could happen through profits being used by local authorities or regional bodies to improve education in the area, or by DfE to improve the education sector at a national level, rather than being distributed to individual schools. Most were comfortable with profits being shared between schools and DfE, however, the general assumption was that pupils would benefit most directly if profits were distributed to individual schools. 

Participants accepted that tech companies would profit in some way from the use of pupil work and data, but the consensus was that they should not be the sole beneficiaries. Parents of children with SEND were particularly negative about AI tool development becoming a money-making exercise. Understanding of how exactly tech companies could profit was limited, with most assuming that they would make money by selling pupils’ data to third parties. There was a lack of awareness of other ways in which they might benefit from this data use such as by developing other AI tools for commercial use. On prompting, this form of benefit was generally seen as acceptable if used to develop educational tools for use outside the education sector, but unacceptable if used to develop tools for other purposes. This possibility was seen as misusing data for something other than its intended use, reflecting existing discomfort and concerns about data being sold by tech companies without participants’ knowledge or agreement. 

7. Reflections and implications for future research 

7.1 methodological reflections .

Due to time pressures, the in-person fieldwork was carried out as a single six-hour session per location. Sitting still and processing information for this length of time can be challenging for adults’ attention spans and energy, but it was particularly difficult for pupils. We knew we would need to share large volumes of information, and aimed to make the sessions as engaging as possible by:  

Using different types of stimulus (including animations, videos from experts, worksheets, hands-on demonstrations of AI tools);  

Providing written summaries of all videos; and  

Including activities that would require participants to stand up and move around (including voting exercises). 

However, in the end, we had to adapt our approach in several ways to counteract participant fatigue: 

In the first workshop, we asked participants to compare three different future scenarios, with detailed information about the different use cases of AI in education, the types of data and work that would be used to optimise it, and the conditions in place to regulate its use. This activity took place towards the end of the workshop, and participants found it very challenging to compare such abstract, yet detailed, scenarios. In subsequent workshops, we focussed instead on asking participants to describe the future they would like to see, rather than testing potential scenarios first. 

We gave pupils additional break time after lunch. By this point they had understood the basic principles of machine-learning and this meant they were more refreshed for the final activity where we discussed conditions for use. 

Some lessons for future engagement workshops: 

Including more interactive tools can help to bring concepts to life and keep participants engaged. Participants who had not previously used LLM tools, benefited from being able to see how it works in reality. For future engagements, it may be worth thinking carefully about how devices and applications can be used in sessions. 

There are some practical implications for running joint sessions for parent and pupil groups, as they have different needs. We adapted discussion guides for parents and pupils and, as much as possible, made all stimulus suitable for the youngest sample members. However, it may be worth considering splitting groups, so their agendas are decoupled from one another, allowing more flexibility and further adaptation to suit participants’ age.  

Shorter sessions over several weeks, as well as a mix of in-person and online fieldwork, may be more suitable for complex topics such as this. Online participants, who had a week between workshops, returned to the second session refreshed. In addition, many had used the interim to think about or discuss what they had learnt with friends or family, which meant they brought more nuanced perceptions and opinions to the final session. 

7.2 Areas for future research 

The research showed that awareness, understanding, and opinions of AI are all still evolving. As the technology becomes more established, the public will be further exposed to its applications and form opinions based on those experiences. However, we also know how important the commentary and opinion of others - both expert and lay person - are in shaping views and impacting trust. For parents in particular, other parents are powerful influencers, so it will be important to continue engaging with this audience to understand how they feel about the use of AI in education. 

There are also a number of specific questions surfaced by the research, which we feel warrant further exploration: 

The relationship between private interest and public good : How comfortable are parents and pupils with private companies profiting and how are they held to account and incentivised to ensure they put public good first?  

Oversight and coordination of data sharing : To what extent is there support for the central management and facilitation of data access across government and with researchers and private companies? Would parents and pupils be comfortable with an “EDR UK” organisation, similar to HDR UK, ADR UK, or SDR UK? 

Equal access and opting out : What happens if you want to opt out? And how can we ensure nobody is left behind? 

8. Appendix 

8.1 demographic sample breakdown .

8.2 Expert video breakdown 

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Current research status and future trends of vibration energy harvesters.

1. Introduction

2. electromagnetic vibration energy harvester, 2.1. working principle and characteristics of the electromagnetic vibration energy harvester, 2.2. advances in electromagnetic vibration energy harvesters, 3. piezoelectric vibration energy harvester, 3.1. operating principle and characteristics of piezoelectric vibration energy harvester, 3.2. progress of research on piezoelectric vibration energy harvesters, 4. friction electric vibration energy harvester, 4.1. mechanism of operation and characteristics of the friction electric vibration energy harvesters, 4.1.1. friction nanogenerator, 4.1.2. principle of friction electric vibration energy harvester, 4.2. advances in friction electric vibration energy harvesters, 5. electrostatic vibration energy harvester, 5.1. the working principle of the electrostatic vibration energy harvester and its characteristics, 5.2. current research status of electrostatic vibration energy harvesters, 6. magnetostrictive vibration energy harvester, 6.1. operating principle and characteristics of magnetostrictive vibration energy harvesters, 6.2. current status of research on magnetostrictive vibration energy harvesters, 7. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

• Bouhedma, S.; Bin Taufik, J.; Lange, F.; Ouali, M.; Seitz, H.; Hohlfeld, D. Different Scenarios of Autonomous Operation of an Environmental Sensor Node Using a Piezoelectric-Vibration-Based Energy Harvester. Sensors 2024 , 24 , 1338. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, X.; Yin, G.; Sun, T.; Xu, X.; Rasool, G.; Abbas, K. Mechanical Vibration Energy Harvesting and Vibration Monitoring Based on Triboelectric Nanogenerators. Energy Technol. 2024 , 12 , 2300931. [ Google Scholar ] [ CrossRef ]
• Yue, O.; Wang, X.; Xie, L.; Bai, Z.; Zou, X.; Liu, X. Biomimetic Exogenous “Tissue Batteries” as Artificial Power Sources for Implantable Bioelectronic Devices Manufacturing. Adv. Sci. 2024 , 11 , e2307369. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Wang, Z.J.; Jiang, S.L.; Wang, Q. Retrospectives and Perspectives of Vibration Energy Harvest Technologies. Mech. Sci. Technol. 2019 , 38 , 985–1018. [ Google Scholar ] [ CrossRef ]
• Ke, Z.; Deng, Z.; Ren, T.; Liu, X.; Yi, H.; Liu, Z.; Li, H.; Sun, Y. Simultaneous vibration suppression and energy harvesting system design via electromagnetic shunt damper for high temperature superconducting pinning maglev. Mech. Syst. Signal Process 2024 , 214 , 111374. [ Google Scholar ] [ CrossRef ]
• Zhao, L.; Zhang, H.; Liu, D.; Zou, Y.; Li, Z.; Liu, B. Application of nanogenerators in self-powered microfluidic systems. Nano Energy 2024 , 123 , 109432. [ Google Scholar ] [ CrossRef ]
• Christenson, G.N.; Yu, Z.; Frontiera, R.R. Wavelength Dependence of Plasmon-Induced Vibrational Energy Transfer in Fluorophore–Plasmonic Systems. J. Phys. Chem. 2024 , 128 , 10784–10789. [ Google Scholar ] [ CrossRef ]
• Qi, Y.; Zhao, J.; Zhang, C. Review and Prospect of Micro-nano Vibration Energy Harvesters. J. Mech. Eng. 2020 , 56 , 1–15. [ Google Scholar ]
• Huo, S.; Wang, P.; Long, H.; Ren, Z.; Yi, Q.; Dai, J.; An, B.; Wang, P.; Wang, Y.; Gao, M.; et al. Dual-mode electromagnetic energy harvester by Halbach arrays. Energy Convers. Manag. 2023 , 286 , 117038. [ Google Scholar ] [ CrossRef ]
• Liu, C.; Meng, A.; Chen, W.; Li, H.; Song, H. Research status and development trend of vibration energy harvesting technology. Equip. Manuf. Technol. 2013 , 12 , 43–47. [ Google Scholar ]
• Qi, L.; Song, J.; Wang, Y.; Yi, M.; Zhang, Z.; Yan, J. Mechanical motion rectification-based electromagnetic vibration energy harvesting technology: A review. Energy 2024 , 289 , 130030. [ Google Scholar ] [ CrossRef ]
• Hadas, Z.; Vetiska, V.; Vetiska, J.; Krejsa, J. Analysis and efficiency measurement of electromagnetic vibration energy harvesting system. Microsyst. Technol. 2016 , 22 , 1767–1779. [ Google Scholar ] [ CrossRef ]
• Choi, W.K.; Kim, H.J.; Park, C.W.; Lee, B. Study on Electromagnetic Energy Transducer in Ambient Vibration. IET Power Electron. 2018 , 11 , 1983–1990. [ Google Scholar ] [ CrossRef ]
• Wang, Y.; Gao, Y.; Chen, Z.; Zong, R.; Li, Y.; Guo, R.; Azam, A.; Qi, L.; Zhang, Z. Performance analysis of electromagnetic vibration energy harvester under square excitation. Int. J. Mech. Sci. 2024 , 271 , 109127. [ Google Scholar ] [ CrossRef ]
• Peng, Y.; Zhang, L.; Li, Z.; Zhong, S.; Liu, Y.; Xie, S.; Luo, J. Influences of Wire Diameters on Output Power in Electromagnetic Energy Harvester. Int. J. Precis. Eng. Manuf. Green Technol. 2022 , 10 , 205–216. [ Google Scholar ] [ CrossRef ]
• Lo Monaco, M.; Russo, C.; Somà, A. Identification Procedure for Design Optimization of Gravitational Electromagnetic Energy Harvesters. Appl. Sci. 2023 , 13 , 2736. [ Google Scholar ] [ CrossRef ]
• Sun, H.; Peng, H.; Xiao, H.; Liu, X.; Chen, Y.; Gao, K.; Wang, S.; Xu, P. An Optimized Design of Compact Self-Powered Module Based on Electromagnetic Vibration Energy Harvester Considering Engineering Feasibility. IEEE Trans. Ind. Appl. 2023 , 59 , 767–778. [ Google Scholar ] [ CrossRef ]
• Ghouli, Z.; Hamdi, M.; Belhaq, M. Energy harvesting from quasi-periodic vibrations using electromagnetic coupling with delay. Nonlinear Dyn. 2017 , 89 , 1625–1636. [ Google Scholar ] [ CrossRef ]
• Nicolini, L.; Castagnetti, D.; Sorrentino, A. A tunable multi-arm electromagnetic pendulum for ultra-low frequency vibration energy harvesting. Smart Mater. Struct. 2022 , 31 , 115009. [ Google Scholar ] [ CrossRef ]
• Xiong, L.; Gao, S.; Jin, L.; Sun, Y.; Du, X.; Liu, F. Study on the Influence of Coil Arrangement on the Output Characteristics of Electromagnetic Galloping Energy Harvester. Micromachines 2023 , 14 , 2158. [ Google Scholar ] [ CrossRef ]
• Sun, R.; Zhou, S.; Li, Z.; Cheng, L. Dual electromagnetic mechanisms with internal resonance for ultra-low frequency vibration energy harvesting. Appl. Energy 2024 , 369 , 123528. [ Google Scholar ] [ CrossRef ]
• Kumar, A.; Kiran, R.; Chauhan, V.S.; Kumar, R.; Vaish, R. Piezoelectric energy harvester for pacemaker application: A comparative study. Mater. Res. Express 2018 , 5 , 075701. [ Google Scholar ] [ CrossRef ]
• Zhang, Y. Sandwich Piezoelectric Transducer and Its Applications ; Science Press: Beijing, China, 2006. [ Google Scholar ]
• Zhang, Y. Key Technology Study of Mass-Sharing Cantilever Array Based Micro Piezoelectric Vibration Energy Harvester. Master’s Thesis, Chongqing University, Chongqing, China, 2016. [ Google Scholar ]
• Galassi, C.; Roncari, E.; Capiani, C.; Craciun, F. Processing and characterization of high Qm ferroelectric ceramics. J. Eur. Ceram. Soc. 1999 , 19 , 1237–1241. [ Google Scholar ] [ CrossRef ]
• Fang, H.-B.; Liu, J.Q.; Xu, Z.Y.; Dong, L.; Wang, L.; Chen, D.; Cai, B.C.; Liu, Y. Fabrication and performance of MEMS-based piezoelectric power generator for vibration energy harvesting. Microelectron. J. 2006 , 37 , 1280–1284. [ Google Scholar ] [ CrossRef ]
• Shen, D.; Park, J.H.; Ajitsaria, J.; Choe, S.Y.; Wikle, H.C.; Kim, D.J. The design, fabrication and evaluation of a MEMS PZT cantilever with an integrated Si proof mass for vibration energy harvesting. J. Micromech. Microeng. 2008 , 18 , 055017. [ Google Scholar ] [ CrossRef ]
• Ye, J.; Ding, G.; Wu, X.; Zhou, M.; Wang, J.; Chen, Y.; Yu, Y. Development and performance research of PSN-PZT piezoelectric ceramics based on road vibration energy harvesting technology. Mater. Today Commun. 2023 , 34 , 105135. [ Google Scholar ] [ CrossRef ]
• Xiang, Z.Y.; Zhang, J.K.; Li, S.J.; Xie, S.L.; Liu, F.P.; Zhu, R.D.; He, D.K. Friction-induced vibration energy harvesting via a piezoelectric cantilever vibration energy collector. Tribol. Int. 2023 , 189 , 108933. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Zhang, G.; Wang, W. A piezoelectric cantilever-beam-spring-pendulum oscillator for multi-directional vibration energy harvesting. Commun. Nonlinear Sci. Numer. Simul. 2024 , 138 , 108199. [ Google Scholar ] [ CrossRef ]
• Wang, J.; Han, C.; Jo, S.-H.; Xu, W.; Tian, H. Enhanced flow induced vibration piezoelectric energy harvesting performance by optimizing tapered beam. Ocean Eng. 2024 , 300 , 117459. [ Google Scholar ] [ CrossRef ]
• Man, D.; Jiang, B.; Xu, Q.; Tang, L.; Zhang, Y.; Xu, G.; Han, T. Enhancing low-orbit vibration energy harvesting by a tri-stable piezoelectric energy harvester with an innovative dynamic amplifier. AIP Adv. 2024 , 14 , 045210. [ Google Scholar ] [ CrossRef ]
• Cho, K.-H.; Park, H.-Y.; Heo, J.S.; Priya, S. Structure—Performance relationships for cantilever-type piezoelectric energy harvesters. J. Appl. Phys. 2014 , 115 , 204108. [ Google Scholar ] [ CrossRef ]
• Lee, B.S.; Lin, S.C.; Wu, W.J.; Wang, X.Y.; Chang, P.Z.; Lee, C.K. Piezoelectric MEMS generators fabricated with an aerosol deposition PZT thin film. J. Micromech. Microeng. 2009 , 19 , 065014. [ Google Scholar ] [ CrossRef ]
• Nair, R.; Tripathi, B.; Jain, A.; Shehata, N. Vibrational energy harvesting and tactile sensing applications based on PVDF-TPU piezoelectric nanofibers. J. Mater. Sci. Mater. Electron. 2024 , 35 , 857. [ Google Scholar ] [ CrossRef ]
• Ramírez, J.M.; Gatti, C.D.; Machado, S.P.; Febbo, M. A multi-modal energy harvesting device for low-frequency vibrations. Extrem. Mech. Lett. 2018 , 22 , 1–7. [ Google Scholar ] [ CrossRef ]
• Su, Y. The Study of Design Preparation and Applications of Triboelectric Nanogenerators. Ph.D. Thesis, University of Electronic Science and Technology, Chengdu, China, 2016. [ Google Scholar ]
• Du, X.; Zhang, K. Recent progress in fibrous high-entropy energy harvesting devices for wearable applications. Nano Energy 2022 , 101 , 107600. [ Google Scholar ] [ CrossRef ]
• Haroun, A.; Tarek, M.; Mosleh, M.; Ismail, F. Recent Progress on Triboelectric Nanogenerators for Vibration Energy Harvesting and Vibration Sensing. Nanomaterials 2022 , 12 , 2960. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, Z.L.; Wang, A.C. On the origin of contact-electrification. Mater. Today 2019 , 30 , 34–51. [ Google Scholar ] [ CrossRef ]
• Yang, W.; Chen, J.; Jing, Q.; Yang, J.; Wen, X.; Su, Y.; Zhu, G.; Bai, P.; Wang, Z.L. 3D Stack Integrated Triboelectric Nanogenerator for Harvesting Vibration Energy. Adv. Funct. Mater. 2014 , 24 , 4090–4096. [ Google Scholar ] [ CrossRef ]
• Qiu, W.; Feng, Y.; Luo, N.; Chen, S.; Wang, D. Sandwich-like sound-driven triboelectric nanogenerator for energy harvesting and electrochromic based on Cu foam. Nano Energy 2020 , 70 , 104543. [ Google Scholar ] [ CrossRef ]
• Shi, Q.; Yang, J.; Gui, H.; Gui, Y.; Tang, C.; Yu, Y.; Zhao, Z. Self-powered circular-honeycomb triboelectric nanogenerator for vibration energy harvesting and resonance detection of synchronous machine. Sens. Actuators Phys. 2023 , 354 , 114291. [ Google Scholar ] [ CrossRef ]
• Liu, Z.; Zhao, C.; Hu, G.; Yang, Y. A multi-degree-of-freedom triboelectric energy harvester for dual-frequency vibration energy harvesting. Mech. Syst. Signal Process. 2023 , 188 , 109951. [ Google Scholar ] [ CrossRef ]
• Yang, X.; Zheng, H.; Ren, H.; An, G.; Zhang, Y.; Yang, W. A tuned triboelectric nanogenerator using a magnetic liquid for low-frequency vibration energy harvesting. Nanoscale 2024 , 16 , 1915–1923. [ Google Scholar ] [ CrossRef ]
• Gao, S.; Wei, H.; Wang, J.; Luo, X.; Wang, R.; Chen, Y.; Xiang, M.; Chen, X.; Xie, H.; Feng, S. Self-powered system by a suspension structure-based triboelectric-electromagnetic-piezoelectric hybrid generator for unifying wind energy and vibration harvesting with vibration attenuation function. Nano Energy 2024 , 122 , 109323. [ Google Scholar ] [ CrossRef ]
• Zhao, H.; Ouyang, H. Theoretical investigation and experiment of a disc-shaped triboelectric energy harvester with a magnetic bistable mechanism. Smart Mater. Struct. 2021 , 30 , 095026. [ Google Scholar ] [ CrossRef ]
• Naruse, Y.; Matsubara, N.; Mabuchi, K.; Izumi, M.; Suzuki, S. Electrostatic micro power generation from low-frequency vibration such as human motion. J. Micromech. Microeng. 2009 , 19 , 094002. [ Google Scholar ] [ CrossRef ]
• Bu, L.; Wu, X.; Wang, X.; Liu, L. A Novel Vibration Energy Harvester of Polytetrafluoroethylene Bulk Electrets and Double Side Charging Structure. Nanotechnol. Precis. Eng. 2013 , 11 , 497–502. [ Google Scholar ]
• Kloub, H.; Hoffmann, D.; Folkmer, B.; Manoli, Y. A micro capacitive vibration energy harvester for low power electronics. Work 2009 , 11 , 1. [ Google Scholar ]
• Tao, K.; Wu, J.; Tang, L.; Hu, L.; Lye, S.W.; Miao, J. Enhanced electrostatic vibrational energy harvesting using integrated opposite-charged electrets. J. Micromech. Microeng. 2017 , 27 , 044002. [ Google Scholar ] [ CrossRef ]
• Aliasghary, M.; Azizi, S.; Madinei, H.; Haddad Khodaparast, H. On the Efficiency Enhancement of an Actively Tunable MEMS Energy Harvesting Device. Vibration 2022 , 5 , 603–612. [ Google Scholar ] [ CrossRef ]
• Erturun, U.; Eisape, A.; West, J.E. Design and analysis of a vibration energy harvester using push-pull electrostatic conversion. Smart Mater. Struct. 2020 , 29 , 105018. [ Google Scholar ] [ CrossRef ]
• Yamane, D.; Tamura, K.; Nota, K.; Iwakawa, R.; Lo, C.Y.; Miwa, K.; Ono, S. Contactless Electrostatic Vibration Energy Harvesting Using Electric Double Layer Electrets. Sens. Mater. 2022 , 34 , 1869–1877. [ Google Scholar ] [ CrossRef ]
• Deng, Z.; Dapino, M.J. Review of magnetostrictive vibration energy harvesters. Smart Mater. Struct. 2017 , 26 , 103001. [ Google Scholar ] [ CrossRef ]
• Dai, X.-Z.; Wen, Y.-M.; Li, P.; Yang, J.; Jiang, X.-F. Vibration energy harvester based on magnetoelectric transducer. Acta Phys. Sin. 2010 , 59 , 2137–2146. [ Google Scholar ] [ CrossRef ]
• Cao, S.; Sun, S.; Zheng, J.; Wang, X.; Han, J.; Liu, L. Research Progress of Magnetostrictive Vibration Energy Harvesters. Micro Nano Electron. 2017 , 54 , 612–620. [ Google Scholar ] [ CrossRef ]
• Kita, S.; Ueno, T.; Yamada, S. Improvement of force factor of magnetostrictive vibration power generator for high efficiency. J. Appl. Phys. 2015 , 117 , 17B508. [ Google Scholar ] [ CrossRef ]
• Yan, B.; Zhang, C.; Li, L. Design and Fabrication of a High-Efficiency Magnetostrictive Energy Harvester for High-Impact Vibration Systems. IEEE Trans. Magn. 2015 , 51 , 1–4. [ Google Scholar ] [ CrossRef ]
• Dong, W.; Liang, Q.; Liu, H.; Mei, X.; Shu, L.; Liu, Z.; Chang, Y. Characterization of magnetostrictive bi-stable rotational vibration energy harvester with integrated centrifugal effect. Smart Mater. Struct. 2024 , 33 , 025034. [ Google Scholar ] [ CrossRef ]
• Liu, H.; Tong, X.; Sun, X.; Wang, W.; Su, L.; Chang, Y.; Liu, Z. Design and analysis of magnetostrictive two-dimensional kinetic energy harvester. Smart Mater. Struct. 2024 , 33 , 025032. [ Google Scholar ] [ CrossRef ]
• Liu, Z.; Huang, W.; Guo, P.; Yang, H.; Weng, L.; Wang, B. Nonlinear Model for Magnetostrictive Vibration Energy Harvester Considering Dynamic Forces. IEEE Sens. J. 2024 , 24 , 16270–16278. [ Google Scholar ] [ CrossRef ]
• Liu, H.; Liu, H.; Zhao, X.; Li, A.; Yu, X. Design and Characteristic Analysis of Magnetostrictive Vibration Harvester with Double-Stage Rhombus Amplification Mechanism. Machines 2022 , 10 , 848. [ Google Scholar ] [ CrossRef ]
• Ueno, T. Performance of improved magnetostrictive vibrational power generator, simple and high power output for practical applications. J. Appl. Phys. 2015 , 117 , 17A740. [ Google Scholar ] [ CrossRef ]
• Clemente, C.S.; Davino, D.; Visone, C. Experimental Characterization of a Three-Rod Magnetostrictive Device for Energy Harvesting. IEEE Trans. Magn. 2017 , 53 , 1–4. [ Google Scholar ] [ CrossRef ]

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Qu, G.; Xia, H.; Liang, Q.; Liu, Y.; Ming, S.; Zhao, J.; Xia, Y.; Wu, J. Current Research Status and Future Trends of Vibration Energy Harvesters. Micromachines 2024 , 15 , 1109. https://doi.org/10.3390/mi15091109

Qu G, Xia H, Liang Q, Liu Y, Ming S, Zhao J, Xia Y, Wu J. Current Research Status and Future Trends of Vibration Energy Harvesters. Micromachines . 2024; 15(9):1109. https://doi.org/10.3390/mi15091109

Qu, Guohao, Hui Xia, Quanwei Liang, Yunping Liu, Shilin Ming, Junke Zhao, Yushu Xia, and Jianbo Wu. 2024. "Current Research Status and Future Trends of Vibration Energy Harvesters" Micromachines 15, no. 9: 1109. https://doi.org/10.3390/mi15091109

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The University of Chicago The Law School

Global human rights clinic—significant achievements for 2023-24.

The Global Human Rights Clinic (GHRC) students continue to advance justice and address the inequalities and structural disparities that lead to human rights violations worldwide using diverse tactics and interdisciplinary tools. Over the past year, students and clinic director Anjli Parrin—who joined the faculty permanently in October 2023—worked in teams to promote human rights around the world. In particular, the GHRC supported justice efforts in the context of conflict and related to mass atrocities; the investigation and prevention of unlawful killings globally; the rights of missing migrants; the right to health; climate justice; and the right to equality and non-discrimination. Select work from each of these strands is described below.

Justice in Conflict: Supporting Atrocity Investigations in The Gambia and Central African Republic

The GHRC partners with civil society organizations and multidisciplinary scientific experts to investigate war crimes and mass atrocities, and advance justice in the context of conflict. Over this past year, the GHRC supported effective investigations in the Central African Republic and the Gambia. In addition, the Clinic worked with grassroots civil society and victims’ associations in both countries to advance critical human rights.

Central African Republic

In the Central African Republic (CAR), protracted violence and conflict has had devastating impacts on the civilian population. Civilians have borne the brunt of grave human rights violations, and the country remains one of the poorest in the world. The GHRC supported judicial authorities to carry out complex investigations of alleged mass atrocities committed during armed conflict in the country. Students worked alongside lawyers and scientific experts to conduct detailed factfinding, prepare legal memos on evidence collection and preservation, and support the creation of investigation files of human rights abuses.

Further, the GHRC alongside the Columbia Law School Smith Family Human Rights Clinic, partnered with CAR civil society, which is significantly under-funded and under-resourced, and therefore frequently shut out of international human rights forums and subject to attacks and threats domestically. We worked with two organizations—the Collectif des Organisations Musulmanes de Centrafrique (COMUC), an umbrella network of Muslim civil society, and the Association des Femmes Juriste de Centrafrique (AFJC), a women’s lawyers’ organization, and one of the largest providers of legal aid in the country—to document and advocate for the rights of religious minorities and women at the United Nations Human Rights Council. Students supported these organizations to:

• Launch a major human rights report on the right to freedom of religion and belief, and non-discrimination of religious minorities in CAR. This report documents violations of the right to life, arbitrary detention, freedom of movement, legal recognition, health, and education, and was launched in Geneva in December 2023.
• Carry out advocacy before the United Nations Human Rights Council in Geneva, as part of CAR’s Universal Periodic Review, a unique process of the Council whereby States’ human rights records are reviewed every five years. Students supported advocates from COMUC and AFJC to prepare reports on the human rights situation, present at a pre-session for the review in Geneva, and to meet diplomatic missions to inform them about the human rights situation in the country. The clinic’s support to national civil society ensured that they had access to this important international advocacy forum. The civil society reports can be accessed at the UN Office of the High Commission for Human Rights website (for a summary, see, A/HRC/WG.6/45/CAF/3 ).

In the Gambia, a military regime run by autocrat Yahya Jammeh committed scores of human rights abuses between 1994 and 2016, including arbitrary detentions, extrajudicial killings, and enforced disappearances. Following the overturning of the Jammeh regime, a truth commission was created to understand what happened during the dictatorship, and a special prosecution office is being set up. Families of those killed and disappeared are searching for answers as to the fate of their loved ones.

In partnership with the African Network Against Extrajudicial Killings and Enforced Disappearances (ANEKED) Gambia chapter, the Gambian Ministry of Justice, and the Argentine Forensic Anthropology Team, GHRC students supported efforts to advance justice and the search for missing persons in the Gambia. In particular, building on an assessment of the forensic and international criminal system conducted last year, the GHRC worked with civil society to carry out factfinding related to a key mass atrocity case. Additionally, in the Fall, the GHRC will work with ANEKED to expand its transitional justice and memory curriculum, so that young persons in the Gambia and globally learn about the process for truth and justice in the country.

Extrajudicial Executions: Preventing and Investigating Unlawful Deaths Globally

The GHRC provided strategic support to Morris Tidball-Binz, the United Nations Special Rapporteur on Extrajudicial, Summary, or Arbitrary Executions, and a leading independent human rights expert appointed by the United Nations to advise on the issue of unlawful killings from a thematic perspective. The Special Rapporteur procedures are a key pillar through which human rights is advanced at the UN. As part of their mandate, Special Rapporteurs undertake country visits, conduct annual thematic studies, and act on individual cases of reported violations by sending communications to States and international authorities. As of June 2024, Tidball-Binz joined the University of Chicago Pozen Family Center for Human Rights as a visiting senior research associate, where he will engage with and conduct joint research alongside Pozen Center and GHRC students.

In particular, the GHRC supported the Special Rapporteur with:

• Preparation for his country visit to Ukraine in May 2024. GHRC students conducted detailed research, factfinding, and analysis of concerns relating to unlawful killings in Ukraine, producing background research about the human rights situation prior to as well as during the ongoing escalation in hostilities. The research covered legislative and policy structures, key crosscutting concerns, emblematic cases, and positive developments. During the Special Rapporteur’s actual time in-country, GHRC students provided remote, ongoing support as required.
• Support in the research and drafting of his thematic report on the protection of the dead from a human rights perspective. GHRC students conducted factfinding, expert interviews, and legal analysis to inform the Special Rapporteur’s thematic report on protection of the dead, which was presented to the UN Human Rights Council on June 26, 2024 ( A/HRC/56/56 ). The UN Special Rapporteur acknowledged the contributions of the GHRC (video, remarks referencing the GHRC at 31:30).

Missing Migrants: A Forensic Response for African Missing Migrants in Southwest Europe

Thousands of Africans go missing each year attempting to cross international borders in search of safety and better opportunities. Despite the broad recognition among states of the importance and need to address the situation of missing migrants, there is a lack of formal coordination and procedures among all relevant stakeholders relating to missing migrants, and in many instances, even within a country’s government, there is a lack of information sharing. For families searching for the fate and whereabouts of their loved ones, the uncertainty is devastating, often leaving them in limbo.

In partnership with the Immigrants’ Rights Clinic (IRC) and the Argentine Forensic Anthropology Team, the GHRC is supporting efforts to identify missing migrants traveling from Africa to South-West Europe. Over this course of this academic year, GHRC/IRC students:

• Researched migration patterns in key departure and transit countries in Africa, focusing on migrants leaving from the Gambia, Senegal, Morocco, and Tunisia. Additionally, students researched migration arrival patterns in Spain.
• Commenced an analysis of the existing legal frameworks governing the rights of missing migrants, and laws that pertain to transnational exchange of information of missing migrants. This analysis will be further developed and published next academic year.
• Prepared to carry out travel to the Gambia, Senegal, Tunisia, and Morocco, including identifying key stakeholders in each country from civil society, state institutions, and intergovernmental institutions.

Advancing the Right to Health Globally

GHRC students work to address violations of the right to health globally. We do so in two key areas—by working with Indigenous groups globally to reinterpret the international human right to health in accordance with Indigenous knowledge systems; and to support the realization of the right to health in the context of armed conflict.

Indigenous rights to health

In partnership with Human Rights Watch and Indigenous groups in South Africa, the Navajo Nation, and Guåhan (Guam), GHRC students are working to tackle systemic harms within global health and understand the impact of colonial determinants on health outcomes. This academic year, students worked to finalize a human rights report on the impact of US military buildup in Guåhan on Indigenous CHamoru medicinal and healing practices (the military currently controls approximately one-third of land on Guåhan). This report will be released in the Fall of 2024. Further, GHRC students supported Indigenous groups in South Africa and the Navajo Nation to document violations of the right to health in their lands.

Drawing upon his research through the GHRC, undergraduate student Elijah Jenkins was selected to receive the prestigious Stamps Scholarship , which will support him to undertake additional research in Guåhan. As a CHamoru student, Jenkins will deepen his understanding of and research into the impact of colonialism on the peoples of Guåhan and will continue to be supported by the GHRC.

Attacks on healthcare in conflict

The GHRC partnered with the University of Chicago’s Pritzker School of Medicine to document, research, and support legal claims of violations of the right to health in the context of the ongoing conflict in Israel and Palestine. This project is taking place with the support and partnership of the Heath and Vascular Hospital at the Public Aid Society in Gaza. GHRC law students and Pritzker School medical students teamed up to conduct interviews with doctors who have recently traveled to Gaza, conduct open-source research into violations of the right to health, and analyze the applicable international humanitarian law governing protection of medical establishments and personnel. The team is currently preparing joint submissions to legal and quasi-judicial bodies.

Bridging the Chasm Between Law, Science, Technology and Narrative to Advance Climate Justice

While climate change is having a devastating impact across the planet, the harms are not experienced equally. Those on the frontlines of the climate crisis are frequently those who have contributed least to climate harms—including Indigenous groups, individuals living in small island nations, young people, and communities across the Global South. Coalitions of young people, including the Pacific Island Students Fighting Climate Change (PISFCC) and the World’s Youth for Climate Justice (WY4CJ), are leading the right to ensure a livable present and future.

In March 2023, the PISFCC succeeded in getting a historic resolution adopted, asking the International Court of Justice—the World’s Court—to rule on what the obligations of States are to protect the climate, and what the consequences are for the world’s biggest violators. Ahead of the ICJ oral hearings, GHRC is partnering with PISFCC, WY4CJ, visual investigations experts SITU Research , and artist Suneil Sanzgiri, to create a fifteen-minute film that weaves together the stories of young people and the impacts of climate harm through testimony, historical and contemporary documentation, and climate science. The film will debut at the Pinakothek der Moderne museum as part of the upcoming exhibition, Visual Investigations: between Advocacy, Journalism, and Law , opening October 10, 2024 in Munich, Germany.

Advancing Equality: Resisting Discriminatory Laws in Uganda and Globally

Discriminatory laws impact the ability of sexual and gender minorities, as well as other vulnerable groups, to access basic rights. Recently, several countries have passed discriminatory laws, including ones criminalizing homosexuality with extraordinarily punitive sentences. GHRC students work alongside civil society organizations in Uganda and around the world to challenge unfair laws and policies. This academic year, students:

• Partnered with Chapter Four Uganda and the Makerere University Human Rights and Peace Centre to develop a strategy to challenge discriminatory provisions in the survivor’s benefit clause of the National Social Security Fund Act. In March 2024, GHRC students traveled to Uganda to host the first of its kind moot court competition around this provision. Students partnered with Ugandan colleagues to prepare their arguments, and following the event met with the Minister of Justice to advocate for changes in the law. Currently, students are preparing a joint white paper on the issue, which will be published over the summer of 2024.
• In partnership with Stanford Law School International Human Rights and Conflict Resolution Clinic, GHRC students supported major NGOs in countries where new restrictions on sexual orientation and gender identity had been passed to analyze the restrictions and publish public-facing advocacy documents explaining their implications.
• Supported the UN Special Rapporteur on Extrajudicial, Summary or Arbitrary Executions with research and legal analysis of LGBTQI+ killings, ahead of a thematic report which he will present to the UN General Assembly in October 2024.

Student Post-Graduate Fellowships

Additionally, GHRC graduating students obtained prestigious fellowships to pursue public interest work post-graduation. In 2023, Nico Thompson Lleras and Marin Murdock both received fellowships to work at Reprieve’s Unlawful Detention program and International Coalition of Sites of Conscience’s Global Initiative for Justice, Truth, and Reconciliation. In 2024, graduating student Bryant King will join the Clooney Foundation for Justice as a legal fellow, and Elisa Epstein received the Equal Justice Works Fellowship to support a two-year fellowship at the American Civil Liberties Union (ACLU).

    Tissue engineering is a biomedical engineering discipline that uses combinations of engineering, cells, materials and biochemical cues to restore, maintain, improve or replace different types of biological tissues. As the understanding of how these factors interplay deepened, the research field of tissue engineering rapidly expanded. So, in order to disseminate the new discoveries in the field, a number of new journals were established. Today, we are starting to see the development of tissue engineered products becoming available and used clinically, and this includes products for hard and soft tissue reconstruction and augmentation for improved clinical outcomes.

    The Journal of Tissue Engineering (JTE) was set up in 2010, when Sage Publishing realized it was a rapidly expanding research field. I was asked to set up the journal as I had had some success as editor-in-chief of the Journal of Biomaterials Applications with Sage, and this seemed to be an opportune moment to move into the open access publishing arena. 

    The JTE aims to publish papers specifically in tissue engineering, as opposed to more cell-focused journals which publish predominantly regenerative medicine or cell biology. We publish papers with an applied focus that are closer to the clinical problem — either in terms of application to the patient or with models that more closely recapitulate the actual cellular environment in the body. Two excellent examples are the development of organ on a chip models and the development of processing methods for extracellular vesicles for therapeutic use.

    The JTE prides itself on having highly active and responsive editors to help the authors through the publication journey. We also have a rigorous level of peer review to ensure only papers of the highest quality are published. 

    Early on, Professor Hae-Won Kim at Dankook University in South Korea, joined as co-editor-in-chief. For many years we have also received strong support from Matt Dalby at Glasgow University and Wojciech Chrzanowski at Sydney University. All of them and many others have helped contribute to shaping the journal to make it the success that it is today.

    Of particular note is the transition towards more freely available open access publishing. This has transformed the academic landscape, making work much more freely available, and this is clearly reflected in JTE's really high levels of downloads and citations.

    We have tried to steer the journal along a path of academic excellence, endeavoring to publish only the leading papers, rather than publishing incremental papers. We have tried to determine the papers that really represent a ground-breaking change in the field. This can sometimes be difficult, especially with the advent of very advanced large scale and/or high throughput measurement methodologies such as genomics and proteomics, so prevalent today in biology.

    However, we have endeavored to analyze each paper to ensure it remains true to the journal's subject area. We have also tried to identify areas that are in the early stages of development and expansion, and have published Special Collections to support these areas. Such topics include extracellular vesicles and additive manufacturing. These Special Collections have proven to be highly successful, with large numbers of downloads and subsequent cites. They are specifically aimed at new and emerging topics to attract submissions from world-leading authors. 

    A significant number of submissions to the journal come from China. It is excellent to see the rapid development in the science being carried out in China, driven by a large-scale investment from the government and the private sector.

    One aspect of these submissions that we have seen occurring is the use of these high throughput methods to provide large datasets and subsequent deep analysis of this data. This type of analysis is relatively unique due to the high cost, but gives important insights into the control and regulation of tissue and how these might be utilized in tissue engineering approaches. One factor that seems to be particularly strong in papers from China is the clear integration of clinical and non-clinical colleagues, and this has contributed hugely to the basic understanding of a disease state and the subsequent development of a coherent tissue engineering strategy to repair the defective tissues.

    The author is editor-in-chief of the Journal of Tissue Engineering, and professor of Biomaterials Science at the Eastman Dental Institute, University College London, UK.

    Journal Review

    Since the concept of tissue engineering was established by Robert  Langer and Joseph P. Vacanti in 1987, it has rapidly developed as an emerging technology. In 2000, it was listed as the top 10 popular professions for the 21st century by the Time magazine. People hope to make it possible in the near future to replace damaged human organs as easily as replacing mechanical parts.

    The core of tissue engineering research is to establish three-dimensional composites made of cells and biomaterials, essentially constructing living tissues with vitality to replace damaged tissues and organs. This aims to achieve permanent replacement through the reconstruction of morphology, structure and function. With the development of life sciences, biomaterials, and engineering technology, tissue engineering is about to become, or is already becoming, an effective treatment method for tissue and organ failure, marking the entry of medicine into a new era of manufacturing tissues and organs.

    Compared to research focused on the cellular level, the Journal of Tissue Engineering prioritizes clinically oriented and application-focused research. It also keeps an eye on emerging topics, encouraging researchers in basic research to develop new technologies based on clinical needs, thereby promoting the advancement of the frontier fields of tissue engineering.

    —— Gu Ning, member of the Chinese Academy of Sciences and professor of the Nanjing University & Li Yan, associate professor of the Southeast University.

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